phishing_detection_models.ipynb

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   "source": [
    "# **Phishing Website Detection by Machine Learning Techniques**\n",
    "\n",
    "*Final project of AI & Cybersecurity Course*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_cJA-yD-vNcS"
   },
   "source": [
    "## **1. Objective:**\n",
    "A phishing website is a common social engineering method that mimics trustful uniform resource locators (URLs) and webpages. The objective of this project is to train machine learning models and deep neural nets on the dataset created to predict phishing websites. Both phishing and benign URLs of websites are gathered to form a dataset and from them required URL and website content-based features are extracted. The performance level of each model is measures and compared.\n",
    "\n",
    "*This project is worked on Google Collaboratory.*<br>\n",
    "*The required packages for this notebook are imported when needed.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NOq598WCqIol"
   },
   "source": [
    "## **2. Loading Data:**\n",
    "\n",
    "The features are extracted and store in the csv file. The working of this can be seen in the 'Phishing Website Detection_Feature Extraction.ipynb' file.\n",
    "\n",
    "The reulted csv file is uploaded to this notebook and stored in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "C297HhYulXcb",
    "outputId": "d6e2a9df-586e-4192-b8ec-1e7b7025c0c3"
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   "outputs": [],
   "source": [
    "#importing basic packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "colab_type": "code",
    "id": "fVPglpaf4REa",
    "outputId": "eef4a4ca-e12d-4cd3-e011-20376fc752a2"
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   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Domain</th>\n",
       "      <th>Have_IP</th>\n",
       "      <th>Have_At</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>URL_Depth</th>\n",
       "      <th>Redirection</th>\n",
       "      <th>https_Domain</th>\n",
       "      <th>TinyURL</th>\n",
       "      <th>Prefix/Suffix</th>\n",
       "      <th>dns</th>\n",
       "      <th>Web_Traffic</th>\n",
       "      <th>Domain_Age</th>\n",
       "      <th>Domain_End</th>\n",
       "      <th>iFrame</th>\n",
       "      <th>Mouse_Over</th>\n",
       "      <th>Right_Click</th>\n",
       "      <th>Web_Forwards</th>\n",
       "      <th>Fuzzy_Hashing</th>\n",
       "      <th>Perceptual_Hashing</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>edisonlearning.com</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>insyncvideo.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doctorww.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>asx-exs-trade.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hexe.in</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "               Domain  Have_IP  Have_At  URL_Length  URL_Depth  Redirection  \\\n",
       "0  edisonlearning.com        0        0           0          0            0   \n",
       "1     insyncvideo.com        0        0           0          0            0   \n",
       "2        doctorww.com        0        0           0          0            0   \n",
       "3   asx-exs-trade.com        0        0           0          2            0   \n",
       "4             hexe.in        0        0           0          0            0   \n",
       "\n",
       "   https_Domain  TinyURL  Prefix/Suffix  dns  Web_Traffic  Domain_Age  \\\n",
       "0             0        0              0    0            1           1   \n",
       "1             0        0              0    0            1           1   \n",
       "2             0        0              0    0            1           1   \n",
       "3             0        0              1    0            1           1   \n",
       "4             0        0              0    0            1           1   \n",
       "\n",
       "   Domain_End  iFrame  Mouse_Over  Right_Click  Web_Forwards  Fuzzy_Hashing  \\\n",
       "0           1       1           0            0             0              1   \n",
       "1           1       0           0            0             0              1   \n",
       "2           1       0           0            0             0              1   \n",
       "3           1       0           0            0             0              1   \n",
       "4           1       0           0            0             0              1   \n",
       "\n",
       "   Perceptual_Hashing  Label  \n",
       "0                   1      1  \n",
       "1                   0      1  \n",
       "2                   0      1  \n",
       "3                   0      1  \n",
       "4                   1      1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data\n",
    "data0 = pd.read_csv(r\"C:\\Users\\R\\Downloads\\phishing\\DataFilesfile.csv\")\n",
    "data0.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lMXihulvMcAj"
   },
   "source": [
    "## **3. Familiarizing with Data**\n",
    "In this step, few dataframe methods are used to look into the data and its features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "4hARIXyGKorc",
    "outputId": "bc223e7f-4529-4ebe-e7d0-7eef13e691c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 20)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the shape of the dataset\n",
    "data0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100
    },
    "colab_type": "code",
    "id": "Q3uBm9ObMyaG",
    "outputId": "551b4c2e-8b33-4a6a-c3e9-f168066915de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Domain', 'Have_IP', 'Have_At', 'URL_Length', 'URL_Depth',\n",
       "       'Redirection', 'https_Domain', 'TinyURL', 'Prefix/Suffix', 'dns',\n",
       "       'Web_Traffic', 'Domain_Age', 'Domain_End', 'iFrame', 'Mouse_Over',\n",
       "       'Right_Click', 'Web_Forwards', 'Fuzzy_Hashing', 'Perceptual_Hashing',\n",
       "       'Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing the features of the dataset\n",
    "data0.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "colab_type": "code",
    "id": "gBMqupCMM74V",
    "outputId": "0e530eb9-699d-4ece-800d-8e853fc9d6ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 20 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   Domain              200 non-null    object\n",
      " 1   Have_IP             200 non-null    int64 \n",
      " 2   Have_At             200 non-null    int64 \n",
      " 3   URL_Length          200 non-null    int64 \n",
      " 4   URL_Depth           200 non-null    int64 \n",
      " 5   Redirection         200 non-null    int64 \n",
      " 6   https_Domain        200 non-null    int64 \n",
      " 7   TinyURL             200 non-null    int64 \n",
      " 8   Prefix/Suffix       200 non-null    int64 \n",
      " 9   dns                 200 non-null    int64 \n",
      " 10  Web_Traffic         200 non-null    int64 \n",
      " 11  Domain_Age          200 non-null    int64 \n",
      " 12  Domain_End          200 non-null    int64 \n",
      " 13  iFrame              200 non-null    int64 \n",
      " 14  Mouse_Over          200 non-null    int64 \n",
      " 15  Right_Click         200 non-null    int64 \n",
      " 16  Web_Forwards        200 non-null    int64 \n",
      " 17  Fuzzy_Hashing       200 non-null    int64 \n",
      " 18  Perceptual_Hashing  200 non-null    int64 \n",
      " 19  Label               200 non-null    int64 \n",
      "dtypes: int64(19), object(1)\n",
      "memory usage: 31.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#Information about the dataset\n",
    "data0.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CfrxhE3pNfiw"
   },
   "source": [
    "## **4. Visualizing the data**\n",
    "Few plots and graphs are displayed to find how the data is distributed and the how features are related to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 879
    },
    "colab_type": "code",
    "id": "N9K0yAdAM70w",
    "outputId": "05687b93-945e-4fee-c3da-baae065ad528"
   },
   "outputs": [
    {
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Nm6zL3WV/lRfyrGjukGcSmUameRYyrWjukGnkGXlWFJdujqWnp9ucTidJPj4+Cg8Pt/sM6sVuv/12vfPOO1q7dq0mTJigt99+W3/7299stntxY0yS9XJx23WW0tZ1sf/973+aOnWq3UcxH3/8cb3//vtKSUlR//79dd9992nWrFlOm/vFt5+Xl1fo/VhUDUXd7wXjC/7ryDadrTR1Xeof//iHoqOjbZ7ASUlJeuutt7R69WpNnz5dX375pZKTk5WXl+fU+RelNHU1adJEb775pj7++GO98847ys/PV6dOnXTw4EFJVWN/VWXuep+X9Tny9ddfa+fOnbrrrrtsllf2c8RRReVZRkaGzp4965QsqQpmzpyp06dPa8CAAdZlHTt2tH60/5VXXlFaWpo6d+6szMzMSpxp8WrXrq05c+boww8/1Icffqg6deqoW7du+vbbbyU5J/vdGXlWOHfJM4lMI9M8C5lWOHfJNPKMPCuKz+WHVLxHHnlE06dPL3bM7t27S739ixtGrVq1Uu3atdWjRw/t27dPDRs2LPV2L6e86yqQkZGhPn36qHnz5po8ebLNuscee8z673bt2ikrK0tPP/20HnjggTLfLi7vqaee0qJFi7Ru3TqbL+kcOHCg9d+tWrVS69at1bBhQ61bt049evSojKleVlxcnOLi4qyXO3XqpGbNmunVV1/V1KlTK3Fm7ssT7vM33nhDrVq1UocOHWyWu+JzxN0tXLhQU6ZM0ccff2zzhk5ycrL1361bt1bHjh1Vr149vf/++4V+D2ZV0KRJEzVp0sR6uVOnTtq3b5+ee+45vf3225U4M/dFnl1AnlUdZBrKgky7gEyrGsiz0qmSZ4499NBD2r17d7F/DRo0UFRUlN2XrOXm5urEiROKiooq8e117NhRkrR3715JUlRUlI4ePWozpuCyI9utjLoyMzOVlJSk4OBgffTRR/L19S12fMeOHXXw4EFlZ2eXuq7C1KxZU97e3oXej0XVUNT9XjC+4L+ObNPZSlNXgZkzZ+qpp57SqlWr1Lp162LHNmjQQDVr1rQ+JstbWeoq4Ovrq3bt2tk8jwq2UdptujN3vc/LUldWVpYWLVpUov85V/RzxFFF5VlISIgCAwOdsv8r06JFi3TXXXfp/ffft/towqXCwsJ09dVXV9l9VZQOHTpY5+zq+6u8kWf23CnPJDLtYmSa+yPT7LlTppFnfyLPbFXJ5litWrXUtGnTYv/8/PwUFxenkydPatu2bdbrrlmzRvn5+daGV0ls375d0oVT9qQLnf8dO3bYNKhSUlIUEhKi5s2bV9m6MjIy1KtXL/n5+WnZsmV2Px9cmO3bt6tGjRry9/cvdV2F8fPzU2xsrFavXm1dlp+fr9WrV9u8q3KxuLg4m/HShfu9YHxMTIyioqJsxmRkZGjLli1FbtPZSlOXdOHXaaZOnaoVK1bYfJdcUQ4ePKjjx49bH5PlrbR1XSwvL087duywzrkq7K+qzF3v87LUtXjxYmVnZ9t8zL0oFf0ccdTl8swZ+7+yvPvuuxo2bJjeffddm59yL8rp06e1b9++KruvirJ9+3brnF15f1UE8syeO+WZRKZdjExzf2SaPXfKNPLsT+TZJRz6+v4qKCkpybRr185s2bLFbNy40TRu3NgMGjTIuv7gwYOmSZMmZsuWLcYYY/bu3Wsef/xxs3XrVpOWlmY+/vhj06BBA9OlSxfrdXJzc03Lli1Nr169zPbt282KFStMrVq1zIQJE6psXadOnTIdO3Y0rVq1Mnv37jVHjhyx/uXm5hpjjFm2bJl5/fXXzY4dO8yePXvMyy+/bKpVq2YmTpxYLjUsWrTI+Pv7m3nz5pkff/zRjBw50oSFhVl/BfSOO+4wjzzyiHX8pk2bjI+Pj5k5c6bZvXu3mTRpkvH19TU7duywjnnqqadMWFiY+fjjj80PP/xgbrrpJhMTE2POnj1bLjU4o66nnnrK+Pn5mQ8++MBmv2RmZhpjjMnMzDTjxo0zqampJi0tzXzxxRfmmmuuMY0bNzbnzp2rsnVNmTLFrFy50uzbt89s27bNDBw40AQEBJhdu3bZ1F7Z+6sqc9f73NG6CsTHx5vbbrvNbnlVeI5kZmaa7777znz33XdGknn22WfNd999Z3799VdjjDGPPPKIueOOO6zjf/nlF1OtWjUzfvx4s3v3bjN79mzj7e1tVqxYYR1zufupKta1YMEC4+PjY2bPnm2TZydPnrSOeeihh8y6detMWlqa2bRpk0lISDA1a9Y0x44dq7J1Pffcc2bp0qVmz549ZseOHebvf/+78fLyMl988YV1TFXYX1UZeWarKudZwTzINDKtsvdXVUam2arKmUaeXUCelX1/uXxz7Pjx42bQoEGmevXqJiQkxAwbNszadDDGmLS0NCPJrF271hhjzIEDB0yXLl1MeHi48ff3N40aNTLjx483p06dstnu/v37TXJysgkMDDQ1a9Y0Dz30kMnJyamydRX8xGlhf2lpacYYYz7//HPTtm1bU716dRMUFGTatGlj5syZY/Ly8sqtjlmzZpm6desaPz8/06FDB7N582bruq5du5ohQ4bYjH///ffN1Vdfbfz8/EyLFi3MZ599ZrM+Pz/fPPbYYyYyMtL4+/ubHj16mJ9//rnc5l8UR+qqV69eoftl0qRJxhhjzpw5Y3r16mVq1aplfH19Tb169cyIESMq5eDEkbrGjBljHRsZGWl69+5tvv32W5vtVZX9VZW5633u6HP/p59+MpLMqlWr7LZVFZ4jRWVsQR1DhgwxXbt2tbtO27ZtjZ+fn2nQoIGZO3eu3XaLu58qgqN1de3atdjxxlz4OfTatWsbPz8/c+WVV5rbbrvN7N27t0rXNX36dNOwYUMTEBBgwsPDTbdu3cyaNWvstlvZ+6uqI88uqOp5ZgyZVoBMI9OKQ6ZdUNUzjTy7gDwr+/6yGGOMQ6eaAQAAAAAAAG6iSn7nGAAAAAAAAFARaI4BAAAAAADAY9EcAwAAAAAAgMeiOQYAAAAAAACPRXMMAAAAAAAAHovmGAAAAAAAADwWzTEAAAAAAAB4LJpjAAAAAAAA8Fg0xwAAAAAAAOCxaI4BAAAAAADAY9EcAwAAAAAAgMeiOQYAAAAAAACPRXMMJTJv3jxZLBbt37+/sqcCAADgUbp166Zu3bqV+rotW7Z07oTK6NJ69u/fL4vFonnz5jm0ncmTJ8tiseh///ufcycIoNxUxUxyNUOHDlX9+vUrexpuh+YYSqVbt26yWCyF/v3000+VPT0AbqCgKW+xWLRx40a79cYY1alTRxaLRTfccEMlzLD8ZGVlaerUqWrdurWqVaum0NBQde7cWW+99ZaMMZU9PQBOcHHGWSwW+fj46Morr9TQoUN16NChCp/P4cOHNXnyZG3fvr3U2zh69KjGjRunpk2bqlq1agoKClJsbKyeeOIJnTx50mlzBeB877//viwWiz766CO7dW3atJHFYtHatWvt1tWtW