Mission798Solutions.ipynb

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    "## Purpose of This Notebook\n",
    "\n",
    "The purpose of this notebook is to provide you with an example solution to the Guided Project for the Introduction to the Deep Learning with TensorFlow course. Since the choice of data preparation and preprocessing is up to you, your results may be slightly different. Use this solution as a guide on how you could structure your own answer."
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   "source": [
    "### Loading the Libraries and the Data"
   ]
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   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt"
   ]
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      "(319, 9)\n"
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>IPOName</th>\n",
       "      <th>Issue_Size</th>\n",
       "      <th>Subscription_QIB</th>\n",
       "      <th>Subscription_HNI</th>\n",
       "      <th>Subscription_RII</th>\n",
       "      <th>Subscription_Total</th>\n",
       "      <th>Issue_Price</th>\n",
       "      <th>Listing_Gains_Percent</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03/02/10</td>\n",
       "      <td>Infinite Comp</td>\n",
       "      <td>189.80</td>\n",
       "      <td>48.44</td>\n",
       "      <td>106.02</td>\n",
       "      <td>11.08</td>\n",
       "      <td>43.22</td>\n",
       "      <td>165</td>\n",
       "      <td>11.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>08/02/10</td>\n",
       "      <td>Jubilant Food</td>\n",
       "      <td>328.70</td>\n",
       "      <td>59.39</td>\n",
       "      <td>51.95</td>\n",
       "      <td>3.79</td>\n",
       "      <td>31.11</td>\n",
       "      <td>145</td>\n",
       "      <td>-84.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15/02/10</td>\n",
       "      <td>Syncom Health</td>\n",
       "      <td>56.25</td>\n",
       "      <td>0.99</td>\n",
       "      <td>16.60</td>\n",
       "      <td>6.25</td>\n",
       "      <td>5.17</td>\n",
       "      <td>75</td>\n",
       "      <td>17.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15/02/10</td>\n",
       "      <td>Vascon Engineer</td>\n",
       "      <td>199.80</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3.65</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.22</td>\n",
       "      <td>165</td>\n",
       "      <td>-11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19/02/10</td>\n",
       "      <td>Thangamayil</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.12</td>\n",
       "      <td>75</td>\n",
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       "      Date           IPOName  Issue_Size  Subscription_QIB  Subscription_HNI  \\\n",
       "0  03/02/10    Infinite Comp      189.80             48.44            106.02   \n",
       "1  08/02/10    Jubilant Food      328.70             59.39             51.95   \n",
       "2  15/02/10    Syncom Health       56.25              0.99             16.60   \n",
       "3  15/02/10  Vascon Engineer      199.80              1.12              3.65   \n",
       "4  19/02/10      Thangamayil        0.00              0.52              1.52   \n",
       "\n",
       "   Subscription_RII  Subscription_Total  Issue_Price  Listing_Gains_Percent  \n",
       "0             11.08               43.22          165                  11.82  \n",
       "1              3.79               31.11          145                 -84.21  \n",
       "2              6.25                5.17           75                  17.13  \n",
       "3              0.62                1.22          165                 -11.28  \n",
       "4              2.26                1.12           75                  -5.20  "
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   "source": [
    "df = pd.read_csv('Indian_IPO_Market_Data.csv')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
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      "Date \n",
      "IPOName\n",
      "Issue_Size\n",
      "Subscription_QIB\n",
      "Subscription_HNI\n",
      "Subscription_RII\n",
      "Subscription_Total\n",
      "Issue_Price\n",
      "Listing_Gains_Percent\n"
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    "for col in df.columns:\n",
    "  print(col)"
   ]
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       "      <th>count</th>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>top</th>\n",
       "      <td>16/08/21</td>\n",
       "      <td>Infinite Comp</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>freq</th>\n",
       "      <td>4</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1192.859969</td>\n",
       "      <td>25.684138</td>\n",
       "      <td>70.091379</td>\n",
       "      <td>8.561599</td>\n",
       "      <td>27.447147</td>\n",
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       "      <td>4.742696</td>\n",
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       "      <th>std</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>2384.643786</td>\n",
       "      <td>40.716782</td>\n",
       "      <td>142.454416</td>\n",
       "      <td>14.508670</td>\n",
       "      <td>48.772203</td>\n",
       "      <td>353.897614</td>\n",
       "      <td>47.650946</td>\n",
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       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-97.150000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169.005000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>1.255000</td>\n",
       "      <td>1.275000</td>\n",
       "      <td>1.645000</td>\n",
       "      <td>119.000000</td>\n",
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       "      <th>50%</th>\n",
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       "      <td>4.940000</td>\n",
       "      <td>5.070000</td>\n",
       "      <td>3.420000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>250.000000</td>\n",
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       "      <th>75%</th>\n",
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       "      <td>1100.000000</td>\n",
       "      <td>34.635000</td>\n",
       "      <td>62.095000</td>\n",
       "      <td>8.605000</td>\n",
       "      <td>33.395000</td>\n",
       "      <td>536.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>215.450000</td>\n",
       "      <td>958.070000</td>\n",
       "      <td>119.440000</td>\n",
       "      <td>326.490000</td>\n",
       "      <td>2150.000000</td>\n",
