TSF - Task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author : Alina Sahoo\n",
    "## Technical TASK 1 :- Prediction using Supervised ML (Level - Beginner)\n",
    "\n",
    "In this task, we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all libraries required in this notebook\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 :- Reading Data from online source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours  Scores\n",
       "0     2.5      21\n",
       "1     5.1      47\n",
       "2     3.2      27\n",
       "3     8.5      75\n",
       "4     3.5      30\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Reading data from remote link\n",
    "data = pd.read_csv('http://bit.ly/w-data')\n",
    "data.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot our data points on 2-D graph to eyeball our dataset and see if we can manually find any relationship between the data. We can create the plot with the following script."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 : Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of scores\n",
    "data.plot(x='Hours', y='Scores', style='o')\n",
    "plt.title('Hours vs Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 :- Preparing The Data\n",
    "The next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:, :-1].values\n",
    "y = data.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 :- Algorithm Training\n",
    "Splitting the data into training data-set and test data-set. Then, start training the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state = 0)\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train.reshape(-1,1), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Done!!\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Done!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5 :- Ploting the line of regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAal0lEQVR4nO3df3CV1b3v8feXH5aAtlhADKGKrZTCSHuh1FIZGIsoap0W6dj2MqhjmWLv6Ln2XIdT1Hbsbf8Ay6lz+lNLtYq1xXIR0ZajQKH1yKnCCaDECg5yUCEEAYViIEAg3/tHdtI8e+8ke+88ez/Ps/fnNeOQvUiyv8Pgh2/WWs9a5u6IiEjy9Iq6ABERKYwCXEQkoRTgIiIJpQAXEUkoBbiISEL1KeWbDR482EeMGFHKtxQRSbzNmzcfcvch6eMlDfARI0ZQW1tbyrcUEUk8M3sr27imUEREEkoBLiKSUApwEZGEUoCLiCSUAlxEJKFKugtFRCTpVm6tZ9Hq19l3pIlhA6uYN30UM8bVRFKLAlxEJEcrt9Zz14o6mprPAFB/pIm7VtQBRBLimkIREcnRotWvt4d3m6bmMyxa/Xok9SjARURytO9IU17jxaYAFxHJ0bCBVXmNF5sCXEQkR/Omj6Kqb+/AWFXf3sybPiqSehTgIiI5mjGuhgUzx1IzsAoDagZWsWDm2G4XMPft21eUerQLRUQkDzPG1eS846Suro5PfvKTAKxevZqrrroq1FrUgYuIhKylpYUpU6a0h3evXr2YNm1a6O+jABcRCdGzzz5L7969eeGFFwBYsWIFZ86coVev8ONWUygiIiE4fvw4Q4cOpbGxEYDx48ezadMmevfu3c1XFk4duIhID/3kJz9hwIAB7eFdW1vL5s2bixreoA5cRKRg+/bto6bmHwuat9xyC7/+9a9L9v7qwEVECjB37txAeO/Zs6ek4Q0KcBGRvDzyyCOYGb/61a8AuP/++3F3hg8fXvJaNIUiIpKD5uZmzjrrrMBYY2MjAwYMiKgideAiIt2aM2dOILznzp2Lu0ca3qAOXESkU/v376e6ujowdurUKfr27Zvz9yjmBRDqwEVEsqiurg6E95IlS3D3vMP7rhV11B9pwvnHBRArt9aHUqM6cBGRDl588UUuu+yywJi7F/S9uroAIowuXAEuIpJiZoHXmzdvZvz48QV/v2JfAKEpFBGpeA8++GAgvEeNGoW79yi8ofgXQKgDF5HYKPWN7ydPnqRfv36BsUOHDjFo0KBQvv+86aMClyBDuBdAqAMXkVgo9oJfusGDBwfC+4477sDdQwtvKPwCiFypAxeRyHTsuHuZcSZtsTDMBb82O3bsYPTo0YGx06dPF+3gqXwugMiXOnARiUR6x50e3m3CvPHdzALh/Z3vfAd3L/qpgcWiDlxEIpFti102YSz4/eY3v+Gmm24KjBW6NTBOFOAiEolcOuueLvi5e8ZNOOvWrWPq1KkFf884UYCLSCSGDayiPkuI9zajxb3Hu1Auvvhidu3aFRgrh667IwW4iESisy12Pd2lceTIEc4999zAWENDA+eff37B3zOuFOAiEom2kA5z33f6k5RQfl13RwpwEYlMWFvsXnjhBaZMmRIYa25upk+f8o44bSMUkUQzs0B433TTTbh72Yc3KMBFJKHuvvvujCkTd2fJkiURVVR65f9PlIiUlWxbA5966ilmzJgRGCv1uSpRUICLSGIMHjyYd999NzCWbZGy7SnPth0ubeeqAGUV4jlNoZjZP5vZ38zsVTNbamb9zOzDZrbWzHamfj23++8kIpK/gwcPYmaB8N67d2+nO0y6ukihnHQb4GZWA/xvYIK7XwL0Br4GzAfWuftIYF3qtYhIqMyM8847LzDm7tTUdN5JF/sihbjIdRGzD1BlZn2A/sA+4EtA22rBEmBGJ18rIpK3tWvXZixSnjlzJqd93cW+SCEuug1wd68H/hV4G2gA/u7ua4Ch7t6Q+pwG4LxsX29mc82s1sxqDx48GF7lIlK2zIyrrrqq/fVtt92WdfGyM/Omj6Kqb/CEwTAvUoiLXKZQzqW1274IGAYMMLPZub6Buy929wnuPmHIkCGFVyoiZW/27NlZtwb+7Gc/y+v7FPsihbjIZRfKNGC3ux8EMLMVwGXAO2ZW7e4NZlYNHChinSJSxlpaWjLO5F69enWgC89XMS9SiItcAvxtYKKZ9QeagCuAWuAYcDOwMPXr08UqUkTKV6WdXxKmXObANwLLgS1AXeprFtMa3Fea2U7gytRrEZGc7Nq1KyO8GxoaFN55yOlBHne/F7g3bfgkrd24iEhe1HWHQ2ehiEjJ/OIXv8gI75aWFoV3gfQovYiU5NyQ9OCeNm0aa9euDfU9Ko0CXKTCFfvckI9//OPs3LkzMKaOOxyaQhGpcMU6N6S5uRkzC4T3smXLYhHeK7fWM2nhei6av4pJC9ezcmt91CUVRB24SIUrxrkhcV6kLKeTCtWBi1S4MM8N2bJlS0Z479+/PzbhDeV1UqECXKTChXVuiJnx6U9/OjDm7gwdOrTHNYapnE4qVICLVLienhty5513JmprYDmdVKg5cBEp+NyQ9OC+9NJL2bhxY1hlFcW86aMCc+CQ3JMKFeAikrc4L1J2p+0fqnK4L1MBLiI5a2pqon///oGxJ554gq9+9asRVVSYcjmpUAEuIjlJctddrrSIKSJd2rBhQ0Z4Hzx4UOEdA+rARaRT6rrjTR24iGSYM2dO1qvNFN7xog5cRALSg/vaa69l1apVEVUjXVGAiwig6ZIk0hSKSIV7//33M8L7j3/8o8I7AdSBi1SwYnTdpbgcQlqpAxepQGvWrMkI7yNHjoQS3netqKP+SBPOP45qTep523GnDlykwhRzrruro1rVhYdPHbhIhbj++uuLvjWwnI5qTQIFuEgFMDNWrlzZ/nrWrFlFWaQsp6Nak0BTKCIxEvYCYKm3BpbTUa1JoA5cJCbCXAB87733MsL7z3/+c9G3Bvb0cgjJjzpwkZgIawEw6gdyyuWo1iRQBy4SEz1dAFyxYkVGeB87dkwP5JQxdeAiMTFsYBX1WcI6lwXAqLtuiYY6cJGYKOR2+Msvv1ynBlYwBbhITOSzAOjumBnPP/98+9jtt9+u4K4wmkIRiZFcFgA1XSJt1IGLJMT+/fszwnvTpk0K7wqmDlwkAdR1SzbqwEVi7LHHHssI75MnTyq8BVAHLhJb6rqlOwpwkRLr7ryTsWPH8uqrrwa+RsEt2WgKRaSEujrvpG1rYMfwvueeexTe0il14CIl1Nl5J9ePH57xuQpu6Y46cJESSj/X5PTfD/DWfdcFxurq6hTekpOcOnAzGwg8BFwCOPB14HXg98AI4E3gK+5+uChVipSJjuedpAc3qOuW/OTagf8YeM7dPwF8CtgOzAfWuftIYF3qtUhFWLm1nkkL13PR/FVMWrg+5zO7500fxYmXV2WE9/JNbyq8JW/W3V8aM/sg8ArwUe/wyWb2OnC5uzeYWTXwF3fv8tqNCRMmeG1tbQhli0SnbSEy/daZXC4uSN8a2KtvP57c+IbOz5Yumdlmd5+QPp5LB/5R4CDwiJltNbOHzGwAMNTdGwBSv54XasUiMdXVxQudqa6uznpq4JlTTQpvKVguAd4HGA884O7jgGPkMV1iZnPNrNbMag8ePFhgmSLxkc/FCy0tLZgZ+/fvbx9btGiRpkskFLksYu4F9rr7xtTr5bQG+DtmVt1hCuVAti9298XAYmidQgmhZpFI5Xrxgp6klGLrtgN39/3AHjNrm9++AngNeAa4OTV2M/B0USoUiZnuLl54/fXXM8J7586dCm8JXa4P8vwT8FszOwv4b+AWWsN/mZnNAd4GbihOiSLx0jZnne1xeHXdUko5Bbi7vwxkrIDS2o2LVJz0ixcWLFjA9ePvDnzOmTNn6NVLz8pJ8ehRepEeSu+6R4wYwe7duyOqRiqJAlykQGeddRbNzc2BMU2XSCnp5zuRPJ0+fRozC4T3gw8+qPCWklMHLpIHLVJKnKgDF8nBjh07MsK7oaFB4S2RUgcu0g113RJX6sClbBR6QmBnfvSjH2WEd0tLi8JbYkMduJSF9BMC264qAwo6LCo9uGfOnMmTTz7Z80JFQqQAl7LQ1QmB+QR4TU0N+/btC4yp45a40hSKlIV8TgjM5tSpU5hZILz/8Ic/KLwl1tSBS1nI9YTAbLRIKUmlDlzKQncnBGbz0ksvZYT3oUOHFN6SGOrApSx0dUJgNuq6pRwowKVspJ8QmM03v/lNfvnLXwbGFNySVApwqRjpXfeVV17JmjVrIqpGpOcU4FL2NF0i5UqLmFK2GhsbM8L76aefVnhL2VAHLmVJXbdUAnXgUlbWrl2bEd6HDx9WeEtZUgcuZUNdt1QadeCSeJMnT84Ib3dXeEvZU4BLopkZGzZsaH89efJkBbdUDE2hSCJpukREHbgkzLvvvpsR3s8880ze4R325Q8iUVAHLokRVtcd9uUPIlFRBy6x97vf/S4jvI8ePVrwlElXlz+IJIk6cIm1Ysx19/TyB5G4UAcusTRmzJiibQ3s7JKHXC5/EIkTBbjEjpmxffv29tczZswIdYdJIZc/iMSRplAkNkq1NTDfyx9E4koBLpFraGhg2LBhgbH169fz+c9/vmjvmcvlDyJxpwCXSOmBHJHCaQ5cIvHAAw9khPfx48cV3iJ5UAcuJaeuWyQc6sClZAYNGqRTA0VCpACvIFGd/+HumBnvvfde+9jXv/51BbdID2kKpUJEdf6HpktEikcdeIUo9fkfu3fvzgjvjRs36tRAkRCpA68QpTz/Q6cGipSGOvAKUYrzP+67776M8D516pRODRQpkpw7cDPrDdQC9e5+nZl9GPg9MAJ4E/iKux8uRpHSc/Omjwp0sxDu+R86NVCk9PLpwO8Atnd4PR9Y5+4jgXWp1xJTM8bVsGDmWGoGVmFAzcAqFswc2+OpCDPTqYEiEckpwM1sOPAF4KEOw18ClqQ+XgLMCLc0CduMcTX85/yp7F74Bf5z/tQehXdLS0tGcN955506NVCkhHKdQvk34F+AczqMDXX3BgB3bzCz87J9oZnNBeYCXHDBBT0oVeJCpwaKxEO3AW5m1wEH3H2zmV2e7xu4+2JgMcCECRO0ATjB3njjDUaOHBkYq6ur45JLLinae+rUQJHO5dKBTwK+aGbXAv2AD5rZ48A7Zlad6r6rgQPFLFSipQdyROKn2zlwd7/L3Ye7+wjga8B6d58NPAPcnPq0m4Gni1alRGbRokUZ4X3mzBmFt0gM9ORBnoXAMjObA7wN3BBOSRIX6cFdXV3Nvn37IqpGRNLlFeDu/hfgL6mP3wWuCL8kiVr//v1pagrutVbHLRI/ehJT2p0+fRozC4T3z3/+c4W3SEzpLBQBtEgpkkTqwCtcXV1dRni/9dZbCm+RBFAHXsF60nWv3FqvB2xEIqYOvALdc889GeHd0tKSV3jftaKO+iNNOP845lVndYuUljrwCpMe3GPHjmXbtm15fY+ujnlVFy5SOgrwChHmIqWOeRWJB02hlLmTJ09mhPeSJUt6tEipY15F4kEdeBkr1tbAYl8OISK5UQdehrZt25YR3g0NDaFtDSzW5RAikh914GWmlGd1K7BFoqUOvEz84Ac/KNrVZiIST+rAy0B6cN9444089thjEVUjIqWiAE+ws88+m2PHjgXG1HGLVA5NoSRQU1MTZhYI7zVr1ii8RSqMOvAIdHWOSHdnjBRrkVJnm4gkjwK8xNrOEWnbQ912jkibzn6v+tReJk6cGPheR44c4UMf+lBRa1KIi8SXArzEujpHpO3j9N+7fvzwjO8T5nSJzjYRSSYFeInlc47I4fUPc/S/ngqMFWOeW2ebiCSTFjFLrKtzRDr+3lv3XRcI79tvv71oi5Q620QkmRTgJTZv+iiq+vYOjLWdIzJv+ije+8MPeeu+6wK//9SWvfz0pz+NpCYRiS9NoZRY25xy+o6Pq0cPoqoq2PFecuuP+cGtXy76PHRnNWn+WyTerJR7hydMmOC1tbUle7+k0NZAEemKmW129wnp45pCidD27dszwvvEiROhhbeuPRMpbwrwiJgZY8aMaX89e/Zs3J0PfOADoXz/7rYrikjyaQ68xJYvX84NN9wQGNPWQBEphDrwEjKzQHgvW7ZMWwNFpGAK8BL4xje+kfWs7vROHFrnrictXM9F81cxaeH6guestTVQpPxpCqWIjh8/zoABAwJje/bsYfjwzEfjIdwzSbQ1UKT8KcA70dMteFVVVZw4caL99Sc+8Qm2b9/e5deEfSaJrj0TKW8K8Cx60gnv2LGD0aNHB8aam5vp06f7P2otPIpIPjQHnkWhW/DMLBDe3/3ud3H3nMIbtPAoIvlRgGeRbyf8+OOPZ12k/P73v5/X+2rhUUTyoSmULIYNrKI+S1ind8LuTq9ewX8D161bx9SpUwt6Xy08ikg+FOBZzJs+KjAHDpmd8KxZs1i6dGng68LY062FRxHJlQI8i6464cbGRs4555zA5zc0NHD++edHUaqIVDAFeCeydcLp89yf+cxn2LRpU97fW6cEikgYFOA52LZtG5/61KcCY6dPn6Z3796dfEXndIGwiIRFu1C6YWaB8F6wYAHuXlB4g04JFJHwdNuBm9lHgMeA84EWYLG7/9jMPgz8HhgBvAl8xd0PF6/U/PVkqmLdunVMmzYtMBbGIqUe1hGRsOTSgZ8G7nT30cBE4DYzGwPMB9a5+0hgXep1bBR6oYG7Y2aB8N6wYUNopwbqYR0RCUu3Ae7uDe6+JfXx+8B2oAb4ErAk9WlLgBnFKrIQhUxV/PCHPwzs6548eTLuzqRJk0KrSw/riEhY8lrENLMRwDhgIzDU3RugNeTN7LxOvmYuMBfgggsu6EmteclnqiLbqYFHjx7N2C4YBj2sIyJhyTnAzexs4EngW+5+NNtFvNm4+2JgMbRealxIkYXI9WnKa665hueee6799fe+9z3uvffeotamh3VEJAw5BbiZ9aU1vH/r7itSw++YWXWq+64GDhSryEJ09zTlrl27uPjiiwNf09LSkvWGeBGROOp2DtxaE+1hYLu739