1edOnWqiCna4TUpnMmnsicA13DHHXdo4MCB8vf3ty676qqrNG3aNLux0dHRFTk1AG4uICBACxcuVHx8vM3yL7/8UgcPHrTJJXdw9OhR9ejRQ7t379bAgQM1evRonTt3Th9++KGGDBmi5cuXa8GCBfL29q7sqQJwgscff1wxMTE6d+6cNm/erHnz5mnjxo3auXOnAgICJEmrVq0q93kcPnxYU6ZMUf369dW2bVuHr//NN9+od+/eOn36tP72t78pNjZWkrR161Y99dRTWr9+fZF11KtXT2fPnpWvr29ZSgBQBgXHWRs3btTNN99sXZ6RkaGdO3fKx8dHmzZtUvfu3a3rfvvtN/32228aOHBghc+3AK9J4Sw0x1Ai3t7edi/EQkND9be//a3E28jKylJQUJCzpwbAzfXu3VuLFy/Wiy++KB+fP/+3tXDhQsXGxrrdx2mGDBmi3bt366OPPtKNN95oXf7AAw9o/Pjxmjlzptq1a6d//OMfFTan/Px8nT9/3vpCHYDzJCcnq3379pKku+66SzVr1tT06dO1bNkyDRgwQJLk5+dXmVO8rJMnT+rmm2+Wt7e3vvvuOzVt2tRm/ZNPPqnXX3+9yOtbLBbyBahk0dHRiomJsTtbPzU1VcYY/fWvf7VbV3D50jcwK5Kjr0lLyhijc+fOKTAw0Onbvpxz587Jz89PXl580K8icW+jRBz9zrGhQ4eqevXq2rdvn3r37q3g4GANHjxYkrRhwwb99a9/Vd26deXv7686derowQcf1NmzZwvdxoEDB3TDDTeoevXquvLKKzV79mxJ0o4dO3T99dcrKChI9erV08KFC+3mcfLkSY0ZM0Z16tSRv7+/GjVqpOnTpys/P79sdwiACjNo0CAdP35cKSkp1mXnz5/XBx98oNtvv91ufFZWlh566CHr875JkyaaOXOmzccRi/t+G4vFosmTJ1svZ2ZmasyYMapfv778/f0VERGhnj176ttvv7W53pYtW5SUlKTQ0FBVq1ZNXbt21aZNmxyqdfPmzVq5cqWGDh1q0xgrMG3aNDVu3FjTp0/X2bNnlZOTo/DwcA0bNsxubEZGhgICAjRu3DjrsuzsbE2aNEmNGjWy5u/DDz+s7Oxsu/tg9OjRWrBggVq0aCF/f3+tWLHCoVoAlE7nzp0lSfv27bMuK+w7x3799VfdeOONCgoKUkREhB588EGtXLlSFotF69ats9vujz/+qO7du6tatWq68sorNWPGDOu6devW6dprr5UkDRs2zPqxpJJ+B9irr76qQ4cO6dlnn7VrjElSZGSkHn300SKvX1Qm//TTTxowYIBq1aqlwMBANWnSRP/617+Kncuvv/6qRo0aqWXLljp69GiJ5g/ggvj4eH333Xc2r8s2bdqkFi1aKDk5WZs3b7Z5HbVp0yZZLBZdd911kqR33nlHsbGxCgwMVHh4uAYOHKjffvut0Nvatm2bOnXqpMDAQMXExGjOnDnlUlNubq6mTp2qhg0byt/fX/Xr19c///lPu2Of+vXr64YbbtDKlSvVvn17BQYG6tVXX9Utt9yia665xmZs3759ZbFYtGzZMuuyLVu2yGKx6PPPP5cknThxQuPGjVOrVq1UvXp1hYSEKDk5Wd9//73NttatWyeLxaJFixbp0Ucf1ZVXXqlq1aopIyNDkrR06VK1bNlSAQEBatmyZaEfe5WkRYsWKTY2VsHBwQoJCVGrVq30wgsvlPn+8yQ0x1BqeXl5+t///mfzd/r0aev63NxcJSYmKiIiQjNnzlT//v0lSYsXL9aZM2d07733atasWUpMTNSsWbN05513FnobycnJqlOnjmbMmKH69etr9OjRmjdvnpKSktS+fXtNnz5dwcHBuvPOO5WWlma97pkzZ9S1a1e98847uvPOO/Xiiy/quuuu04QJEzR27Njyv4MAOEX9+vUVFxend99917rs888/16lTp+xO4zfG6MYbb9Rzzz2npKQkPfvss2rSpInGjx9f6uf9Pffco1deeUX9+/fXyy+/rHHjxikwMFC7d++2jlmzZo26dOmijIwMTZo0Sf/+97918uRJXX/99fr6669LfFuffPKJJBWah5Lk4+Oj22+/XX/88Yc2bdokX19f3XzzzVq6dKnOnz9vM3bp0qXKzs623kf5+fm68cYbNXPmTPXt21ezZs1Sv3799Nxzz+m2226zu601a9bowQcf1G233aYXXniBL34FKkjBG5E1atQockxWVpauv/56ffHFF3rggQf0r3/9S1999VWRZ5T+8ccfSkpKUps2bfTMM8+oadOm+sc//mF9EdesWTM9/vjjkqSRI0fq7bff1ttvv60uXbqUaM7Lli1TYGCgbr31VgcqLd4PP/ygjh07as2aNRoxYoReeOEF9evXz5qThdm3b5+6dOmi4OBgrVu3TpGRkU6bD+AJ4uPjlZOToy1btliXbdq0SZ06dVKnTp106tQp7dy502Zd06ZNdcUVV+jJJ5/UnXfeqcaNG+vZZ5/VmDFjtHr1anXp0sXuOwf/+OMP9e7dW7GxsZoxY4auuuoq3XvvvXrzzTcdnvPlXpPeddddmjhxoq655ho999xz6tq1q6ZNm1boR0F//vlnDRo0SD179tQLL7ygtm3bqnPnzvr++++tzSpjjDZt2iQvLy9t2LDBet0NGzbIy8vL2ij85ZdftHTpUt1www169tlnNX78eO3YsUNdu3bV4cOH7W576tSp+uyzzzRu3Dj9+9//lp+fn1atWqX+/fvLYrFo2rRp6tevn4YNG6atW7faXDclJUWDBg1SjRo1NH36dD311FPq1q2bw2/SejwDlMDcuXONJJOWlmaMMaZr165Gkt3fkCFDjDHGDBkyxEgyjzzyiN22zpw5Y7ds2rRpxmKxmF9//dW6rGAb//73v63L/vjjDxMYGGgsFotZtGiRdflPP/1kJJlJkyZZl02dOtUEBQWZ//73vza39cgjjxhvb29z4MCB0twVACpIQe5888035qWXXjLBwcHW/PjrX/9qunfvbowxpl69eqZPnz7GGGOWLl1qJJknnnjCZlu33nqrsVgsZu/evcYYY9LS0owkM3fuXLvbvTRLQkNDzahRo4qcZ35+vmncuLFJTEw0+fn51uVnzpwxMTExpmfPniWuuV+/fkaS+eOPP4ocs2TJEiPJvPjii8YYY1auXGkkmU8++cRmXO/evU2DBg2sl99++23j5eVlNmzYYDNuzpw5RpLZtGmTdZkk4+XlZXbt2lXiuQNwTEHGffHFF+b33383v/32m/nggw9MrVq1jL+/v/ntt9+sY7t27Wq6du1qvfzMM88YSWbp0qXWZWfPnjVNmzY1kszatWttrivJvPXWW9Zl2dnZJioqyvTv39+67JtvvikyFy+nRo0apk2bNiUef2k9hWVyly5dTHBwsM2xoTHGJmcnTZpkJJnff//d7N6920RHR5trr73WnDhxwuEaABiza9cuI8lMnTrVGGNMTk6OCQoKMvPnzzfGGBMZGWlmz55tjDEmIyPDeHt7mxEjRpj9+/cbb29v8+STT9psb8eOHcbHx8dmeUEmPfPMM9Zl2dnZpm3btiYiIsKcP3++xPO93GvS7du3G0nmrrvusrneuHHjjCSzZs0a67J69eoZSWbFihU2Ywuycfny5cYYY3744Qcjyfz1r381HTt2tI678cYbTbt27ayXz507Z/Ly8my2lZaWZvz9/c3jjz9uXbZ27VojyTRo0MDudXLbtm1N7dq1zcmTJ63LVq1aZSSZevXqWZf9/e9/NyEhISY3N7ckdxuKwJljKLX69esrJSXF5u/hhx+2GXPvvffaXe/iz21nZWXpf//7nzp16iRjjL777ju78XfddZf132FhYWrSpImCgoKs38MhSU2aNFFYWJh++eUX67LFixerc+fOqlGjhs07CQkJCcrLy9P69evLVD+AijNgwACdPXtWn376qTIzM/Xpp58W+pHK5cuXy9vbWw888IDN8oceekjGGOtZEo4ICwvTli1bCn2XT5K2b9+uPXv26Pbbb9fx48etWZOVlaUePXpo/fr1Jf4od2ZmpiQpODi4yDEF6wrewbz++utVs2ZNvffee9Yxf/zxh1JSUmzOCFu8eLGaNWumpk2b2mTi9ddfL0l2v0DVtWtXNW/evETzBlB6CQkJqlWrlurUqaNbb71VQUFBWrZsma666qoir7NixQpdeeWVNh+/DggI0IgRIwodX716dZvv5PHz81OHDh1sjpvKIiMjo9jcctTvv/+u9evX6//+7/9Ut25dm3UWi8Vu/M6dO9W1a1fVr19fX3zxRbFn3QEoWrNmzXTFFVdYv0vs+++/V1ZWlvXXKDt16mQ9Gyk1NVV5eXmKj4/XkiVLlJ+frwEDBtgcY0RFRalx48Z2xxg+Pj66++67rZf9/Px0991369ixY9q2bZtDcy7uNeny5cslye7TAw899JAk6bPPPrNZHhMTo8TERJtl7dq1U/Xq1a2vHTds2KCrrrpKd955p7799ludOXNGxhht3LjR+rF4SfL397d+Z1heXp6OHz+u6tWrq0mTJnZfzSFd+M7Zi18nHzlyRNu3b9eQIUMUGhpqXd6zZ0+747OwsDBlZWXZfAUJHMcX8qPUgoKClJCQUOR6Hx+fQg/sDhw4oIkTJ2rZsmX6448/bNadOnXK5nJAQIBq1aplsyw0NFRXXXWV3cFRaGiozfb27NmjH374we76BY4dO1bk3AFULbVq1VJCQoIWLlyoM2fOKC8vr9CP7/z666+Kjo62e5HWrFkz63pHzZgxQ0OGDFGdOnUUGxur3r17684771SDBg0kXcga6cJBTVFOnTpVohdrBfPOzMxUWFhYoWMubaD5+Piof//+WrhwobKzs+Xv768lS5YoJyfHpjm2Z88e7d69u8SZGBMTc9n5Aii72bNn6+qrr9apU6f05ptvav369Zf9Fd5ff/1VDRs2tDsWatSoUaHjCztuqlGjhn744YeyTf7/CwkJsWaTMxQ07Vq2bFmi8X379lVkZKRWrlyp6tWrO20egKexWCzq1KmT9Y29TZs2KSIiwpotnTp10ksvvSRJ1iZZfHy89btdGzduXOh2L/0l2ujoaLsfarv66qslXfho+V/+8pcSz7m416S//vqrvLy87LIxKipKYWFhdseFhR37eHt7Ky4uzvoRyg0bNqhz586Kj49XXl6eNm/erMjISJ04ccKmOZafn68XXnhBL7/8stLS0pSXl2ddd8UVV9jdzqW3XTC3wu7TSxts9913n95//30lJyfryiuvVK9evTRgwAAlJSUVer+gcDTHUG4u7pYXyMvLU8+ePXXixAn94x//UNOmTRUUFKRDhw5p6NChdmdXXPoLmZdbbi76wu38/Hz17NnT7my2AgUBDMA13H777RoxYoTS09OVnJxcZPOoJAo780CSzYFLgQEDBqhz58766KOPtGrVKj399NOaPn26lixZouTkZGtuPf3002rbtm2h2y3pi7VmzZpp6dKl+uGHH4r8rp+CF7MXv2s4cOBAvfrqq/r888/Vr18/vf/++2ratKnatGljHZOfn69WrVrp2WefLXS7derUsblcGb/OBHiiDh06WH+tsl+/foqPj9ftt9+un3/+2WmNnpIcN5VF06ZNtX37dp0/f75Sflmzf//+mj9/vhYsWGBzNgoAx8XHx+uTTz7Rjh07rN83VqBTp04aP368Dh06pI0bNyo6OloNGjRQfn6+9cvoC8ubym5aF3Xcd6mijn3i4+P15JNP6ty5c9qwYYP+9a9/KSwsTC1bttSGDRus3294cXPs3//+tx577DH93//9n6ZOnarw8HB5eXlpzJgxhX6ioCzHXREREdq+fbtWrlypzz//XJ9//rnmzp2rO++8U/Pnzy/1dj0NzTFUqB07dui///2v5s+fb/OF0+VxCmjDhg11+vTpYs9uA+A6br75Zt19993avHmzzUcIL1avXj198cUXyszMtDl77KeffrKul/78outLvyC2qDPLateurfvuu0/33Xefjh07pmuuuUZPPvmkkpOT1bBhQ0kXzpwoa97ccMMNmjZtmt56661Cm2N5eXlauHChatSoYf3CV0nq0qWLateurffee0/x8fFas2aN3S+6NWzYUN9//7169OhR4oNEABXL29tb06ZNU/fu3fXSSy/pkUceKXRcvXr19OOPP8oYY/N83rt3b6lvuyy50LdvX6WmpurDDz/UoEGDSr2dAgVn5l78xd/Fefrpp+Xj46P77rtPwcHBhX7sHkDJxMfHS5I2btyoTZs2acyYMdZ1sbGx8vf317p167Rlyxb17t1b0oVjDGOMYmJiSnQCwuHDh5WVlWVz9th///tfSXLqDwDVq1dP+fn52rNnj/VTBJJ09OhRnTx50npceDmdO3fW+fPn9e677+rQoUPWJliXLl2szbGrr77a5kdAPvjgA3Xv3l1vvPGGzbZOnjypmjVrlmju0p+fULjYzz//bLfMz89Pffv2Vd++fZWfn6/77rtPr776qh577LEizyqGLb5zDBWq4J2Ei9+pNMaUy8/MDhgwQKmpqVq5cqXdupMnTyo3N9fptwmg/FSvXl2vvPKKJk+erL59+xY6pnfv3srLy7Oe8l/gueeek8ViUXJysqQLjayaNWvafffgyy+/bHM5Ly/P7uPeERERio6Otv4EeGxsrBo2bKiZM2fa/DpSgd9//73ENXbq1EkJCQmaO3euPv30U7v1//rXv/Tf//5XDz/8sM07jF5eXrr11lv1ySef6O2331Zubq7dL1AOGDBAhw4d0uuvv2633bNnzyorK6vE8wRQfrp166YOHTro+eef17lz5wodk5iYqEOHDmnZsmXWZefOnSv0+V1SBS9SL33ToCTuuece1a5dWw899JD1Be7Fjh07pieeeKLE26tVq5a6dOmiN998UwcOHLBZV9jZbhaLRa+99ppuvfVWDRkyxOZ+AeCY9u3bKyAgQAsWLNChQ4dszhzz9/fXNddco9mzZysrK8vaSLvlllvk7e2tKVOm2D1HjTE6fvy4zbLc3Fy9+uqr1svnz5/Xq6++qlq1aik2NtZptRQ0755//nmb5QVn0ffp06dE2+nYsaN8fX01ffp0hYeHq0WLFpIuNM02b96sL7/80uasMenC695L74vFixfr0KFDJbrN2rVrq23btpo/f77NsWhKSop+/PFHm7GX3r9eXl5q3bq1JFmPV3F5nDmGCtW0aVM1bNhQ48aN06FDhxQSEqIPP/zQ7rvHnGH8+PFatmyZbrjhBg0dOlSxsbHKysrSjh079MEHH2j//v0l6toDqDqK+14v6cLZC927d9e//vUv7d+/X23atNGqVav08ccfa8yYMdazvKQLP/bx1FNP6a677lL79u21fv16uxd1mZmZuuqqq3TrrbeqTZs2ql69ur744gt98803euaZZyRdOAD5z3/+o+TkZLVo0ULDhg3TlVdeqUOHDmnt2rUKCQnRJ598UuIa33rrLfXo0UM33XSTbr/9dnXu3FnZ2dlasmSJ1q1bp9tuu03jx4+3u95tt92mWbNmadKkSWrVqpXNO6SSdMcdd+j999/XPffco7Vr1+q6665TXl6efvrpJ73//vtauXKl9aNdACrX+PHj9de//lXz5s3TPffcY7f+7rvv1ksvvaRBgwbp73//u2rXrq0FCxYoICBAUunOAmvYsKHCwsI0Z84cBQcHKygoSB07dizR9w/WqFFDH330kXr37q22bdvqb3/7m/UF7rfffqt3331XcXFxDs3nxRdfVHx8vK655hqNHDlSMTEx2r9/vz777DNt377dbryXl5feeecd9evXTwMGDNDy5cutPzgCoOT8/Px07bXXasOGDfL397drVnXq1Ml6DFTQHGvYsKGeeOIJTZgwQfv371e/fv0UHBystLQ0ffTRRxo5cqTGjRtn3UZ0dLSmT5+u/fv36+qrr9Z7772n7du367XXXrP7frKyaNOmjYYMGaLXXntNJ0+eVNeuXfX1119r/vz56tevn7p3716i7VSrVk2xsbHavHmz+vbta83YLl26KCsrS1lZWXbNsRtuuEGPP/64hg0bpk6dOmnHjh1asGCB9czYkpg2bZr69Omj+Ph4/d///Z9OnDihWbNmqUWLFjZvyN511106ceKErr/+el111VX69ddfNWvWLLVt29bueBDFqIyfyITrKfi58bS0NGPMhZ/NbdGiRZHjhwwZYoKCggpd9+OPP5qEhARTvXp1U7NmTTNixAjz/fff2/2Ed1HbKOq269WrZ/r06WOzLDMz00yYMME0atTI+Pn5mZo1a5pOnTqZmTNnOvQzwQAqXkHufPPNN8WOu/S5n5mZaR588EETHR1tfH19TePGjc3TTz9t8vPzba535swZM3z4cBMaGmqCg4PNgAEDzLFjx4wkM2nSJGPMhZ8WHz9+vGnTpo0JDg42QUFBpk2bNubll1+2m8d3331nbrnlFnPFFVcYf39/U69ePTNgwACzevVqh2vPzMw0kydPNi1atDCBgYEmODjYXHfddWbevHl2dRTIz883derUMZLME088UeiY8+fPm+nTp5sWLVoYf39/U6NGDRMbG2umTJliTp06ZR0nyYwaNcrheQMoueIyLi8vzzRs2NA0bNjQ5Obmmq5du5quXbvajPnll19Mnz59TGBgoKlVq5Z56KGHzIcffmgkmc2bN1vHFXXcNGTIEFOvXj2bZR9//LFp3ry58fHxsTsuK4nDhw+bBx980Fx99dUmICDAVKtWzcTGxponn3zSJmMurSctLa3Q29u5c6e5+eabTVhYmAkICDBNmjQxjz32mHX9pEmTjCTz+++/W5edOXPGdO3a1VSvXt3mfgBQchMmTDCSTKdOnezWLVmyxEgywcHBJjc312bdhx9+aOLj401QUJAJCgoyTZs2NaNGjTI///yzdUxBJm3dutXExcWZgIAAU69ePfPSSy85PM/LvSY1xpicnBwzZcoUExMTY3x9fU2dOnXMhAkTzLlz52zGFfZa8mLjx483ksz06dNtljdq1MhIMvv27bNZfu7cOfPQQw+Z2rVrm8DAQHPdddeZ1NRUu/xbu3atkWQWL15c6O1++OGHplmzZsbf3980b97cLFmyxC6/P/jgA9OrVy8TERFh/Pz8TN26dc3dd99tjhw5Uux9A1sWY5z0TZwAAAAAKs3zzz+vBx98UAcPHtSVV15Z2dMBAMBl0BwDAAAAXMzZs2dtvnvw3LlzateunfLy8gr93i8AAFA0vnMMAIBylpeXd9kv5q9evXql/9Q5ANdxyy23qG7dumrbtq1OnTqld955Rz/99JMWLFjgtNs4e/as3Y+SXCo8PFx+fn5Ou00A+P3335WXl1fkej8/P4WHh1fgjOAJaI4BAFDOfvvtt8t+qfWkSZM0efLkipkQAJeXmJio//znP1qwYIHy8vLUvHlzLVq0yO6Xasvivffe07Bhw4ods3btWnXr1s1ptwkA1157rX799dci13ft2lXr1q2ruAnBI/CxSgAAytm5c+e0cePGYsc0aNDAoV8wAoDyduTIEe3atavYMbGxsapRo0YFzQiAJ9i0aZPOnj1b5PoaNWrY/YomUFY0xwAAAAAAAOCxvCp7AgAAAAAAAEBlccnvHMvPz9fhw4cVHBwsi8VS2dMBcBnGGGVmZio6OlpeXvTkL0WmAa6FTCsaeQa4FvKsaOQZ4HrKkmku2Rw7fPiw6tSpU9nTAOCg3377TVdddVVlT6PKIdMA10Sm2SPPANdEntkjzwDXVZpMc8nmWHBwsKQLBYeEhBQ7NicnR6tWrVKvXr3k6+tbEdOrENTlWjy9royMDNWpU8f63IWtkmaapz+OXA11uRZH6iLTisYxGnW5EnesSXL9PJs2bZqWLFmin376SYGBgerUqZOmT5+uJk2aWMecO3dODz30kBYtWqTs7GwlJibq5ZdfVmRkpHXMgQMHdO+992rt2rWqXr26hgwZomnTpsnHp2Qvgckz6nI11FW2THPJ5ljBaa0hISElCqpq1aopJCTE7R4g1OU6qOuCqnZKelU5+CpppvE4ci3U5VpKU1dVy7SqgGM06nIl7liT5Pp59uWXX2rUqFG69tprlZubq3/+85/q1auXfvzxRwUFBUmSHnzwQX322WdavHixQkNDNXr0aN1yyy3atGmTJCkvL099+vRRVFSUvvrqKx05ckR33nmnfH199e9//7tE8yDPqMvVUNefSpNpfLAcgMcqOPjavHmzUlJSlJOTo169eikrK8s65sEHH9Qnn3yixYsX68svv9Thw4d1yy23WNcXHHydP39eX331lebPn6958+Zp4sSJlVESAACAS1uxYoWGDh2qFi1aqE2bNpo3b54OHDigbdu2SZJOnTqlN954Q88++6yuv/56xcbGau7cufrqq6+0efNmSdKqVav0448/6p133lHbtm2VnJysqVOnavbs2Tp//nxllgeginLJM8cAwBlWrFhhc3nevHmKiIjQtm3b1KVLF+vB18KFC3X99ddLkubOnatmzZpp8+bN+stf/mI9+Priiy8UGRmptm3baurUqfrHP/6hyZMny8/PrzJKA+BhqsqZsADgbKdOnZIkhYeHS5K2bdumnJwcJSQkWMc0bdpUdevWVWpqqv7yl78oNTVVrVq1ssm3xMRE3Xvvvdq1a5fatWtndzvZ2dnKzs62Xs7IyJB04ayVnJycYudYsP5y41wNdbkW6ipb7RzpAMD/V1EHXwDgbFXlY0gA4Ez5+fkaM2aMrrvuOrVs2VKSlJ6eLj8/P4WFhdmMjYyMVHp6unXMxcdmBesL1hVm2rRpmjJlit3yVatWqVq1aiWab0pKSonGuRrqci2eXNeZM2dKvX2aYwCgij34Ku07k7wb5Fqoy7VU1LuS5YUzYQG4o1GjRmnnzp3auHFjud/WhAkTNHbsWOvlgi/27tWrV4m+cywlJUU9e/Z0u+96oi7XQV1/vq4qDZpjAKCKPfgq6zuTnvxukCuiLtdS3u9KVhTOhAXg6kaPHq1PP/1U69ev11VXXWVdHhUVpfPnz+vkyZM2b2AePXpUUVFR1jFff/21zfaOHj1qXVcYf39/+fv72y339fUtcaPBkbGuhLpciyfXVZa6aY4B8HgVffBV2ncmeTfItVCXa6modyUrgiucCVsw5uL/ugvqch3uWJPk+mfCGmN0//3366OPPtK6desUExNjsz42Nla+vr5avXq1+vfvL0n6+eefdeDAAcXFxUmS4uLi9OSTT+rYsWOKiIiQdOHNj5CQEDVv3rxiCwLgEjymOdZy8kpl5134Oc/9T/Wp5NkAqAoq6+CrrO9MtntyjTXPJPfJNE9+l8sVeXJdVb1uVzoTVvLssxBdkTvW5Y41Sa57JuyoUaO0cOFCffzxxwoODrY250NDQxUYGKjQ0FANHz5cY8eOVXh4uEJCQnT//fcrLi5Of/nLXyRJvXr1UvPmzXXHHXdoxowZSk9P16OPPqpRo0YVegzmLLzmBFy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      "text/plain": [
       "<Figure size 1500x1500 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the data distribution\n",