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       "      \n",
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       "    .colab-df-container {\n",
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       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
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       "    .colab-df-convert:hover {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-6f8a3775-9519-44be-a868-10b94774e6bd button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-6f8a3775-9519-44be-a868-10b94774e6bd');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           Date         IPOName    Issue_Size  Subscription_QIB  \\\n",
       "count        319            319    319.000000        319.000000   \n",
       "unique       287            319           NaN               NaN   \n",
       "top     16/08/21  Infinite Comp           NaN               NaN   \n",
       "freq           4              1           NaN               NaN   \n",
       "mean         NaN            NaN   1192.859969         25.684138   \n",
       "std          NaN            NaN   2384.643786         40.716782   \n",
       "min          NaN            NaN      0.000000          0.000000   \n",
       "25%          NaN            NaN    169.005000          1.150000   \n",
       "50%          NaN            NaN    496.250000          4.940000   \n",
       "75%          NaN            NaN   1100.000000         34.635000   \n",
       "max          NaN            NaN  21000.000000        215.450000   \n",
       "\n",
       "        Subscription_HNI  Subscription_RII  Subscription_Total  Issue_Price  \\\n",
       "count         319.000000        319.000000          319.000000   319.000000   \n",
       "unique               NaN               NaN                 NaN          NaN   \n",
       "top                  NaN               NaN                 NaN          NaN   \n",
       "freq                 NaN               NaN                 NaN          NaN   \n",
       "mean           70.091379          8.561599           27.447147   375.128527   \n",
       "std           142.454416         14.508670           48.772203   353.897614   \n",
       "min             0.000000          0.000000            0.000000     0.000000   \n",
       "25%             1.255000          1.275000            1.645000   119.000000   \n",
       "50%             5.070000          3.420000            4.930000   250.000000   \n",
       "75%            62.095000          8.605000           33.395000   536.000000   \n",
       "max           958.070000        119.440000          326.490000  2150.000000   \n",
       "\n",
       "        Listing_Gains_Percent  \n",
       "count              319.000000  \n",
       "unique                    NaN  \n",
       "top                       NaN  \n",
       "freq                      NaN  \n",
       "mean                 4.742696  \n",
       "std                 47.650946  \n",
       "min                -97.150000  \n",
       "25%                -11.555000  \n",
       "50%                  1.810000  \n",
       "75%                 25.310000  \n",
       "max                270.400000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:09.893105Z",
     "iopub.status.busy": "2022-12-27T18:32:09.892679Z",
     "iopub.status.idle": "2022-12-27T18:32:09.903968Z",
     "shell.execute_reply": "2022-12-27T18:32:09.902838Z",
     "shell.execute_reply.started": "2022-12-27T18:32:09.893072Z"
    },
    "id": "quX0LdyMK5uo",
    "outputId": "1262653e-07aa-40cf-c0ee-bce542137be3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    319.000000\n",
       "mean       4.742696\n",
       "std       47.650946\n",
       "min      -97.150000\n",
       "25%      -11.555000\n",
       "50%        1.810000\n",
       "75%       25.310000\n",
       "max      270.400000\n",
       "Name: Listing_Gains_Percent, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Listing_Gains_Percent'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7zveNwPwK5uo"
   },
   "source": [
    "The `Listing_Gains_Percent` variable interests us. This is the variable that will be used to create the target or outcome variable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KwApVwu4K5uo"
   },
   "source": [
    "## Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:09.906322Z",
     "iopub.status.busy": "2022-12-27T18:32:09.905646Z",
     "iopub.status.idle": "2022-12-27T18:32:09.919659Z",
     "shell.execute_reply": "2022-12-27T18:32:09.918367Z",
     "shell.execute_reply.started": "2022-12-27T18:32:09.906268Z"
    },
    "id": "555InP1vK5up",
    "outputId": "57b6589d-17a3-4809-943f-f1e82a55c475"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                     0\n",
       "IPOName                  0\n",
       "Issue_Size               0\n",
       "Subscription_QIB         0\n",
       "Subscription_HNI         0\n",
       "Subscription_RII         0\n",
       "Subscription_Total       0\n",
       "Issue_Price              0\n",
       "Listing_Gains_Percent    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:09.922352Z",
     "iopub.status.busy": "2022-12-27T18:32:09.921602Z",
     "iopub.status.idle": "2022-12-27T18:32:09.930727Z",
     "shell.execute_reply": "2022-12-27T18:32:09.929560Z",
     "shell.execute_reply.started": "2022-12-27T18:32:09.922304Z"
    },
    "id": "QqkYBOUNK5up"
   },
   "outputs": [],
   "source": [
    "df['Listing_Gains_Profit'] = np.where(df['Listing_Gains_Percent'] > 0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:09.932506Z",
     "iopub.status.busy": "2022-12-27T18:32:09.932169Z",
     "iopub.status.idle": "2022-12-27T18:32:09.946096Z",
     "shell.execute_reply": "2022-12-27T18:32:09.944849Z",
     "shell.execute_reply.started": "2022-12-27T18:32:09.932469Z"
    },
    "id": "FyUnzaeYK5up",
    "outputId": "46fab12a-dbfe-4130-b484-60062cc9d161"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    319.000000\n",
       "mean       0.545455\n",
       "std        0.498712\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        1.000000\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: Listing_Gains_Profit, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Listing_Gains_Profit'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:09.948100Z",
     "iopub.status.busy": "2022-12-27T18:32:09.947749Z",
     "iopub.status.idle": "2022-12-27T18:32:09.957636Z",
     "shell.execute_reply": "2022-12-27T18:32:09.956803Z",
     "shell.execute_reply.started": "2022-12-27T18:32:09.948070Z"