/ht54Bbk59fDPwdPjlFW7GuBoWzBxLzcAqDKgZWMWCmWOZMa4GMwuE96pVq9oXL0VEkiKXDnwScCNQZ2Yvp8buBhYCy8xsDvA2kHk/WAh6shUwfari2WefxSx4G06x7qQUESm2bgPc3TcAnbWmV4RbTlBYTy1mOzXwjTfe4GMf+1h4xYqIlFisn8QM46nFRx99NBDe06dPx90V3iKSeLE+C6UnTy2ePHmSkSNHsmfPnvaxxsbGjO2CIiJJFesOvNCnFh999FH69evXHt7PP/887q7wFpGyEusAz/epxUOHDmFm3HLLLQB8+ctfpqWlhSlTphS9VhGRUot1gHe1FTDdt7/9bYYMGdL+eteuXSxfvlxbA0WkbMV6Dhy6f2pxx44dgZvgS/EkpYhIHMQ+wDvj7lx99dWsWbOmfezw4cMMHDgwwqpEREon1lMonfnTn/5Er1692sN76dKluLvCW0QqSqI68BMnTnDhhRdy4EDrsSujR4/mlVdeoW/fvhFXJiJSeonpwBcvXkxVVVV7eL/44ou89tprCm8RqViJCPBHHnmEW2+9FYBZs2bR0tLCxIkTI65KRCRaiZhCGTNmDJ/73OdYunQpF154YdTliIjEQiIC/LOf/Sx//etfoy5DRCRWEjGFIiIimRTgIiIJpQAXEUkoBbiISEIpwEVEEkoBLiKSUApwEZGEUoCLiCSUuXvp3szsIPBWjp8+GDhUxHIKpbpyF8eaIJ51xbEmiGddcawJilvXhe4+JH2wpAGeDzOrdfcJUdeRTnXlLo41QTzrimNNEM+64lgTRFOXplBERBJKAS4iklBxDvDFURfQCdWVuzjWBPGsK441QTzrimNNEEFdsZ0DFxGRrsW5AxcRkS4owEVEEip2AW5mvzazA2b2atS1dGRmHzGzP5vZdjP7m5ndEYOa+pnZJjN7JVXT/426pjZm1tvMtprZH6OupY2ZvWlmdWb2spnVRl1PGzMbaGbLzWxH6u/X5yKuZ1Tqz6jtv6Nm9q0oa2pjZv+c+rv+qpktNbN+MajpjlQ9fyv1n1Ps5sDNbArQCDzm7pdEXU8bM6sGqt19i5mdA2wGZrj7axHWZMAAd280s77ABuAOd38pqpramNn/ASYAH3T366KuB1oDHJjg7rF6CMTMlgAvuPtDZnYW0N/dj0RdF7T+QwzUA59191wfwitWLTW0/h0f4+5NZrYM+Hd3fzTCmi4BngAuBU4BzwH/y913luL9Y9eBu/t/AO9FXUc6d29w9y2pj98HtgM1Edfk7t6Yetk39V/k/yKb2XDgC8BDUdcSd2b2QWAK8DCAu5+KS3inXAHsijq8O+gDVJlZH6A/sC/iekYDL7n7cXc/DTwPXF+qN49dgCeBmY0AxgEbo62kfariZeAAsNbdI68J+DfgX4CWqAtJ48AaM9tsZnOjLiblo8BB4JHUlNNDZjYg6qI6+BqwNOoiANy9HvhX4G2gAfi7u6+JtipeBaaY2SAz6w9cC3ykVG+uAM+TmZ0NPAl8y92PRl2Pu59x9/8BDAcuTf1IFxkzuw444O6bo6yjE5PcfTxwDXBbarouan2A8cAD7j4OOAbMj7akVqnpnC8C/y/qWgDM7FzgS8BFwDBggJnNjrImd98O3AespXX65BXgdKneXwGeh9Q885PAb919RdT1dJT6sfsvwNURlzIJ+GJqvvkJYKqZPR5tSa3cfV/q1wPAU7TOW0ZtL7C3w09Oy2kN9Di4Btji7u9EXUjKNGC3ux9092ZgBXBZxDXh7g+7+3h3n0Lr9G9J5r9BAZ6z1ILhw8B2d78/6noAzGyImQ1MfVxF61/wHVHW5O53uftwdx9B64/f69090i4JwMwGpBafSU1RXEXrj7+Rcvf9wB4zG5UaugKIbGE8zf8kJtMnKW8DE82sf+r/xytoXYuKlJmdl/r1AmAmJfwz61OqN8qVmS0FLgcGm9le4F53fzjaqoDWzvJGoC415wxwt7v/e4Q1VQNLUjsFegHL3D022/ZiZijwVOv/9/QBfufuz0VbUrt/An6bmrL4b+CWiOshNZ97JXBr1LW0cfeNZrYc2ELrNMVW4vFY/ZNmNghoBm5z98OleuPYbSMUEZHcaApFRCShFOAiIgmlABcRSSgFuIhIQinARUQSSgEuIpJQCnARkYT6/9oFMfyF1E2zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the regression line\n",