    "data0.hist(bins = 50,figsize = (15,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 821
    },
    "colab_type": "code",
    "id": "jy9fjgj3M7zc",
    "outputId": "0292cc0a-8436-49d9-c724-ad9345f1b693"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'edisonlearning.com'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Correlation heatmap\u001b[39;00m\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m15\u001b[39m,\u001b[38;5;241m13\u001b[39m))\n\u001b[1;32m----> 4\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(\u001b[43mdata0\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\pandas\\core\\frame.py:11049\u001b[0m, in \u001b[0;36mDataFrame.corr\u001b[1;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[0;32m  11047\u001b[0m cols \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m  11048\u001b[0m idx \u001b[38;5;241m=\u001b[39m cols\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m> 11049\u001b[0m mat \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnan\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m  11051\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpearson\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m  11052\u001b[0m     correl \u001b[38;5;241m=\u001b[39m libalgos\u001b[38;5;241m.\u001b[39mnancorr(mat, minp\u001b[38;5;241m=\u001b[39mmin_periods)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\pandas\\core\\frame.py:1993\u001b[0m, in \u001b[0;36mDataFrame.to_numpy\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1992\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(dtype)\n\u001b[1;32m-> 1993\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1994\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dtype:\n\u001b[0;32m   1995\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(result, dtype\u001b[38;5;241m=\u001b[39mdtype)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\pandas\\core\\internals\\managers.py:1694\u001b[0m, in \u001b[0;36mBlockManager.as_array\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1692\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m   1693\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1694\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_interleave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1695\u001b[0m     \u001b[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001b[39;00m\n\u001b[0;32m   1696\u001b[0m     \u001b[38;5;66;03m# to further copy if copy=True or setting na_value\u001b[39;00m\n\u001b[0;32m   1698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\pandas\\core\\internals\\managers.py:1753\u001b[0m, in \u001b[0;36mBlockManager._interleave\u001b[1;34m(self, dtype, na_value)\u001b[0m\n\u001b[0;32m   1751\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1752\u001b[0m         arr \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mget_values(dtype)\n\u001b[1;32m-> 1753\u001b[0m     \u001b[43mresult\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m arr\n\u001b[0;32m   1754\u001b[0m     itemmask[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1756\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m itemmask\u001b[38;5;241m.\u001b[39mall():\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'edisonlearning.com'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation heatmap\n",
    "\n",
    "plt.figure(figsize=(15,13))\n",
    "sns.heatmap(data0.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9cbVPMe8NxFN"
   },
   "source": [
    "## **5. Data Preprocessing & EDA**\n",
    "Here, we clean the data by applying data preprocesssing techniques and transform the data to use it in the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 307
    },
    "colab_type": "code",
    "id": "b_HKPelgnmf8",
    "outputId": "f9fb9a1b-0ddc-470a-f4c0-db248a1a94f3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Have_IP</th>\n",
       "      <th>Have_At</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>URL_Depth</th>\n",
       "      <th>Redirection</th>\n",
       "      <th>https_Domain</th>\n",
       "      <th>TinyURL</th>\n",
       "      <th>Prefix/Suffix</th>\n",
       "      <th>dns</th>\n",
       "      <th>Web_Traffic</th>\n",
       "      <th>Domain_Age</th>\n",
       "      <th>Domain_End</th>\n",
       "      <th>iFrame</th>\n",
       "      <th>Mouse_Over</th>\n",
       "      <th>Right_Click</th>\n",
       "      <th>Web_Forwards</th>\n",
       "      <th>Fuzzy_Hashing</th>\n",
       "      <th>Perceptual_Hashing</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.205000</td>\n",
       "      <td>0.745000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.205000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.099748</td>\n",
       "      <td>0.404715</td>\n",
       "      <td>1.190403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.264052</td>\n",
       "      <td>0.271974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.404715</td>\n",
       "      <td>0.099748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.499648</td>\n",
       "      <td>0.478167</td>\n",
       "      <td>0.501255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Have_IP     Have_At  URL_Length   URL_Depth  Redirection  https_Domain  \\\n",
       "count    200.0  200.000000  200.000000  200.000000        200.0         200.0   \n",
       "mean       0.0    0.010000    0.205000    0.745000          0.0           0.0   \n",
       "std        0.0    0.099748    0.404715    1.190403          0.0           0.0   \n",
       "min        0.0    0.000000    0.000000    0.000000          0.0           0.0   \n",
       "25%        0.0    0.000000    0.000000    0.000000          0.0           0.0   \n",
       "50%        0.0    0.000000    0.000000    0.000000          0.0           0.0   \n",
       "75%        0.0    0.000000    0.000000    1.000000          0.0           0.0   \n",
       "max        0.0    1.000000    1.000000    6.000000          0.0           0.0   \n",
       "\n",
       "          TinyURL  Prefix/Suffix    dns  Web_Traffic  Domain_Age  Domain_End  \\\n",
       "count  200.000000     200.000000  200.0        200.0       200.0       200.0   \n",
       "mean     0.075000       0.080000    0.0          1.0         1.0         1.0   \n",
       "std      0.264052       0.271974    0.0          0.0         0.0         0.0   \n",
       "min      0.000000       0.000000    0.0          1.0         1.0         1.0   \n",
       "25%      0.000000       0.000000    0.0          1.0         1.0         1.0   \n",
       "50%      0.000000       0.000000    0.0          1.0         1.0         1.0   \n",
       "75%      0.000000       0.000000    0.0          1.0         1.0         1.0   \n",
       "max      1.000000       1.000000    0.0          1.0         1.0         1.0   \n",
       "\n",
       "           iFrame  Mouse_Over  Right_Click  Web_Forwards  Fuzzy_Hashing  \\\n",
       "count  200.000000  200.000000        200.0    200.000000     200.000000   \n",
       "mean     0.205000    0.010000          0.0      0.020000       0.540000   \n",
       "std      0.404715    0.099748          0.0      0.140351       0.499648   \n",
       "min      0.000000    0.000000          0.0      0.000000       0.000000   \n",
       "25%      0.000000    0.000000          0.0      0.000000       0.000000   \n",
       "50%      0.000000    0.000000          0.0      0.000000       1.000000   \n",
       "75%      0.000000    0.000000          0.0      0.000000       1.000000   \n",
       "max      1.000000    1.000000          0.0      1.000000       1.000000   \n",
       "\n",
       "       Perceptual_Hashing       Label  \n",
       "count          200.000000  200.000000  \n",
       "mean             0.350000    0.500000  \n",
       "std              0.478167    0.501255  \n",
       "min              0.000000    0.000000  \n",
       "25%              0.000000    0.000000  \n",
       "50%              0.000000    0.500000  \n",
       "75%              1.000000    1.000000  \n",
       "max              1.000000    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data0.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XBqTcpqen_fB"
   },
   "source": [
    "The above obtained result shows that the most of the data is made of 0's & 1's except 'Domain' & 'URL_Depth' columns. The Domain column doesnt have any significance to the machine learning model training. So dropping the *'Domain'* column from the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tdpRw0Bcn_K1"
   },
   "outputs": [],
   "source": [
    "#Dropping the Domain column\n",
    "data = data0.drop(['Domain'], axis = 1).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "peN2Q-WdowNL"
   },
   "source": [
    "This leaves us with 16 features & a target column. The *'URL_Depth'* maximum value is 20. According to my understanding, there is no necessity to change this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "iGO3nWV1nvW1",
    "outputId": "f9cc1bae-b91b-483d-a1ca-44aea204dae5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Have_IP               0\n",
       "Have_At               0\n",
       "URL_Length            0\n",
       "URL_Depth             0\n",
       "Redirection           0\n",
       "https_Domain          0\n",
       "TinyURL               0\n",
       "Prefix/Suffix         0\n",
       "dns                   0\n",
       "Web_Traffic           0\n",
       "Domain_Age            0\n",
       "Domain_End            0\n",
       "iFrame                0\n",
       "Mouse_Over            0\n",
       "Right_Click           0\n",
       "Web_Forwards          0\n",
       "Fuzzy_Hashing         0\n",
       "Perceptual_Hashing    0\n",
       "Label                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the data for null or missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5xo6cAqAqJOq"
   },
   "source": [
    "In the feature extraction file, the extracted features of legitmate & phishing url datasets are just concatenated without any shuffling. This resulted in top 5000 rows of legitimate url data & bottom 5000 of phishing url data. \n",
    "\n",
    "To even out the distribution while splitting the data into training & testing sets, we need to shuffle it. This even evades the case of overfitting while model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "colab_type": "code",
    "id": "4LZnaoU_qBsz",
    "outputId": "df212692-ea66-4d67-a4aa-00a256010f69"
   },
   "outputs": [
    {
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       "      <th>Have_IP</th>\n",
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      "text/plain": [
       "   Have_IP  Have_At  URL_Length  URL_Depth  Redirection  https_Domain  \\\n",
       "0        0        0           0          1            0             0   \n",
       "1        0        0           0          1            0             0   \n",
       "2        0        0           1          2            0             0   \n",
       "3        0        0           0          2            0             0   \n",
       "4        0        0           0          0            0             0   \n",
       "\n",
       "   TinyURL  Prefix/Suffix  dns  Web_Traffic  Domain_Age  Domain_End  iFrame  \\\n",
       "0        0              0    0            1           1           1       0   \n",
       "1        0              0    0            1           1           1       0   \n",
       "2        0              0    0            1           1           1       0   \n",
       "3        0              0    0            1           1           1       0   \n",