    },
    "id": "tusoDA3PK5up",
    "outputId": "c50fed56-638e-42b4-f28e-700dfa1caaae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.545455\n",
       "0    0.454545\n",
       "Name: Listing_Gains_Profit, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Listing_Gains_Profit'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:09.959345Z",
     "iopub.status.busy": "2022-12-27T18:32:09.958997Z",
     "iopub.status.idle": "2022-12-27T18:32:09.982144Z",
     "shell.execute_reply": "2022-12-27T18:32:09.980694Z",
     "shell.execute_reply.started": "2022-12-27T18:32:09.959314Z"
    },
    "id": "M8LYT3OdK5uq",
    "outputId": "3ac1f945-2e95-4a17-f5e9-d24b4d6d6a5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 319 entries, 0 to 318\n",
      "Data columns (total 7 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Issue_Size            319 non-null    float64\n",
      " 1   Subscription_QIB      319 non-null    float64\n",
      " 2   Subscription_HNI      319 non-null    float64\n",
      " 3   Subscription_RII      319 non-null    float64\n",
      " 4   Subscription_Total    319 non-null    float64\n",
      " 5   Issue_Price           319 non-null    int64  \n",
      " 6   Listing_Gains_Profit  319 non-null    int64  \n",
      "dtypes: float64(5), int64(2)\n",
      "memory usage: 17.6 KB\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(['Date ', 'IPOName', 'Listing_Gains_Percent'], axis=1)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ReVp20tOK5uq"
   },
   "source": [
    "We can see approximately 55% of the IPOs listed in profit, and we can also see that the data is fairly balanced. We have also dropped some of the variables that might not carry predictive power. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rqly_22nK5uq"
   },
   "source": [
    "## Data Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0QoVhxt5K5uq"
   },
   "source": [
    "Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:09.984476Z",
     "iopub.status.busy": "2022-12-27T18:32:09.983979Z",
     "iopub.status.idle": "2022-12-27T18:32:10.215080Z",
     "shell.execute_reply": "2022-12-27T18:32:10.214284Z",
     "shell.execute_reply.started": "2022-12-27T18:32:09.984433Z"
    },
    "id": "MGFC-ZSNK5ur",
    "outputId": "3811b1fe-a766-4131-ae9c-2540fd0187ce"
   },
   "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": [
    "# visualizing the target variable\n",
    "sns.countplot(x='Listing_Gains_Profit', data=df)\n",
    "plt.title('Distribution of IPO Listing Profit Category')\n",
    "plt.xlabel('Listing Profit (No=0, Yes=1)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:10.216760Z",
     "iopub.status.busy": "2022-12-27T18:32:10.216291Z",
     "iopub.status.idle": "2022-12-27T18:32:10.518091Z",
     "shell.execute_reply": "2022-12-27T18:32:10.516968Z",
     "shell.execute_reply.started": "2022-12-27T18:32:10.216731Z"
    },
    "id": "u8zD7oyCK5ur",
    "outputId": "e5a8a747-b65c-4021-bd87-bc00276d06d5"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "sns.histplot(data=df, x='Issue_Price', bins=50).set(title='Distribution of Issue_Price', ylabel='Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:10.520447Z",
     "iopub.status.busy": "2022-12-27T18:32:10.520140Z",
     "iopub.status.idle": "2022-12-27T18:32:10.822114Z",
     "shell.execute_reply": "2022-12-27T18:32:10.820900Z",
     "shell.execute_reply.started": "2022-12-27T18:32:10.520419Z"
    },
    "id": "62lZ4aQTK5ur",
    "outputId": "2099c778-fce5-4837-8819-9749ddf89d36"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "sns.histplot(data=df, x='Issue_Size', bins=50).set(title='Distribution of Issue_Size', ylabel='Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 268
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:10.828097Z",
     "iopub.status.busy": "2022-12-27T18:32:10.827747Z",
     "iopub.status.idle": "2022-12-27T18:32:10.998452Z",
     "shell.execute_reply": "2022-12-27T18:32:10.997110Z",
     "shell.execute_reply.started": "2022-12-27T18:32:10.828067Z"
    },
    "id": "WrkZRIztK5ur",
    "outputId": "5e348e9e-9a2f-470d-b685-cb152052f7bf"
   },
   "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": [
    "sns.boxplot(data=df, y='Issue_Size')\n",
    "plt.title('Boxplot of Issue_Size')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.000271Z",
     "iopub.status.busy": "2022-12-27T18:32:10.999879Z",
     "iopub.status.idle": "2022-12-27T18:32:11.198774Z",
     "shell.execute_reply": "2022-12-27T18:32:11.197900Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.000237Z"
    },
    "id": "UEK5vEBYK5us",
    "outputId": "e7938243-822e-4beb-fd1d-ca27ee676b74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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581HA/Eh+DewkaTfg1cCiiFgTEQ8Ai4A57YzdzMw2N2LXNCRNB14IXANMiYh7cq9VwJT8eSqwvDDailzWqLx+HsdJWiJpyerVq4c1fjMzG6GkIWkS8APgvRHxcLFfRAQQwzGfiDg3ImZGxMzJkycPxyTNzKyg7UlD0lakhPGdiPhhLr43NzuR3+/L5SuBaYXR98hljcrNzGwEtfvuKQHfAm6NiC8Vei0AandA9QA/LpQfk++iejHwUG7GuhR4laSd8wXwV+UyMzMbQe1+YOFLgbcDSyXdmMs+ApwOXCjpWOBO4I253yXAkUAfsB54J0BErJH0KeC6PNy/RsSaNsduZmZ1lC4pjD0zZ86MJUuWVB2GmdmoIun6iJjZqL9/EW5mZqU5aZiZWWlOGmZmVpqThpmZleakYWZmpTlpmJlZaU4aZmZWmpOGmZmV5qRhZmalOWmYmVlpThpmZlaak4aZmZXmpGEt9ff3c8opp9Df3191KGZWMScNa6m3t5elS5cyf/78qkMxs4o5aVhT/f39LFy4kIhg4cKFrm2YjXNOGtZUb28vGzduBGBgYMC1DbNxzknDmlq8eDEbNmwAYMOGDSxatKjiiMysSk4a1tSsWbOYODH9K/DEiROZPXt2xRGZWZWcNKypnp4eJkxIm0lXVxfHHHNMxRGZWZWcNKyp7u5u5syZgyTmzJlDd3d31SGZWYUmVh2Adb6enh6WLVvmWoaZOWlYa93d3Zx55plVh2FmHcDNU9aSfxFuZjVOGtaSfxFuZjVOGtaUfxFuZkVOGtZUb28vAwMDQPpxn2sbZuObk4Y1tXjx4ieTxsDAgH8RbjbOOWlYU4cccsgm3YceemhFkZhZJ3DSsKYkVR2CmXUQJw1r6uqrr27abWbji5OGNeUHFppZkZOGNeUHFppZkZOGNeUHFppZkZ89ZS35gYVmVuOkYS35gYVmVuPmKTMzK81Jw8xGLT+BeeS1NWlIOk/SfZJuLpR9UtJKSTfm15GFfh+W1CfpNkmvLpTPyWV9kk5tZ8y2Oe+Y1qnOOeccbrrpJs4999yqQxk32l3TmAfMGaT83yPiwPy6BEDSfsCbgRfkcb4mqUtSF/BV4AhgP+AteVgbIX40unWi/v5+Fi9eDMCiRYt8UjNC2po0IuIqYE3JwY8CLoiIxyLij0AfcFB+9UXEHRHxOHBBHtZGgB+Nbp3qnHPOYePGjQBs3LjRtY0RUtU1jZMk3ZSbr3bOZVOB5YVhVuSyRuWbkXScpCWSlqxevbodcY87vb29T+6YAwMDrm1Yx7j88ss36a7VOqy9qkgaZwN7AwcC9wBfHK4JR8S5ETEzImZOnjx5uCY7ri1evJgNGzYA6f80/Gh06xS1k5lG3dYeI540IuLeiBiIiI3AN0jNTwArgWmFQffIZY3KbQT42VPWqWqPt2nUbe0x4ktZ0m6FztcBtTurFgBvlrSNpOcA+wDXAtcB+0h6jqStSRfLF4xkzOOZnz1lnerwww/fpHvWrFkVRTK+tPuW2/OBXwH7Sloh6Vjg85KWSroJeAXwPoCIuAW4EPgtsBA4MddINgAnAZcCtwIX5mFtBPjZU9apjj/++Cf/72XChAkcd9xxFUc0PrT1MSIR8ZZBir/VZPjTgNMGKb8EuGQYQ7Mh8LOnrBN1d3cze/ZsLrvsMmbPnu0TmhHiZ09ZS372lHWq448/nlWrVrmWMYKcNMxs1PIJzcjz7QZmZlaak4aZmZXmpGFmZqU5aZiZWWlOGmZmVpqThrXk/9MwsxonDWvJ/6dhZjWlk4akKZK+JemnuXu//FgQG8P8fxpmVjSUmsY80vOfds/dvwfeO9wBWWfx/2mYWdFQksauEXEhsBEgP0hwoC1RWcfw/2mYWdFQksYjkrqBAJD0YuChtkRlHcP/p2FmRUNJGu8n/Y/F3pJ+AcwHTm5LVNYx/H8aZlZUOmlExA3Ay4G/Bo4HXhARN7UrMOsM/j8NMysayt1TJwKTIuKWiLgZmCTpPe0LzTpFT08P+++/v2sZ1nH8G6KRN5TmqXdHxIO1joh4AHj38IdkZlaOf0M08oaSNLpU+29FQFIXsPXwh2SdxjumdSL/hqgaQ0kaC4HvSTpc0uHA+bnMxjDvmNap/BuiagwlaXwI+Bnwj/l1OfBP7QjKOod3TOtU/g1RNYZy99TGiDg7Il6fX+dEhH/cN8Z5x7RONWvWLGot5pL8G6IR0jJpSLowvy+VdFP9q/0hWpX84z7rVK997WuJCAAigte85jUVRzQ+lKlpzM3vfwu8ZpCXjWH+cZ91qgULFmxS07j44osrjmh8aJk0IuKefKfUvIi4s/41AjFahfzjPutUixcv3qSm4abTkVHqmka+drFR0jPbHI91IP+4zzqRm06rMXEIw64DlkpaBDxSK4yIU4Y9Kuso3d3dnHnmmVWHYbaJnp4eFi5Md/276XTkDCVp/DC/zMwqV2s6vfjii910OoJKJQ1JRwOTgaURcWl7QzKz0eCss86ir6+v0hiWL19OV1cXt99+O3Pnzm09QhvNmDGDk08e+w/+LnPL7deA9wHdwKckfaztUZmZlfDYY4+xzTbbsNVWW1UdyrhRpqbxMuCAiBiQtD1wNfCp9oZlZp2uE86qa7WLM844o+JIxo8ySePx2i+/I2J98aGF1n6d0ASwcuVKAKZOnVppHDB+mgDMOlWZpPG8wi+/Rfrnvpvy54iIv2hbdNYRHn300apDMLMOUSZpPL/tUVhDnXBW7SYAM6tpmTTK/upb0q8i4iVPPyQzM+tUQ3k0eivbDuO0zMysAw1n0ohhnJaZmXWg4UwaZmY2xg1n0tjsVlxJ50m6T9LNhbJdJC2SdHt+3zmXS9KZkvryf3W8qDBOTx7+dkk9wxizmZkNwZCShqS9JM3Kn7eTtGOh99sHGWUeMKeu7FTg8ojYh/SXsafm8iOAffLrOODsPJ9dgE8ABwMHAZ+oJRozMxtZpZOGpHcD3wfOyUV7AD+q9Y+Im+vHiYirgDV1xUcBvflzL3B0oXx+JL8GdpK0G/BqYFFErImIB4BFbJ6IzMxsBAylpnEi8FLgYYCIuB141hbMc0pE3JM/rwKm5M9TgeWF4Vbkskblm5F0nKQlkpasXr16C0IzM7NmhpI0HouIx2sdkibyNO+YivS3W8N211VEnBsRMyNi5uTJk4drsmZmlg0laVwp6SPAdpJmA/8FbMmf8t6bm53I7/fl8pXAtMJwe+SyRuVmZjbChpI0TgVWA0uB44FLgI9uwTwXALU7oHqAHxfKj8l3Ub0YeCg3Y10KvErSzvkC+KtymZmZjbDS/9wXERuBb+RXKZLOBw4DdpW0gnQX1OnAhZKOBe4E3pgHvwQ4EugD1gPvzPNdI+lTwHV5uH+NiPqL62ZmNgJKJw1Jf2SQ6w8R8dxG40TEWxr0OnyQYYN0sX2w6ZwHnFcuUjMza5eh/Ef4zMLnbYE3ALsMbzhmZtbJSl/TiIj+wmtlRHwZ+Js2xmZmZh1mKM1TLyp0TiDVPIZSUzEzs1FuKAf9LxY+bwCW8dRFbDMzGweGcvfUK9oZiJmZdb6hPHtqrqRn5N9RfFPSDZJe1c7gzMysswzlx33vioiHST+u6yY91fb0tkRlZmYdaShJo/Z/GUeSnkZ7C4P8h4aZmY1dQ0ka10u6jJQ0Ls3/pbGxPWGZmVknGsrdU8cCBwJ3RMT6/OdI72xPWGZm1omGUtN4CXBbRDwo6W2khxU+1J6wzMysEw0laZwNrJd0APAB4A/A/LZEZWZmHWkoSWNDfqjgUcBXIuKrwI4txjEzszFkKNc01kr6MPA24GWSJgBbtScsMzPrREOpabwJeAw4NiJWkf5B7wtticrMzDrSUB4jsgr4UqH7LnxNw8xsXGmZNCStZZA/XyL9sC8i4hnDHpWZmXWklkkjInyx28zMgKFd0zAzs3HOScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0pw0zMysNCcNMzMrzUnDzMxKc9IwM7PSnDTMzKw0Jw0zMyvNScPMzEpz0jAzs9KcNMzMrLTSf/dqZp3hrLPOoq+vr+owOkJtOcydO7fiSDrDjBkzOPnkk9s6DycNs1Gmr6+P22/5X/acNFB1KJXb+onUWPLYnUsqjqR6d63rGpH5OGmYjUJ7ThrgIy96uOowrIN85oZnjMh8KksakpYBa4EBYENEzJS0C/A9YDqwDHhjRDwgScAZwJHAeuAdEXFDO+NzE8BT3ASwqZFoAjDrVFXXNF4REfcXuk8FLo+I0yWdmrs/BBwB7JNfBwNn5/e26evr48abb2Vg+13aOZtRYcLjAcD1d9xbcSTV61q/puoQzCpVddKodxRwWP7cC1xBShpHAfMjIoBfS9pJ0m4RcU87gxnYfhcefd6R7ZyFjTLb/e6SqkMwq1SVt9wGcJmk6yUdl8umFBLBKmBK/jwVWF4Yd0Uu24Sk4yQtkbRk9erV7YrbzGzcqrKmcUhErJT0LGCRpN8Ve0ZESIqhTDAizgXOBZg5c+aQxjUzs9Yqq2lExMr8fh9wEXAQcK+k3QDy+3158JXAtMLoe+QyMzMbQZUkDUk7SNqx9hl4FXAzsADoyYP1AD/OnxcAxyh5MfBQu69nmJnZ5qpqnpoCXJTupGUi8N2IWCjpOuBCSccCdwJvzMNfQrrdto90y+07Rz5kMzOrJGlExB3AAYOU9wOHD1IewIkjEJqZmTXhBxaamVlpThpmZlaak4aZmZXmpGFmZqU5aZiZWWlOGmZmVpqThpmZleakYWZmpTlpmJlZaU4aZmZWmpOGmZmV5qRhZmalOWmYmVlpThpmZlZalX/3amZbYOXKlTyytovP3PCMqkOxDnLn2i52WNn+PzR1TcPMzEpzTcNslJk6dSqPbbiHj7zo4apDsQ7ymRuewTZTp7Z9Pq5pmJlZaU4aZmZWmpunGli5ciVd6x9iu99dUnUo1kG61vezcuWGqsMwq4xrGmZmVpprGg1MnTqVVY9N5NHnHVl1KNZBtvvdJUydOqXqMMwq45qGmZmV5qRhZmalOWmYmVlpvqZhNgrdtc6PEQG4d306752y/caKI6neXeu62GcE5uOkYTbKzJgxo+oQOsbjfX0AbLOXl8k+jMy24aRhNsqcfPLJVYfQMebOnQvAGWecUXEk44evaZiZWWlOGmZmVpqbp5roWr/GjxEBJvwpPU1147a+8Nq1fg3gH/fZ+OWk0YAvNj6lr28tADOe64MlTPG2YeOak0YDvtj4FF9sNLMaX9MwM7PSnDTMzKw0Jw0zMyvNScPMzEobVUlD0hxJt0nqk3Rq1fGYmY03o+buKUldwFeB2cAK4DpJCyLit9VGZjY+nXXWWfTlZz9VpTb/2h1+VZoxY8a4uOty1CQN4CCgLyLuAJB0AXAUMKaThnfMTY2XHdPK2W677aoOYdwZTUljKrC80L0COLg4gKTjgOMA9txzz5GLbIzzjmmDcfIen0ZT0mgpIs4FzgWYOXNmVBzOsPCOaWadZDRdCF8JTCt075HLzMxshIympHEdsI+k50jaGngzsKDimMzMxpVR0zwVERsknQRcCnQB50XELRWHZWY2royapAEQEZcAfla5mVlFRlPzlJmZVcxJw8zMSnPSMDOz0pw0zMysNEWMid/AbUbSauDOquMYQ3YF7q86CLNBeNscXntFxORGPcds0rDhJWlJRMysOg6zet42R5abp8zMrDQnDTMzK81Jw8o6t+oAzBrwtjmCfE3DzMxKc03DzMxKc9IwM7PSnDSsJUlzJN0mqU/SqVXHYwYg6TxJ90m6uepYxhMnDWtKUhfwVeAIYD/gLZL2qzYqMwDmAXOqDmK8cdKwVg4C+iLijoh4HLgAOKrimMyIiKuANVXHMd44aVgrU4Hlhe4VuczMxiEnDTMzK81Jw1pZCUwrdO+Ry8xsHHLSsFauA/aR9BxJWwNvBhZUHJOZVcRJw5qKiA3AScClwK3AhRFxS7VRmYGk84FfAftKWiHp2KpjGg/8GBEzMyvNNQ0zMyvNScPMzEpz0jAzs9KcNMzMrDQnDTMzK81Jw0aEpHWDlJ0g6Zgm4xwm6a/LDj/EeK7IT+79jaRfSNp3iOOfIulWSd+R9Nra038lHVi5h0cAAATsSURBVN3sgY6S3lv7DpLmSVopaZvcvaukZU/ja9Xm8RxJ1+SnEn8v/76m0bDPkrRM0rMLZV+V9OGnG0fdfE6TtLx+O5B0kqR3Dee8rL2cNKwyEfH1iJjfZJDDgCeTRonhh+qtEXEA0At8ob5nfsJvI+8BZkfEWyNiQUScnsuPJj0NeDOSJgLvAr5bKB7IZcPpc8C/R8QM4AGg4e8XIuI+4HTg33KMLwIOrXUPo4tJD7+sdx5w8jDPy9rIScMqI+mTkj6YP58i6beSbpJ0gaTpwAnA+yTdKOnQuuGvkPQ5SddK+r2kQ3P59pIuzNO6KJ9xz2wRylXAjDz+OklflPQb4CWS3i/p5vx6bx7m68BzgZ9Kep+kd0j6Sq4VvRb4Qo5577r5vBK4If9gsubL+TtOrFs2kvSFPN+lkt5Ucpkqz+f7uaiXlMiaORfYW9IrSI/BPwnYU9JCSddLulrS8/L035Bj+o2kq8rEBBARv46IewYpXw8skzRYQrEONLH1IGYj4lTgORHxmKSdIuLBfHBeFxG1s+DD68aZGBEHSToS+AQwi1QDeCAi9pP058CNJeb9GmBp/rwDcE1EfEDSXwLvBA4GBFwj6cqIOEHSHOAVEXG/pHcARMQvJS0A/jsivr/5bHgpcH1d2V3Az4G3k87Ga/4OOBA4ANgVuC4fpNcBVzf4Hn8P3Ac8WEhMLZ9KHBEbJf0j8D/Agoi4StLlwAkRcbukg4GvkZLRx4FXR8RKSTsB5Ka97zWY/GER8WCz+QNLSLWba1sMZx3AScM6xU3AdyT9CPhRyXF+mN+vB6bnz4cAZwBExM2Sbmoy/nckPQos46kmkgHgB4VpXRQRjwBI+iHp4Pa/JeOrtxvpUSz1Pgv8GPhJoewQ4PyIGADulXQl8FcRsYCUTAYladctCSwiblT6B7yvSZpEahb8r1RxAWCb/P4LYJ6kC8nLPyJuaxZTCfcBz3sa49sIctKwTvE3wMtIZ/3/LGn/EuM8lt8H2LJt+a0RsaSu7E/5QN0OjwLb1hfms/kbgTe2moCkHWle07gV2EnSxFzbGMpTiTfm1wRSbWWzRJBrWQeT1tf1uTa2K0+vprEtadnYKOBrGlY5SROAaRHxM+BDwDOBScBaYMchTu4X5INvvoupTPJp5Grg6HydZAfgdTQ+YNc0i/lW8rWTQZwGfLBu3m+S1CVpMimhXhsRayPiwAav30Z6mNzPgNfn6fSQajFIep2kz7aIn4h4GPijpDfk8STpgPx574i4JiI+DqwmrbfbmsTUKmEA/Bng//keJZw0bKRsr/Qk0trr/YV+XcC3JS0lNf2cmQ82FwOvq10ILzmfrwGTJf0W+DRwC/DQlgQcETeQ/of6WuAa4JsR0app6gLg/0v630EuhP+UdPAfbF63ADcUii4iNdn9hnSt4Z8iYlXJ0D8EvF9SH9ANfCuX7w08XHIabwWOzTcE3MJTf/H7hXxh/mbglzm+liR9XtIKntoOPlno/VJgUcm4rGJ+yq2NKfk22a0i4k/5oL0Y2Df/v3nlJF1ESgC3VzDvbwPvi4jVIz3vRiS9EHh/RLy96lisHCcNG1Nym//PgK1Idzx9KCJ+Wm1UT8l3Gk2JiNK3q45lkmYDt0fEsqpjsXKcNMzMrDRf0zAzs9KcNMzMrDQnDTMzK81Jw8zMSnPSMDOz0v4Pk5AYQ5XyZxgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, x='Listing_Gains_Profit', y='Issue_Price')\n",
    "plt.title('Boxplot of Issue_Price with respect to Listing Gains Type')\n",
    "plt.xlabel('Listing Profit (No=0, Yes=1)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.200947Z",
     "iopub.status.busy": "2022-12-27T18:32:11.200319Z",
     "iopub.status.idle": "2022-12-27T18:32:11.207924Z",
     "shell.execute_reply": "2022-12-27T18:32:11.207051Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.200912Z"
    },
    "id": "5Tas_DpEK5us",