    "line = regressor.coef_*x+regressor.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter (x,y)\n",
    "plt.plot (x, line, color = 'Black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6 :- Making Predictions\n",
    "Now that we have trained our algorithm, it's time to make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "### Testing data - In Hours\n",
    "print(x_test)\n",
    "\n",
    "### Predicting the scores\n",
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7 :- Comparing Actual vs Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Comparing Actual vs Predicted\n",
    "data = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score: 0.9515510725211552\n",
      "Testing score: 0.9454906892105356\n"
     ]
    }
   ],
   "source": [
    "### Estimating the Training Data and Test Data Score\n",
    "print(\"Training score:\", regressor.score(x_train, y_train))\n",
    "print(\"Testing score:\", regressor.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Ploting the line graph to depict the diffrence between the actual and predicted value.\n",
    "data.plot(kind='line', figsize=(8,6))\n",
    "plt.grid(which='major', linewidth='0.5', color = 'black')\n",
    "plt.grid(which='major', linewidth='0.5', color = 'blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of Hours = 9.25\n",
      "Predicted Score = 93.69173248737538\n"
     ]
    }
   ],
   "source": [
    "### Testing your own data.\n",
    "hours = 9.25\n",
    "test = np.array([hours])\n",
    "test = test.reshape(-1,1)\n",
    "own_pred = regressor.predict(test)\n",
    "print (\"No. of Hours = {}\".format(hours))\n",
    "print (\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8 :- Evaluating the model\n",
    "The final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. For simplicity here, we have chosen the mean square error. There are many such metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n",
      "Mean Squared Error: 21.5987693072174\n",
      "Root mean squared Error: 4.6474476121003665\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root mean squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}