       "4        0              0    0            1           1           1       0   \n",
       "\n",
       "   Mouse_Over  Right_Click  Web_Forwards  Fuzzy_Hashing  Perceptual_Hashing  \\\n",
       "0           0            0             0              1                   1   \n",
       "1           0            0             0              0                   0   \n",
       "2           0            0             0              0                   1   \n",
       "3           0            0             1              1                   1   \n",
       "4           0            0             0              0                   0   \n",
       "\n",
       "   Label  \n",
       "0      1  \n",
       "1      0  \n",
       "2      0  \n",
       "3      1  \n",
       "4      0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shuffling the rows in the dataset so that when splitting the train and test set are equally distributed\n",
    "data = data.sample(frac=1).reset_index(drop=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4FsQosd_nycx"
   },
   "source": [
    "From the above execution, it is clear that the data doesnot have any missing values.\n",
    "\n",
    "By this, the data is throughly preprocessed & is ready for training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fs5hu2UR4SIF"
   },
   "source": [
    "## **6. Splitting the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "FzEU-wcLN8K7",
    "outputId": "534f9839-31e6-4b19-b469-c16db57fd5a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200, 18), (200,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sepratating & assigning features and target columns to X & y\n",
    "y = data['Label']\n",
    "X = data.drop('Label',axis=1)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "84xKobSqAV3U",
    "outputId": "20c0a9f7-d20e-4176-f815-238727c44336"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((160, 18), (40, 18))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into train and test sets: 80-20 split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size = 0.2, random_state = 12)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Have_IP  Have_At  URL_Length  URL_Depth  Redirection  https_Domain  \\\n",
       "77         0        0           0          1            0             0   \n",
       "47         0        0           0          0            0             0   \n",
       "94         0        0           1          2            0             0   \n",
       "14         0        0           0          0            0             0   \n",
       "41         0        0           0          0            0             0   \n",
       "..       ...      ...         ...        ...          ...           ...   \n",
       "3          0        0           0          2            0             0   \n",
       "130        0        0           0          1            0             0   \n",
       "134        0        0           0          0            0             0   \n",
       "155        0        0           0          2            0             0   \n",
       "75         0        0           1          1            0             0   \n",
       "\n",
       "     TinyURL  Prefix/Suffix  dns  Web_Traffic  Domain_Age  Domain_End  iFrame  \\\n",
       "77         0              0    0            1           1           1       0   \n",
       "47         0              0    0            1           1           1       0   \n",
       "94         0              0    0            1           1           1       0   \n",
       "14         0              0    0            1           1           1       1   \n",
       "41         0              0    0            1           1           1       0   \n",
       "..       ...            ...  ...          ...         ...         ...     ...   \n",
       "3          0              0    0            1           1           1       0   \n",
       "130        1              0    0            1           1           1       0   \n",
       "134        0              0    0            1           1           1       1   \n",
       "155        0              0    0            1           1           1       0   \n",
       "75         0              0    0            1           1           1       0   \n",
       "\n",
       "     Mouse_Over  Right_Click  Web_Forwards  Fuzzy_Hashing  Perceptual_Hashing  \n",
       "77            0            0             0              0                   1  \n",
       "47            0            0             0              0                   0  \n",
       "94            0            0             0              0                   0  \n",
       "14            0            0             0              1                   0  \n",
       "41            0            0             0              0                   1  \n",
       "..          ...          ...           ...            ...                 ...  \n",
       "3             0            0             1              1                   1  \n",
       "130           0            0             0              0                   0  \n",
       "134           0            0             0              0                   0  \n",
       "155           0            0             0              0                   0  \n",
       "75            0            0             0              1                   0  \n",
       "\n",
       "[160 rows x 18 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ah9B035xOjs1"
   },
   "source": [
    "## **7. Machine Learning Models & Training**\n",
    "\n",
    "From the dataset above, it is clear that this is a supervised machine learning task. There are two major types of supervised machine learning problems, called classification and regression. \n",
    "\n",
    "This data set comes under classification problem, as the input URL is classified as phishing (1) or legitimate (0). The supervised machine learning models (classification) considered to train the dataset in this notebook are:\n",
    "* Decision Tree\n",
    "* Random Forest\n",
    "* Multilayer Perceptrons\n",
    "* XGBoost\n",
    "* Autoencoder Neural Network\n",
    "* Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D5Tg_ei0-xPU"
   },
   "outputs": [],
   "source": [
    "#importing packages\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DPBHdBikSXHv"
   },
   "outputs": [],
   "source": [
    "# Creating holders to store the model performance results\n",
    "ML_Model = []\n",
    "acc_train = []\n",
    "acc_test = []\n",
    "\n",
    "#function to call for storing the results\n",
    "def storeResults(model, a,b):\n",
    "  ML_Model.append(model)\n",
    "  acc_train.append(round(a, 3))\n",
    "  acc_test.append(round(b, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YOolxQWs-VbB"
   },
   "source": [
    "### **7.1. Decision Tree Classifier**\n",
    " Decision trees are widely used models for classification and regression tasks. Essentially, they learn a hierarchy of if/else questions, leading to a decision. Learning a decision tree means learning the sequence of if/else questions that gets us to the true answer most quickly.\n",
    " \n",
    "In the machine learning setting, these questions are called tests (not to be confused with the test set, which is the data we use to test to see how generalizable our model is). To build a tree, the algorithm searches over all possible tests and finds the one that is most informative about the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 117
    },
    "colab_type": "code",
    "id": "1kzsjtudy-0w",
    "outputId": "80b84eba-eeb1-48d1-d95a-412b7cfb4c45"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree model \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# instantiate the model \n",
    "tree = DecisionTreeClassifier(max_depth = 5)\n",
    "# fit the model \n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cpPk7O-MrTZi"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_tree = tree.predict(X_test)\n",
    "y_train_tree = tree.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kLn-_qOuS_9Y"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "X4wDTnFZrz3q",
    "outputId": "a8bf5873-8185-4f18-e0f0-87717975e5a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree: Accuracy on training Data: 0.894\n",
      "Decision Tree: Accuracy on test Data: 0.850\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_tree = accuracy_score(y_train,y_train_tree)\n",
    "acc_test_tree = accuracy_score(y_test,y_test_tree)\n",
    "\n",
    "print(\"Decision Tree: Accuracy on training Data: {:.3f}\".format(acc_train_tree))\n",
    "print(\"Decision Tree: Accuracy on test Data: {:.3f}\".format(acc_test_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "LITrJdVGWwTl",
    "outputId": "363e0abd-28df-4703-b784-5f5af37cab30"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the feature improtance in the model\n",
    "plt.figure(figsize=(9,7))\n",
    "n_features = X_train.shape[1]\n",
    "plt.barh(range(n_features), tree.feature_importances_, align='center')\n",
    "plt.yticks(np.arange(n_features), X_train.columns)\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XpC9PAn5RTfY"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5XKvXxr9RSxl"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Decision Tree', acc_train_tree, acc_test_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LuDTMcmb-n5x"
   },
   "source": [
    "### **7.2. Random Forest Classifier**\n",
    "Random forests for regression and classification are currently among the most widely used machine learning methods.A random forest is essentially a collection of decision trees, where each tree is slightly different from the others. The idea behind random forests is that each tree might do a relatively good job of predicting, but will likely overfit on part of the data.\n",
    "\n",
    "If we build many trees, all of which work well and overfit in different ways, we can reduce the amount of overfitting by averaging their results. To build a random forest model, you need to decide on the number of trees to build (the n_estimators parameter of RandomForestRegressor or RandomForestClassifier). They are very powerful, often work well without heavy tuning of the parameters, and don’t require scaling of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150
    },
    "colab_type": "code",
    "id": "2fmB9rPSsR6y",
    "outputId": "27ddebf4-bee1-4eec-eb4e-995d4cdc08b2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=5)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=5)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# instantiate the model\n",
    "forest = RandomForestClassifier(max_depth=5)\n",
    "\n",
    "# fit the model \n",
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J1Qck-wrsabB"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_forest = forest.predict(X_test)\n",
    "y_train_forest = forest.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i8TybBPHT1ao"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "Oguf-37tsboO",
    "outputId": "34386ec6-a7f0-4185-b3c0-a40de3239fb7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest: Accuracy on training Data: 0.894\n",
      "Random forest: Accuracy on test Data: 0.850\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_forest = accuracy_score(y_train,y_train_forest)\n",
    "acc_test_forest = accuracy_score(y_test,y_test_forest)\n",
    "\n",
    "print(\"Random forest: Accuracy on training Data: {:.3f}\".format(acc_train_forest))\n",
    "print(\"Random forest: Accuracy on test Data: {:.3f}\".format(acc_test_forest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "m9GZGxvZ9jnB",