    "outputId": "ab488bb0-5fad-452c-8ea0-84e480a780f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Issue_Size              4.853402\n",
      "Subscription_QIB        2.143705\n",
      "Subscription_HNI        3.078445\n",
      "Subscription_RII        3.708274\n",
      "Subscription_Total      2.911907\n",
      "Issue_Price             1.696881\n",
      "Listing_Gains_Profit   -0.183438\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 268
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.209644Z",
     "iopub.status.busy": "2022-12-27T18:32:11.209282Z",
     "iopub.status.idle": "2022-12-27T18:32:11.386354Z",
     "shell.execute_reply": "2022-12-27T18:32:11.385216Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.209599Z"
    },
    "id": "9OclMZRSK5us",
    "outputId": "baa862bb-349c-40f2-d9dc-b11c948f6bd9"
   },
   "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": [
    "sns.boxplot(data=df, y='Subscription_QIB')\n",
    "plt.title('Boxplot of Subscription_QIB')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.388794Z",
     "iopub.status.busy": "2022-12-27T18:32:11.388041Z",
     "iopub.status.idle": "2022-12-27T18:32:11.623059Z",
     "shell.execute_reply": "2022-12-27T18:32:11.621912Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.388733Z"
    },
    "id": "xFoR7F-lK5us",
    "outputId": "9bc27b47-0862-4735-e301-21330ef1b8e1"
   },
   "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": [
    "sns.scatterplot(data=df, x='Subscription_RII', y='Subscription_Total')\n",
    "plt.title('Scatterplot between Retail and Total IPO Subscription')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zS9z1_A-K5us"
   },
   "source": [
    "Here are some of the findings that we can draw from the visualizations above:\n",
    "\n",
    "1. The histogram and the boxplots show that outliers are present in the data and might need outlier treatment. \n",
    "\n",
    "2. The boxplot of `Issue_Price`, with respect to `Listing_Gains_Profit`, shows that there are more outliers for IPOs that listed a loss than there are outliers for IPOs that listed a profit. \n",
    "\n",
    "3. We also observed a correlation between Retail and Total IPO Subscription via a scatterplot. You can check for correlations between other continuous variables as well.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T-PN0gKiK5ut"
   },
   "source": [
    "## Outlier Treatment\n",
    "\n",
    "Since there are outliers in the data, we need to treat them. However, feel free to use a different approach or choose to work with outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 268
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.625254Z",
     "iopub.status.busy": "2022-12-27T18:32:11.624775Z",
     "iopub.status.idle": "2022-12-27T18:32:11.796003Z",
     "shell.execute_reply": "2022-12-27T18:32:11.794960Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.625201Z"
    },
    "id": "HuG-Jcs2K5ut",
    "outputId": "3512eab7-8eab-4e93-f96f-f3a0c76f304e"
   },
   "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": [
    "sns.boxplot(data=df, y='Issue_Size')\n",
    "plt.title('Boxplot of Issue_Size')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.797659Z",
     "iopub.status.busy": "2022-12-27T18:32:11.797316Z",
     "iopub.status.idle": "2022-12-27T18:32:11.808432Z",
     "shell.execute_reply": "2022-12-27T18:32:11.807238Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.797626Z"
    },
    "id": "VXcqCGqCK5ut",
    "outputId": "3347b3ee-6a34-4f2d-c8f4-39af7d394616"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR = 930.995\n",
      "lower = -1227.4875000000002\n",
      "upper = 2496.4925000000003\n"
     ]
    }
   ],
   "source": [
    "q1 = df['Issue_Size'].quantile(q=0.25)\n",
    "q3 = df['Issue_Size'].quantile(q=0.75)  \n",
    "iqr = q3 - q1   \n",
    "lower = (q1 - 1.5 * iqr) \n",
    "upper = (q3 + 1.5 * iqr) \n",
    "print('IQR = ', iqr, '\\nlower = ', lower, '\\nupper = ', upper, sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.809845Z",
     "iopub.status.busy": "2022-12-27T18:32:11.809529Z",
     "iopub.status.idle": "2022-12-27T18:32:11.829979Z",
     "shell.execute_reply": "2022-12-27T18:32:11.828506Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.809817Z"
    },
    "id": "MyxUXuWCK5ut",
    "outputId": "ec2114dc-1139-4fc4-a173-7b75c18ea6ec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     319.000000\n",
       "mean      763.561238\n",
       "std       769.689122\n",
       "min         0.000000\n",
       "25%       169.005000\n",
       "50%       496.250000\n",
       "75%      1100.000000\n",
       "max      2496.492500\n",
       "Name: Issue_Size, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Issue_Size'] = df['Issue_Size'].clip(lower, upper)\n",
    "df['Issue_Size'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.831412Z",
     "iopub.status.busy": "2022-12-27T18:32:11.831071Z",
     "iopub.status.idle": "2022-12-27T18:32:11.842221Z",
     "shell.execute_reply": "2022-12-27T18:32:11.840980Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.831377Z"
    },
    "id": "2no3iuXXK5ut",
    "outputId": "d7c63024-5830-4c17-b6f8-1eb659dcc223"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR = 33.48500000000001\n",
      "lower = -49.07750000000001\n",
      "upper = 84.86250000000001\n"
     ]
    }
   ],
   "source": [
    "q1 = df['Subscription_QIB'].quantile(q=0.25)\n",
    "q3 = df['Subscription_QIB'].quantile(q=0.75)  \n",
    "iqr = q3 - q1   \n",
    "lower = (q1 - 1.5 * iqr) \n",
    "upper = (q3 + 1.5 * iqr) \n",
    "print('IQR = ', iqr, '\\nlower = ', lower, '\\nupper = ', upper, sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.844442Z",
     "iopub.status.busy": "2022-12-27T18:32:11.843981Z",
     "iopub.status.idle": "2022-12-27T18:32:11.860042Z",
     "shell.execute_reply": "2022-12-27T18:32:11.858761Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.844398Z"
    },
    "id": "KTRUl5bdK5ut",
    "outputId": "a6d98c3b-4113-4244-fd0b-a697ca16e139"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    319.000000\n",
       "mean      21.521183\n",
       "std       29.104549\n",
       "min        0.000000\n",
       "25%        1.150000\n",
       "50%        4.940000\n",
       "75%       34.635000\n",
       "max       84.862500\n",
       "Name: Subscription_QIB, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Subscription_QIB'] = df['Subscription_QIB'].clip(lower, upper)\n",
    "df['Subscription_QIB'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.862921Z",
     "iopub.status.busy": "2022-12-27T18:32:11.861528Z",
     "iopub.status.idle": "2022-12-27T18:32:11.871911Z",
     "shell.execute_reply": "2022-12-27T18:32:11.870729Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.862883Z"
    },
    "id": "RCU2kxX-K5uu",
    "outputId": "b0b6bcd6-de91-42d3-d45d-0912f6f3e198"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR = 60.839999999999996\n",
      "lower = -90.005\n",
      "upper = 153.355\n"
     ]
    }
   ],
   "source": [
    "q1 = df['Subscription_HNI'].quantile(q=0.25)\n",
    "q3 = df['Subscription_HNI'].quantile(q=0.75)  \n",
    "iqr = q3 - q1   \n",
    "lower = (q1 - 1.5 * iqr) \n",
    "upper = (q3 + 1.5 * iqr) \n",
    "print('IQR = ', iqr, '\\nlower = ', lower, '\\nupper = ', upper, sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.874563Z",
     "iopub.status.busy": "2022-12-27T18:32:11.873563Z",
     "iopub.status.idle": "2022-12-27T18:32:11.889349Z",
     "shell.execute_reply": "2022-12-27T18:32:11.888085Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.874517Z"
    },
    "id": "7Bi4KyUOK5uu",
    "outputId": "189f296a-a9b6-4c07-cdf3-d5c7cd94e147"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    319.000000\n",
       "mean      40.356426\n",
       "std       57.427921\n",
       "min        0.000000\n",
       "25%        1.255000\n",
       "50%        5.070000\n",
       "75%       62.095000\n",
       "max      153.355000\n",
       "Name: Subscription_HNI, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Subscription_HNI'] = df['Subscription_HNI'].clip(lower, upper)\n",
    "df['Subscription_HNI'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.891309Z",
     "iopub.status.busy": "2022-12-27T18:32:11.890841Z",
     "iopub.status.idle": "2022-12-27T18:32:11.903508Z",
     "shell.execute_reply": "2022-12-27T18:32:11.902206Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.891254Z"
    },
    "id": "ynAHmlw4K5uu",
    "outputId": "32547e3f-effb-4be6-b31f-d94e5ca8fc15"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR = 7.33\n",
      "lower = -9.72\n",
      "upper = 19.6\n"
     ]
    }
   ],
   "source": [
    "q1 = df['Subscription_RII'].quantile(q=0.25)\n",
    "q3 = df['Subscription_RII'].quantile(q=0.75)  \n",
    "iqr = q3 - q1   \n",
    "lower = (q1 - 1.5 * iqr) \n",
    "upper = (q3 + 1.5 * iqr) \n",
    "print('IQR = ', iqr, '\\nlower = ', lower, '\\nupper = ', upper, sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.905291Z",
     "iopub.status.busy": "2022-12-27T18:32:11.904829Z",
     "iopub.status.idle": "2022-12-27T18:32:11.920193Z",
     "shell.execute_reply": "2022-12-27T18:32:11.919398Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.905250Z"
    },
    "id": "IjQZoL6KK5uu",
    "outputId": "30c6be06-c313-4ea4-a481-03e0c34b3452"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    319.000000\n",
       "mean       6.060940\n",
       "std        6.176882\n",
       "min        0.000000\n",
       "25%        1.275000\n",
       "50%        3.420000\n",
       "75%        8.605000\n",
       "max       19.600000\n",
       "Name: Subscription_RII, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Subscription_RII'] = df['Subscription_RII'].clip(lower, upper)\n",
    "df['Subscription_RII'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.921919Z",
     "iopub.status.busy": "2022-12-27T18:32:11.921276Z",
     "iopub.status.idle": "2022-12-27T18:32:11.935310Z",
     "shell.execute_reply": "2022-12-27T18:32:11.934484Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.921885Z"
    },
    "id": "jDf0sDipK5uu",
    "outputId": "a2f3cd7c-2456-4217-df5c-1b4f019af4fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR = 31.749999999999996\n",
      "lower = -45.97999999999999\n",
      "upper = 81.01999999999998\n"
     ]
    }
   ],
   "source": [
    "q1 = df['Subscription_Total'].quantile(q=0.25)\n",
    "q3 = df['Subscription_Total'].quantile(q=0.75)  \n",
    "iqr = q3 - q1   \n",
    "lower = (q1 - 1.5 * iqr) \n",
    "upper = (q3 + 1.5 * iqr) \n",
    "print('IQR = ', iqr, '\\nlower = ', lower, '\\nupper = ', upper, sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.937247Z",
     "iopub.status.busy": "2022-12-27T18:32:11.936404Z",
     "iopub.status.idle": "2022-12-27T18:32:11.952110Z",
     "shell.execute_reply": "2022-12-27T18:32:11.951342Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.937211Z"
    },
    "id": "nrK_ia3IK5uu",
    "outputId": "329220ea-eb96-4a7e-c206-479a8a735e36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    319.000000\n",
       "mean      20.456646\n",
       "std       27.217740\n",
       "min        0.000000\n",
       "25%        1.645000\n",
       "50%        4.930000\n",
       "75%       33.395000\n",
       "max       81.020000\n",
       "Name: Subscription_Total, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Subscription_Total'] = df['Subscription_Total'].clip(lower, upper)\n",
    "df['Subscription_Total'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CqBFYEsEK5uv"
   },
   "source": [
    "There are different approaches to outlier treatment, but the one we've used here is outlier identification using the interquartile menthod. Once we identified the outliers, we clipped the variable values between the upper and lower bounds. This is only one appoach — feel free to experiment with other methods. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0FdzVKR3K5uv"
   },
   "source": [
    "## Setting the Target and Predictor Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.953327Z",
     "iopub.status.busy": "2022-12-27T18:32:11.953000Z",
     "iopub.status.idle": "2022-12-27T18:32:11.996296Z",
     "shell.execute_reply": "2022-12-27T18:32:11.995087Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.953297Z"
    },
    "id": "lnCJqqTzK5uv",
    "outputId": "d8a74d8b-72c1-4c15-8231-9c793f9733bf"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Issue_Size</th>\n",
       "      <th>Subscription_QIB</th>\n",
       "      <th>Subscription_HNI</th>\n",
       "      <th>Subscription_RII</th>\n",
       "      <th>Subscription_Total</th>\n",
       "      <th>Issue_Price</th>\n",
       "      <th>Listing_Gains_Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>319.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>319.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.305854</td>\n",
       "      <td>0.253601</td>\n",
       "      <td>0.263157</td>\n",
       "      <td>0.309232</td>\n",
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       "      <td>0.174478</td>\n",