    "outputId": "465186a8-d622-4427-c148-9dff349b40eb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the feature improtance in the model\n",
    "plt.figure(figsize=(9,7))\n",
    "n_features = X_train.shape[1]\n",
    "plt.barh(range(n_features), forest.feature_importances_, align='center')\n",
    "plt.yticks(np.arange(n_features), X_train.columns)\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t6U_BEF8W-FS"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YNf4EXHUW-FU"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Random Forest', acc_train_forest, acc_test_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VJ_EW8QUC0bn"
   },
   "source": [
    "### **7.3. Multilayer Perceptrons (MLPs): Deep Learning**\n",
    "Multilayer perceptrons (MLPs) are also known as (vanilla) feed-forward neural networks, or sometimes just neural networks. Multilayer perceptrons can be applied for both classification and regression problems.\n",
    "\n",
    "MLPs can be viewed as generalizations of linear models that perform multiple stages of processing to come to a decision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150
    },
    "colab_type": "code",
    "id": "JSFAbsgnAxqv",
    "outputId": "2828ce2e-95ec-4dfd-e7dd-5d3da152ea09"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\R\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multilayer Perceptrons model\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# instantiate the model\n",
    "mlp = MLPClassifier(alpha=0.001, hidden_layer_sizes=([100,100,100]))\n",
    "\n",
    "# fit the model \n",
    "mlp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gyuSg6w_A4pN"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_mlp = mlp.predict(X_test)\n",
    "y_train_mlp = mlp.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UlDx0rDXatCl"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "z2ndgKQbA64_",
    "outputId": "40ddef62-9dd4-4d55-b5ba-9932ba07a0b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multilayer Perceptrons: Accuracy on training Data: 0.906\n",
      "Multilayer Perceptrons: Accuracy on test Data: 0.825\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_mlp = accuracy_score(y_train,y_train_mlp)\n",
    "acc_test_mlp = accuracy_score(y_test,y_test_mlp)\n",
    "\n",
    "print(\"Multilayer Perceptrons: Accuracy on training Data: {:.3f}\".format(acc_train_mlp))\n",
    "print(\"Multilayer Perceptrons: Accuracy on test Data: {:.3f}\".format(acc_test_mlp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zjBgfI64Xubd"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N0fsq4yEXubk"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Multilayer Perceptrons', acc_train_mlp, acc_test_mlp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lVuTty-KaS4c"
   },
   "source": [
    "### **7.4. XGBoost Classifier**\n",
    "XGBoost is one of the most popular machine learning algorithms these days. XGBoost stands for eXtreme Gradient Boosting. Regardless of the type of prediction task at hand; regression or classification. XGBoost is an implementation of gradient boosted decision trees designed for speed and performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "colab_type": "code",
    "id": "oIIQGzxgAREc",
    "outputId": "fc27da07-7071-4fbf-9d05-05e514ad9b3e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#XGBoost Classification model\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# instantiate the model\n",
    "xgb = XGBClassifier(learning_rate=0.4,max_depth=7)\n",
    "#fit the model\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_fx9xbzfAUO-"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_xgb = xgb.predict(X_test)\n",
    "y_train_xgb = xgb.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pwoDNqDIaxB9"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "x1NNeI-NaxCA",
    "outputId": "d021057e-e9bc-487d-b584-9fb2492305de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost: Accuracy on training Data: 0.900\n",
      "XGBoost : Accuracy on test Data: 0.850\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_xgb = accuracy_score(y_train,y_train_xgb)\n",
    "acc_test_xgb = accuracy_score(y_test,y_test_xgb)\n",
    "\n",
    "print(\"XGBoost: Accuracy on training Data: {:.3f}\".format(acc_train_xgb))\n",
    "print(\"XGBoost : Accuracy on test Data: {:.3f}\".format(acc_test_xgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_g2HQNotaxCQ"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sFNo8jskaxCS"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('XGBoost', acc_train_xgb, acc_test_xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EwQ8DP4OsmcG"
   },
   "source": [
    "### **7.5. Autoencoder Neural Network**\n",
    "An auto encoder is a neural network that has the same number of input neurons as it does outputs. The hidden layers of the neural network will have fewer neurons than the input/output neurons. Because there are fewer neurons, the auto-encoder must learn to encode the input to the fewer hidden neurons. The predictors (x) and output (y) are exactly the same in an auto encoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "7cy28vy3fDab",
    "outputId": "2947530e-ed77-40b2-bc9e-c3c575ff4faf"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow.python'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[60], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#importing required packages\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input, Dense\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m regularizers\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\__init__.py:8\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _tf_keras\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m applications\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\_tf_keras\\__init__.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_tf_keras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m keras\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\_tf_keras\\keras\\__init__.py:8\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m callbacks\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\activations\\__init__.py:8\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deserialize\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m serialize\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\__init__.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\activations\\__init__.py:3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtypes\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m elu\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m exponential\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m gelu\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\activations\\activations.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ops\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_export\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m keras_export\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\backend\\__init__.py:9\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;66;03m# When using the torch backend,\u001b[39;00m\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;66;03m# torch needs to be imported first, otherwise it will segfault\u001b[39;00m\n\u001b[0;32m      6\u001b[0m     \u001b[38;5;66;03m# upon import.\u001b[39;00m\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m result_type\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras_tensor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KerasTensor\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras_tensor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m any_symbolic_tensors\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\backend\\common\\__init__.py:2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_utils\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m result_type\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvariables\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutocastScope\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvariables\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KerasVariable\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\backend\\common\\dtypes.py:5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_export\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m keras_export\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvariables\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m standardize_dtype\n\u001b[0;32m      7\u001b[0m BOOL_TYPES \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbool\u001b[39m\u001b[38;5;124m\"\u001b[39m,)\n\u001b[0;32m      8\u001b[0m INT_TYPES \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muint8\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     10\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muint16\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mint64\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     17\u001b[0m )\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\backend\\common\\variables.py:11\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstateless_scope\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m in_stateless_scope\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodule_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tensorflow \u001b[38;5;28;01mas\u001b[39;00m tf\n\u001b[1;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnaming\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m auto_name\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mKerasVariable\u001b[39;00m:\n\u001b[0;32m     15\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Represents a backend-agnostic variable in Keras.\u001b[39;00m\n\u001b[0;32m     16\u001b[0m \n\u001b[0;32m     17\u001b[0m \u001b[38;5;124;03m    A `Variable` acts as a container for state. It holds a tensor value and can\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     77\u001b[0m \u001b[38;5;124;03m    ```\u001b[39;00m\n\u001b[0;32m     78\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\utils\\__init__.py:12\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m enable_interactive_logging\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_interactive_logging_enabled\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_visualization\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m model_to_dot\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_visualization\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_model\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumerical_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m normalize\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\utils\\model_visualization.py:8\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_export\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m keras_export\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m io_utils\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tree\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     11\u001b[0m     \u001b[38;5;66;03m# pydot-ng is a fork of pydot that is better maintained.\u001b[39;00m\n\u001b[0;32m     12\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpydot_ng\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpydot\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\utils\\tree.py:12\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# Register backend-specific node classes\u001b[39;00m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtensorflow\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m---> 12\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrackable\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_structures\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ListWrapper\n\u001b[0;32m     14\u001b[0m     optree\u001b[38;5;241m.\u001b[39mregister_pytree_node(\n\u001b[0;32m     15\u001b[0m         ListWrapper,\n\u001b[0;32m     16\u001b[0m         \u001b[38;5;28;01mlambda\u001b[39;00m x: (x, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m     17\u001b[0m         \u001b[38;5;28;01mlambda\u001b[39;00m metadata, children: ListWrapper(\u001b[38;5;28mlist\u001b[39m(children)),\n\u001b[0;32m     18\u001b[0m         namespace\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeras\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     19\u001b[0m     )\n\u001b[0;32m     22\u001b[0m \u001b[38;5;129m@keras_export\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeras.tree.is_nested\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_nested\u001b[39m(structure):\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tensorflow\\__init__.py:37\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msite\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_site\u001b[39;00m\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[1;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m module_util \u001b[38;5;28;01mas\u001b[39;00m _module_util\n\u001b[0;32m     38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlazy_loader\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KerasLazyLoader \u001b[38;5;28;01mas\u001b[39;00m _KerasLazyLoader\n\u001b[0;32m     40\u001b[0m \u001b[38;5;66;03m# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow.python'"