       "      <td>0.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.308308</td>\n",
       "      <td>0.342961</td>\n",
       "      <td>0.374477</td>\n",
       "      <td>0.315147</td>\n",
       "      <td>0.335939</td>\n",
       "      <td>0.164604</td>\n",
       "      <td>0.498712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.067697</td>\n",
       "      <td>0.013551</td>\n",
       "      <td>0.008184</td>\n",
       "      <td>0.065051</td>\n",
       "      <td>0.020304</td>\n",
       "      <td>0.055349</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.198779</td>\n",
       "      <td>0.058212</td>\n",
       "      <td>0.033061</td>\n",
       "      <td>0.174490</td>\n",
       "      <td>0.060849</td>\n",
       "      <td>0.116279</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.440618</td>\n",
       "      <td>0.408131</td>\n",
       "      <td>0.404910</td>\n",
       "      <td>0.439031</td>\n",
       "      <td>0.412182</td>\n",
       "      <td>0.249302</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-6da0948d-d60d-45c2-bbd2-8a0580becfd3 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
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       "          const element = document.querySelector('#df-6da0948d-d60d-45c2-bbd2-8a0580becfd3');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       Issue_Size  Subscription_QIB  Subscription_HNI  Subscription_RII  \\\n",
       "count  319.000000        319.000000        319.000000        319.000000   \n",
       "mean     0.305854          0.253601          0.263157          0.309232   \n",
       "std      0.308308          0.342961          0.374477          0.315147   \n",
       "min      0.000000          0.000000          0.000000          0.000000   \n",
       "25%      0.067697          0.013551          0.008184          0.065051   \n",
       "50%      0.198779          0.058212          0.033061          0.174490   \n",
       "75%      0.440618          0.408131          0.404910          0.439031   \n",
       "max      1.000000          1.000000          1.000000          1.000000   \n",
       "\n",
       "       Subscription_Total  Issue_Price  Listing_Gains_Profit  \n",
       "count          319.000000   319.000000            319.000000  \n",
       "mean             0.252489     0.174478              0.545455  \n",
       "std              0.335939     0.164604              0.498712  \n",
       "min              0.000000     0.000000              0.000000  \n",
       "25%              0.020304     0.055349              0.000000  \n",
       "50%              0.060849     0.116279              1.000000  \n",
       "75%              0.412182     0.249302              1.000000  \n",
       "max              1.000000     1.000000              1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_variable = ['Listing_Gains_Profit'] \n",
    "predictors = list(set(list(df.columns)) - set(target_variable))\n",
    "df[predictors] = df[predictors]/df[predictors].max()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qB4LgEa5K5uv"
   },
   "source": [
    "We have created an object of the dependent variable called `target_variable` and also a list of all the features, excluding the target variable `Listing_Gains_Profit`. During data exploration, we observed that the distribution of the variables differed significantly. This could influence the modeling process, so to prevent this, we performed normalization by scaling the predictors. You can see that the normalized values of the predictors lie between 0 and 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Re0qt2A7K5uv"
   },
   "source": [
    "## Creating the Holdout Validation Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:11.997760Z",
     "iopub.status.busy": "2022-12-27T18:32:11.997459Z",
     "iopub.status.idle": "2022-12-27T18:32:12.007201Z",
     "shell.execute_reply": "2022-12-27T18:32:12.006116Z",
     "shell.execute_reply.started": "2022-12-27T18:32:11.997732Z"
    },
    "id": "79ia8fAdK5uv",
    "outputId": "831e6c4e-fd28-4311-f1c1-db69f0d196e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(223, 6)\n",
      "(96, 6)\n"
     ]
    }
   ],
   "source": [
    "X = df[predictors].values\n",
    "y = df[target_variable].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=100)\n",
    "print(X_train.shape); print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4yRccKTCK5uv"
   },
   "source": [
    "We will use the hold out validation approach to model evaluation. In this approach, we will divide the data in the 70:30 ratio, where we will use 70% of the data for training the model, while we will use the other 30% of the data to test the model. Feel free to use a different train-to-test ratio. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hbdwpVyOK5uv"
   },
   "source": [
    "## Define the Deep Learning Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-27T18:32:12.009002Z",
     "iopub.status.busy": "2022-12-27T18:32:12.008652Z",
     "iopub.status.idle": "2022-12-27T18:32:12.162585Z",
     "shell.execute_reply": "2022-12-27T18:32:12.161353Z",
     "shell.execute_reply.started": "2022-12-27T18:32:12.008972Z"
    },
    "id": "5fE0bP4rK5uv"
   },
   "outputs": [],
   "source": [
    "# define model\n",
    "tf.random.set_seed(100)\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(32, input_shape = (X_train.shape[1],), activation = 'relu'))\n",
    "model.add(tf.keras.layers.Dense(16, activation= 'relu'))\n",
    "model.add(tf.keras.layers.Dense(8, activation= 'relu'))\n",
    "model.add(tf.keras.layers.Dense(4, activation= 'relu'))\n",
    "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hUY4nTU8K5uw"
   },
   "source": [
    "In this step, we have defined the model by instantiating the sequential model class in TensorFlow's Keras. The model architecture is comprised of four hidden layers with `relu` as the activation function. The output layer uses a `sigmoid` activation function, which is a good choice for a binary classification model. Feel free to experiment with different model architectures, activation functions, and the number of nodes in each layer!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_8zUg4DyK5uw"
   },
   "source": [
    "## Compile and Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-27T18:35:24.389908Z",
     "iopub.status.busy": "2022-12-27T18:35:24.389439Z",
     "iopub.status.idle": "2022-12-27T18:35:24.406351Z",
     "shell.execute_reply": "2022-12-27T18:35:24.405197Z",