     ]
    }
   ],
   "source": [
    "#importing required packages\n",
    "import keras\n",
    "from keras.layers import Input, Dense\n",
    "from keras import regularizers\n",
    "#import tensorflow as tf\n",
    "from keras.models import Model\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow.python'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[61], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m  \u001b[38;5;66;03m# If not already imported, needed for storing results in a DataFrame\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m accuracy_score  \u001b[38;5;66;03m# For computing accuracy scores\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input, Dense  \u001b[38;5;66;03m# For building the autoencoder model\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Model  \u001b[38;5;66;03m# For creating the autoencoder model\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m regularizers  \u001b[38;5;66;03m# For specifying regularizers in the autoencoder\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\__init__.py:8\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _tf_keras\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m applications\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\_tf_keras\\__init__.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_tf_keras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m keras\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\_tf_keras\\keras\\__init__.py:8\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m callbacks\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\activations\\__init__.py:8\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"DO NOT EDIT.\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03mThis file was autogenerated. Do not edit it by hand,\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;124;03msince your modifications would be overwritten.\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deserialize\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m serialize\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\__init__.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m activations\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m applications\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\activations\\__init__.py:3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtypes\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m elu\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m exponential\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mactivations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m gelu\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\activations\\activations.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ops\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_export\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m keras_export\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\backend\\__init__.py:9\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;66;03m# When using the torch backend,\u001b[39;00m\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;66;03m# torch needs to be imported first, otherwise it will segfault\u001b[39;00m\n\u001b[0;32m      6\u001b[0m     \u001b[38;5;66;03m# upon import.\u001b[39;00m\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m result_type\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras_tensor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KerasTensor\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras_tensor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m any_symbolic_tensors\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\backend\\common\\__init__.py:2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_utils\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdtypes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m result_type\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvariables\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutocastScope\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvariables\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KerasVariable\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\backend\\common\\dtypes.py:5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_export\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m keras_export\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvariables\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m standardize_dtype\n\u001b[0;32m      7\u001b[0m BOOL_TYPES \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbool\u001b[39m\u001b[38;5;124m\"\u001b[39m,)\n\u001b[0;32m      8\u001b[0m INT_TYPES \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muint8\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     10\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muint16\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mint64\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     17\u001b[0m )\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\backend\\common\\variables.py:11\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstateless_scope\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m in_stateless_scope\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodule_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tensorflow \u001b[38;5;28;01mas\u001b[39;00m tf\n\u001b[1;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnaming\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m auto_name\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mKerasVariable\u001b[39;00m:\n\u001b[0;32m     15\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Represents a backend-agnostic variable in Keras.\u001b[39;00m\n\u001b[0;32m     16\u001b[0m \n\u001b[0;32m     17\u001b[0m \u001b[38;5;124;03m    A `Variable` acts as a container for state. It holds a tensor value and can\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     77\u001b[0m \u001b[38;5;124;03m    ```\u001b[39;00m\n\u001b[0;32m     78\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\utils\\__init__.py:12\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m enable_interactive_logging\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_interactive_logging_enabled\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_visualization\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m model_to_dot\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_visualization\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_model\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumerical_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m normalize\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\utils\\model_visualization.py:8\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi_export\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m keras_export\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m io_utils\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tree\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     11\u001b[0m     \u001b[38;5;66;03m# pydot-ng is a fork of pydot that is better maintained.\u001b[39;00m\n\u001b[0;32m     12\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpydot_ng\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpydot\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\src\\utils\\tree.py:12\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# Register backend-specific node classes\u001b[39;00m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtensorflow\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m---> 12\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrackable\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_structures\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ListWrapper\n\u001b[0;32m     14\u001b[0m     optree\u001b[38;5;241m.\u001b[39mregister_pytree_node(\n\u001b[0;32m     15\u001b[0m         ListWrapper,\n\u001b[0;32m     16\u001b[0m         \u001b[38;5;28;01mlambda\u001b[39;00m x: (x, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m     17\u001b[0m         \u001b[38;5;28;01mlambda\u001b[39;00m metadata, children: ListWrapper(\u001b[38;5;28mlist\u001b[39m(children)),\n\u001b[0;32m     18\u001b[0m         namespace\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeras\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     19\u001b[0m     )\n\u001b[0;32m     22\u001b[0m \u001b[38;5;129m@keras_export\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeras.tree.is_nested\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_nested\u001b[39m(structure):\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tensorflow\\__init__.py:37\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msite\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_site\u001b[39;00m\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[1;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m module_util \u001b[38;5;28;01mas\u001b[39;00m _module_util\n\u001b[0;32m     38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlazy_loader\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KerasLazyLoader \u001b[38;5;28;01mas\u001b[39;00m _KerasLazyLoader\n\u001b[0;32m     40\u001b[0m \u001b[38;5;66;03m# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow.python'"
     ]
    }
   ],
   "source": [
    "import numpy as np  # If not already imported assuming X_train and X_test are numpy arrays\n",
    "import pandas as pd  # If not already imported, needed for storing results in a DataFrame\n",
    "from sklearn.metrics import accuracy_score  # For computing accuracy scores\n",
    "from keras.layers import Input, Dense  # For building the autoencoder model\n",
    "from keras.models import Model  # For creating the autoencoder model\n",
    "from keras import regularizers  # For specifying regularizers in the autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "8Rca6ZpShnRN",
    "outputId": "1e8f7ecb-bbaa-4bd0-92dc-73a3ccf58fe2"
   },
   "outputs": [],
   "source": [
    "#building autoencoder model\n",
    "\n",
    "input_dim = X_train.shape[1]\n",
    "encoding_dim = input_dim\n",
    "\n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "encoder = Dense(encoding_dim, activation=\"relu\",\n",
    "                activity_regularizer=regularizers.l1(10e-4))(input_layer)\n",
    "encoder = Dense(int(encoding_dim), activation=\"relu\")(encoder)\n",
    "\n",
    "encoder = Dense(int(encoding_dim-2), activation=\"relu\")(encoder)\n",
    "code = Dense(int(encoding_dim-4), activation='relu')(encoder)\n",
    "decoder = Dense(int(encoding_dim-2), activation='relu')(code)\n",
    "\n",
    "decoder = Dense(int(encoding_dim), activation='relu')(encoder)\n",
    "decoder = Dense(input_dim, activation='relu')(decoder)\n",
    "autoencoder = Model(inputs=input_layer, outputs=decoder)\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367
    },
    "colab_type": "code",
    "id": "FFet6_03sBtK",
    "outputId": "bfc5a82f-b08b-453b-cd31-da9ee824480f",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#compiling the model\n",
    "autoencoder.compile(optimizer='adam',\n",
    "                    loss='binary_crossentropy',\n",
    "                    metrics=['accuracy'])\n",
    "\n",
    "#Training the model\n",
    "history = autoencoder.fit(X_train, X_train, epochs=50, batch_size=64, shuffle=True, validation_split=0.2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q1ONIVqSsXSP"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100
    },
    "colab_type": "code",
    "id": "TQa0u7vyj2Ml",
    "outputId": "73a8713c-683d-4a13-8619-5bd87adec0c8"
   },
   "outputs": [],
   "source": [
    "acc_train_auto = autoencoder.evaluate(X_train, X_train)[1]\n",
    "acc_test_auto = autoencoder.evaluate(X_test, X_test)[1]\n",
    "\n",
    "print('\\nAutoencoder: Accuracy on training Data: {:.3f}' .format(acc_train_auto))\n",
    "print('Autoencoder: Accuracy on test Data: {:.3f}' .format(acc_test_auto))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YG5oxuvJsflM"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xqjgA0aCsflO"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('AutoEncoder', acc_train_auto, acc_test_auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "plQeFh4fwB3a"
   },
   "source": [
    "### **7.6. Support Vector Machines**\n",
    "\n",
    "In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 83
    },
    "colab_type": "code",
    "id": "VBnV71QowZ07",
    "outputId": "258bc8b4-ba81-4036-c558-11923a7b0b5e"
   },
   "outputs": [],
   "source": [
    "#Support vector machine model\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# instantiate the model\n",
    "svm = SVC(kernel='linear', C=1.0, random_state=12)\n",
    "#fit the model\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Nlrfq-pwZ1G"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_svm = svm.predict(X_test)\n",
    "y_train_svm = svm.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Qb2FHrUpwZ1P"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "1ofewcVHwZ1R",
    "outputId": "3f83bfc7-f885-49a7-ed88-816e05097eac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM: Accuracy on training Data: 0.625\n",
      "SVM : Accuracy on test Data: 0.000\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_svm = accuracy_score(y_train,y_train_svm)\n",
    "acc_test_svm = accuracy_score(y_test,y_test_svm)\n",
    "\n",
    "print(\"SVM: Accuracy on training Data: {:.3f}\".format(acc_train_svm))\n",
    "print(\"SVM : Accuracy on test Data: {:.3f}\".format(acc_test_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ToF9fRNSwZ1b"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4ZAc00sUwZ1d"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('SVM', acc_train_svm, acc_test_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k3vsRppPv3rs"
   },
   "source": [
    "## **8. Comparision of Models**\n",
    "To compare the models performance, a dataframe is created. The columns of this dataframe are the lists created to store the results of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227
    },
    "colab_type": "code",
    "id": "RkOSzcfsv8Xl",
    "outputId": "82b2e437-b210-4b83-c3a0-dc9c5f65f9e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ML Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AutoEncoder</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ML Model  Train Accuracy  Test Accuracy\n",
       "0           Decision Tree           0.750            0.5\n",
       "1           Random Forest           0.750            0.0\n",
       "2  Multilayer Perceptrons           0.750            0.5\n",
       "3                 XGBoost           0.625            0.0\n",
       "4             AutoEncoder           0.250            0.0\n",
       "5                     SVM           0.625            0.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating dataframe\n",
    "results = pd.DataFrame({ 'ML Model': ML_Model,    \n",
    "    'Train Accuracy': acc_train,\n",
    "    'Test Accuracy': acc_test})\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227
    },
    "colab_type": "code",
    "id": "eKheGBiHwDfK",
    "outputId": "8ff038a3-9eea-472a-e1e7-ac6be45c9882"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ML Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AutoEncoder</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ML Model  Train Accuracy  Test Accuracy\n",
       "0           Decision Tree           0.750            0.5\n",
       "2  Multilayer Perceptrons           0.750            0.5\n",
       "1           Random Forest           0.750            0.0\n",
       "3                 XGBoost           0.625            0.0\n",
       "5                     SVM           0.625            0.0\n",
       "4             AutoEncoder           0.250            0.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sorting the datafram on accuracy\n",
    "results.sort_values(by=['Test Accuracy', 'Train Accuracy'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5t9806vn601b"
   },
   "source": [
    "For the above comparision, it is clear that the XGBoost Classifier works well with this dataset.\n",
    "\n",
    "So, saving the model for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aCIIkZ7V3AFN"
   },
   "outputs": [],
   "source": [
    "# save XGBoost model to file\n",
    "import pickle\n",
    "pickle.dump(xgb, open(\"XGBoostClassifier.pickle.dat\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PbrNHP0o3QrD"
   },
   "source": [
    "**Testing the saved model:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "colab_type": "code",
    "id": "-ZEm_PS33QD-",
    "outputId": "a4195d7f-94ef-4bc7-a165-35ed2ed5493f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load model from file\n",
    "loaded_model = pickle.load(open(\"XGBoostClassifier.pickle.dat\", \"rb\"))\n",
    "loaded_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3vy2egEdwkqZ"
   },
   "source": [
    "## **9. References**\n",
    "* https://blog.keras.io/building-autoencoders-in-keras.html\n",
    "* https://en.wikipedia.org/wiki/Autoencoder\n",
    "* https://mc.ai/a-beginners-guide-to-build-stacked-autoencoder-and-tying-weights-with-it/\n",
    "* https://github.com/shreyagopal/t81_558_deep_learning/blob/master/t81_558_class_14_03_anomaly.ipynb\n",
    "* https://machinelearningmastery.com/save-gradient-boosting-models-xgboost-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'MLPClassifier' object has no attribute 'save'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[51], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential  \u001b[38;5;66;03m# For defining the model architecture\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dense  \u001b[38;5;66;03m# For adding layers to the model\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[43mmlp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMLP.h5\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'MLPClassifier' object has no attribute 'save'"
     ]
    }
   ],
   "source": [
    "# Save the Multilayer Perceptrons model as an h5 file\n",
    "from keras.models import Sequential  # For defining the model architecture\n",
    "from keras.layers import Dense  # For adding layers to the model\n",
    "\n",
    "mlp.save('MLP.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\23820\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:88: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 614ms/step - accuracy: 0.0000e+00 - loss: 2.3053\n",
      "Epoch 2/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.0000e+00 - loss: 2.2840\n",
      "Epoch 3/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.0000e+00 - loss: 2.2640\n",
      "Epoch 4/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.0000e+00 - loss: 2.2444\n",
      "Epoch 5/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.0000e+00 - loss: 2.2255\n",
      "Epoch 6/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.0000e+00 - loss: 2.2074\n",
      "Epoch 7/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.0000e+00 - loss: 2.1900\n",
      "Epoch 8/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.0000e+00 - loss: 2.1731\n",
      "Epoch 9/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.0000e+00 - loss: 2.1567\n",
      "Epoch 10/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 2.1417\n",
      "Epoch 11/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.0000e+00 - loss: 2.1274\n",
      "Epoch 12/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.0000e+00 - loss: 2.1138\n",
      "Epoch 13/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 2.1005\n",
      "Epoch 14/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 2.0875\n",
      "Epoch 15/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.0000e+00 - loss: 2.0751\n",
      "Epoch 16/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.0000e+00 - loss: 2.0637\n",
      "Epoch 17/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.0000e+00 - loss: 2.0524\n",
      "Epoch 18/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.0000e+00 - loss: 2.0412\n",
      "Epoch 19/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.0000e+00 - loss: 2.0308\n",
      "Epoch 20/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.0000e+00 - loss: 2.0223\n",
      "Epoch 21/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 2.0160\n",
      "Epoch 22/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.0000e+00 - loss: 2.0103\n",
      "Epoch 23/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.0000e+00 - loss: 2.0043\n",
      "Epoch 24/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 1.9983\n",
      "Epoch 25/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.0000e+00 - loss: 1.9919\n",
      "Epoch 26/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.0000e+00 - loss: 1.9854\n",
      "Epoch 27/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.0000e+00 - loss: 1.9787\n",
      "Epoch 28/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.0000e+00 - loss: 1.9721\n",
      "Epoch 29/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.0000e+00 - loss: 1.9654\n",
      "Epoch 30/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.0000e+00 - loss: 1.9585\n",
      "Epoch 31/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 1.9515\n",
      "Epoch 32/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 1.9443\n",
      "Epoch 33/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 1.9370\n",
      "Epoch 34/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 1.9295\n",
      "Epoch 35/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 1.9218\n",
      "Epoch 36/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.0000e+00 - loss: 1.9139\n",
      "Epoch 37/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.0000e+00 - loss: 1.9059\n",
      "Epoch 38/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.0000e+00 - loss: 1.8977\n",
      "Epoch 39/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.1250 - loss: 1.8893\n",
      "Epoch 40/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.2500 - loss: 1.8806\n",
      "Epoch 41/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.2500 - loss: 1.8718\n",
      "Epoch 42/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.2500 - loss: 1.8629\n",
      "Epoch 43/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.2500 - loss: 1.8537\n",
      "Epoch 44/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.2500 - loss: 1.8444\n",
      "Epoch 45/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.2500 - loss: 1.8350\n",
      "Epoch 46/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.2500 - loss: 1.8255\n",
      "Epoch 47/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - accuracy: 0.3750 - loss: 1.8157\n",
      "Epoch 48/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.5000 - loss: 1.8059\n",
      "Epoch 49/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.6250 - loss: 1.7958\n",
      "Epoch 50/50\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.6250 - loss: 1.7857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import save_model\n",
    "\n",
    "# Reshaping the y_train\n",
    "y_train_binary = tf.keras.utils.to_categorical(y_train, num_classes=8)\n",
    "\n",
    "# Instantiate the MLPClassifier\n",
    "tf_mlp = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(50, input_shape=(X_train.shape[1],), activation='relu'),\n",
    "    tf.keras.layers.Dense(8, activation='relu'),\n",
    "    tf.keras.layers.Dense(8, activation='softmax')\n",
    "])\n",
    "\n",
    "tf_mlp.compile(optimizer='adam',\n",
    "                loss='categorical_crossentropy',\n",
    "                metrics=['accuracy'])\n",
    "\n",
    "tf_mlp.fit(X_train, y_train_binary, epochs=50, batch_size=64, shuffle= True)\n",
    "\n",
    "# Save the TensorFlow MLPClassifier as an HDF5 file\n",
    "save_model(tf_mlp, 'MLP.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model as pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the trained model to a file\n",
    "with open('mlp_model.pkl', 'wb') as file:\n",
    "    pickle.dump(mlp, file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.Domain of the URL (Domain) \n",
    "import re\n",
    "\n",
    "def get_domain(url):  \n",
    "  domain = urlparse(url).netloc\n",
    "  if re.match(r\"^www.\",domain):\n",
    "\t       domain = domain.replace(\"www.\",\"\")\n",
    "  return domain\n",
    "\n",
    "# 2.Checks for IP address in URL (Have_IP)\n",
    "def having_ip(url):\n",
    "  try:\n",
    "    ipaddress.ip_address(url)\n",
    "    ip = 1\n",
    "  except:\n",
    "    ip = 0\n",
    "  return ip\n",
    "# 3.Checks the presence of @ in URL (Have_At)\n",
    "def have_at_sign(url):\n",
    "  if \"@\" in url:\n",
    "    at = 1    \n",
    "  else:\n",
    "    at = 0    \n",
    "  return at\n",
    "\n",
    "# 4.Finding the length of URL and categorizing (URL_Length)\n",
    "def get_length(url):\n",
    "  if len(url) < 54:\n",
    "    length = 0            \n",
    "  else:\n",
    "    length = 1            \n",
    "  return length\n",
    "# 5.Gives number of '/' in URL (URL_Depth)\n",
    "def get_depth(url):\n",
    "  s = urlparse(url).path.split('/')\n",
    "  depth = 0\n",
    "  for j in range(len(s)):\n",
    "    if len(s[j]) != 0:\n",
    "      depth = depth+1\n",
    "  return depth\n",
    "# 6.Checking for redirection '//' in the url (Redirection)\n",
    "def redirection(url):\n",
    "  pos = url.rfind('//')\n",
    "  if pos > 6:\n",
    "    if pos > 7:\n",
    "      return 1\n",
    "    else:\n",
    "      return 0\n",
    "  else:\n",
    "    return 0\n",
    "# 7.Existence of “HTTPS” Token in the Domain Part of the URL (https_Domain)\n",
    "def http_domain(url):\n",
    "  domain = urlparse(url).netloc\n",
    "  if 'https' in domain:\n",
    "    return 1\n",
    "  else:\n",
    "    return 0\n",
    "# 8. Checking for Shortening Services in URL (Tiny_URL)\n",
    "def tiny_url(url):#listing shortening services\n",
    "    shortening_services = r\"bit\\.ly|goo\\.gl|shorte\\.st|go2l\\.ink|x\\.co|ow\\.ly|t\\.co|tinyurl|tr\\.im|is\\.gd|cli\\.gs|\" \\\n",
    "                      r\"yfrog\\.com|migre\\.me|ff\\.im|tiny\\.cc|url4\\.eu|twit\\.ac|su\\.pr|twurl\\.nl|snipurl\\.com|\" \\\n",
    "                      r\"short\\.to|BudURL\\.com|ping\\.fm|post\\.ly|Just\\.as|bkite\\.com|snipr\\.com|fic\\.kr|loopt\\.us|\" \\\n",
    "                      r\"doiop\\.com|short\\.ie|kl\\.am|wp\\.me|rubyurl\\.com|om\\.ly|to\\.ly|bit\\.do|t\\.co|lnkd\\.in|db\\.tt|\" \\\n",
    "                      r\"qr\\.ae|adf\\.ly|goo\\.gl|bitly\\.com|cur\\.lv|tinyurl\\.com|ow\\.ly|bit\\.ly|ity\\.im|q\\.gs|is\\.gd|\" \\\n",
    "                      r\"po\\.st|bc\\.vc|twitthis\\.com|u\\.to|j\\.mp|buzurl\\.com|cutt\\.us|u\\.bb|yourls\\.org|x\\.co|\" \\\n",
    "                      r\"prettylinkpro\\.com|scrnch\\.me|filoops\\.info|vzturl\\.com|qr\\.net|1url\\.com|tweez\\.me|v\\.gd|\" \\\n",
    "                      r\"tr\\.im|link\\.zip\\.net\"\n",
    "    match=re.search(shortening_services,url)\n",
    "    if match:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "# 9.Checking for Prefix or Suffix Separated by (-) in the Domain (Prefix/Suffix)\n",
    "def prefix_suffix(url):\n",
    "    if '-' in urlparse(url).netloc:\n",
    "        return 1            \n",
    "    else:\n",
    "        return 0 \n",
    "import requests\n",
    "\n",
    "def web_traffic(url):\n",
    "    try:\n",
    "        querystring = {\"domain\": url}\n",
    "        headers = {\n",
    "            \"X-RapidAPI-Key\": \"cd4733fedbmsh6f2cfc21cf195f2p1d088djsn84e6c824c74e\",\n",
    "            \"X-RapidAPI-Host\": \"similar-web.p.rapidapi.com\"\n",
    "        }\n",
    "        response = requests.get(\"https://similar-web.p.rapidapi.com/get-analysis\", headers=headers, params=querystring)\n",
    "        data = response.json()\n",
    "        rank = data['GlobalRank']['Rank']\n",
    "        rank = int(rank)\n",
    "    except (requests.exceptions.RequestException, ValueError, KeyError):\n",
    "        rank = 1\n",
    "\n",
    "    if rank < 100000:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "# 15. IFrame Redirection (iFrame)\n",
    "def iframe(response):\n",
    "  if response == \"\":\n",
    "      return 1\n",
    "  else:\n",
    "      if re.findall(r\"[<iframe>|<frameBorder>]\", response.text):\n",
    "          return 0\n",
    "      else:\n",
    "          return 1\n",
    "\n",
    "# 16.Checks the effect of mouse over on status bar (Mouse_Over)\n",
    "def mouse_over(response): \n",
    "  if response == \"\" :\n",
    "    return 1\n",
    "  else:\n",
    "    if re.findall(\"<script>.+onmouseover.+</script>\", response.text):\n",
    "      return 1\n",
    "    else:\n",
    "      return 0\n",
    "# 17.Checks the status of the right click attribute (Right_Click)\n",
    "def right_click(response):\n",
    "  if response == \"\":\n",
    "    return 1\n",
    "  else:\n",
    "    if re.findall(r\"event.button ?== ?2\", response.text):\n",
    "      return 0\n",
    "    else:\n",
    "      return 1\n",
    "# 18.Checks the number of forwardings (Web_Forwards)    \n",
    "def forwarding(response):\n",
    "  if response == \"\":\n",
    "    return 1\n",
    "  else:\n",
    "    if len(response.history) <= 2:\n",
    "      return 0\n",
    "    else:\n",
    "      return 1\n",
    "# Function to handle HTTP request with retries\n",
    "def get_http_response(url):\n",
    "    try:\n",
    "        response = requests.get(url, timeout=5)  # Set a timeout of 5 seconds\n",
    "        return response\n",
    "    except ConnectionError as e:\n",
    "        print(f\"ConnectionError: Failed to make HTTP request to {url}. Exception: {e}\")\n",
    "        return None\n",
    "    except SSLError as e:\n",
    "        print(f\"SSLError occurred while fetching URL: {url}. Exception: {e}\")\n",
    "        return None\n",
    "    except Timeout as e:\n",
    "        print(f\"Timeout: Request timed out while making HTTP request to {url}. Exception: {e}\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import ast\n",
    "from urllib.parse import urlparse\n",
    "from datetime import datetime\n",
    "\n",
    "def extract_features(url, domain_name=None):\n",
    "    features = []\n",
    "\n",
    "\n",
    "    \n",
    "    # Address bar based features\n",
    "    features.append(having_ip(url))\n",
    "    features.append(have_at_sign(url))\n",
    "    features.append(get_length(url))\n",
    "    features.append(get_depth(url))\n",
    "    features.append(redirection(url))\n",
    "    features.append(http_domain(url))\n",
    "    features.append(tiny_url(url))\n",
    "    features.append(prefix_suffix(url))\n",
    "\n",
    "    # Domain based features\n",
    "    dns = 0\n",
    "    dns_age = 0\n",
    "    dns_end = 0\n",
    "    features.append(dns)\n",
    "    features.append(dns_age)\n",
    "    features.append(dns_end)\n",
    "    features.append(web_traffic(url))\n",
    "    response = get_http_response(url)\n",
    "\n",
    "    # HTML & Javascript based features\n",
    "    if response is not None:\n",
    "        features.append(iframe(response))\n",
    "        features.append(mouse_over(response))\n",
    "        features.append(right_click(response))\n",
    "        features.append(forwarding(response))\n",
    "    else:\n",
    "        # If response is None, set these features to 0 or None\n",
    "        features.extend([0, 0, 0, 0])\n",
    "\n",
    "    return features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timeout: Request timed out while making HTTP request to http://1337x.to/torrent/1048648/American-Sniper-2014-MD-iTALiAN-DVDSCR-X264-BST-MT/. Exception: HTTPConnectionPool(host='1337x.to', port=80): Max retries exceeded with url: /torrent/1048648/American-Sniper-2014-MD-iTALiAN-DVDSCR-X264-BST-MT/ (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x0000029219E72020>, 'Connection to 1337x.to timed out. (connect timeout=5)'))\n",
      "[[0 0 1 3 0 0 0 0 0 0 0 1 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from requests.exceptions import SSLError, Timeout  # Add this import for SSLError and Timeout\n",
    "\n",
    "# Load the model from the file\n",
    "with open('mlp_model.pkl', 'rb') as file:\n",
    "    loaded_model = pickle.load(file)\n",
    "\n",
    "# Assuming you have new data in a variable called 'new_data'\n",
    "# Make predictions on the new data\n",
    "new_data = extract_features(\"http://1337x.to/torrent/1048648/American-Sniper-2014-MD-iTALiAN-DVDSCR-X264-BST-MT/\")\n",
    "new_data = [new_data]\n",
    "new_data = np.array(new_data)\n",
    "print(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudha\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but MLPClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "predictions = loaded_model.predict(new_data)\n",
    "\n",
    "# 'predictions' now contains the predicted labels for the new data\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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