     "shell.execute_reply.started": "2022-12-27T18:35:24.389872Z"
    },
    "id": "RG0Ssv5dK5uw"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:35:30.988666Z",
     "iopub.status.busy": "2022-12-27T18:35:30.987862Z",
     "iopub.status.idle": "2022-12-27T18:35:30.995402Z",
     "shell.execute_reply": "2022-12-27T18:35:30.994354Z",
     "shell.execute_reply.started": "2022-12-27T18:35:30.988624Z"
    },
    "id": "IPz2XmGoK5uw",
    "outputId": "3c333a63-86a1-43d7-cfc1-b6787f6ee1a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 32)                224       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 16)                528       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 8)                 136       \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 4)                 36        \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 929\n",
      "Trainable params: 929\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:47:17.359902Z",
     "iopub.status.busy": "2022-12-27T18:47:17.359424Z",
     "iopub.status.idle": "2022-12-27T18:47:23.418354Z",
     "shell.execute_reply": "2022-12-27T18:47:23.417193Z",
     "shell.execute_reply.started": "2022-12-27T18:47:17.359866Z"
    },
    "id": "2hFSDV0oK5uw",
    "outputId": "15f1297b-d73d-4764-d75a-4b873e8a424c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "7/7 [==============================] - 2s 5ms/step - loss: 0.6892 - accuracy: 0.6009\n",
      "Epoch 2/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6836 - accuracy: 0.5561\n",
      "Epoch 3/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6800 - accuracy: 0.5695\n",
      "Epoch 4/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6746 - accuracy: 0.5561\n",
      "Epoch 5/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6705 - accuracy: 0.5561\n",
      "Epoch 6/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6661 - accuracy: 0.5605\n",
      "Epoch 7/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6618 - accuracy: 0.5650\n",
      "Epoch 8/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6581 - accuracy: 0.5830\n",
      "Epoch 9/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6536 - accuracy: 0.5874\n",
      "Epoch 10/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6497 - accuracy: 0.6054\n",
      "Epoch 11/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6462 - accuracy: 0.5874\n",
      "Epoch 12/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6426 - accuracy: 0.6099\n",
      "Epoch 13/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6391 - accuracy: 0.6233\n",
      "Epoch 14/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6367 - accuracy: 0.6368\n",
      "Epoch 15/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6347 - accuracy: 0.6502\n",
      "Epoch 16/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6324 - accuracy: 0.6637\n",
      "Epoch 17/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6302 - accuracy: 0.6637\n",
      "Epoch 18/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6295 - accuracy: 0.6547\n",
      "Epoch 19/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6282 - accuracy: 0.6592\n",
      "Epoch 20/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6271 - accuracy: 0.6726\n",
      "Epoch 21/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6258 - accuracy: 0.6682\n",
      "Epoch 22/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6251 - accuracy: 0.6771\n",
      "Epoch 23/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6239 - accuracy: 0.6816\n",
      "Epoch 24/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6233 - accuracy: 0.6771\n",
      "Epoch 25/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6225 - accuracy: 0.6771\n",
      "Epoch 26/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6225 - accuracy: 0.6816\n",
      "Epoch 27/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6219 - accuracy: 0.6682\n",
      "Epoch 28/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.6205 - accuracy: 0.6816\n",
      "Epoch 29/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6199 - accuracy: 0.6726\n",
      "Epoch 30/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6194 - accuracy: 0.6816\n",
      "Epoch 31/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6186 - accuracy: 0.6816\n",
      "Epoch 32/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6182 - accuracy: 0.6771\n",
      "Epoch 33/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6174 - accuracy: 0.6816\n",
      "Epoch 34/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6165 - accuracy: 0.6816\n",
      "Epoch 35/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6158 - accuracy: 0.6861\n",
      "Epoch 36/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6155 - accuracy: 0.6771\n",
      "Epoch 37/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6151 - accuracy: 0.6861\n",
      "Epoch 38/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6141 - accuracy: 0.6816\n",
      "Epoch 39/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6139 - accuracy: 0.6771\n",
      "Epoch 40/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6136 - accuracy: 0.6771\n",
      "Epoch 41/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6130 - accuracy: 0.6861\n",
      "Epoch 42/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6127 - accuracy: 0.6861\n",
      "Epoch 43/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6120 - accuracy: 0.6906\n",
      "Epoch 44/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6122 - accuracy: 0.6816\n",
      "Epoch 45/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6117 - accuracy: 0.6771\n",
      "Epoch 46/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6113 - accuracy: 0.6861\n",
      "Epoch 47/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6107 - accuracy: 0.6816\n",
      "Epoch 48/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6101 - accuracy: 0.6861\n",
      "Epoch 49/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6099 - accuracy: 0.6726\n",
      "Epoch 50/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6091 - accuracy: 0.6861\n",
      "Epoch 51/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6094 - accuracy: 0.6861\n",
      "Epoch 52/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6086 - accuracy: 0.6816\n",
      "Epoch 53/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6083 - accuracy: 0.6816\n",
      "Epoch 54/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6074 - accuracy: 0.6816\n",
      "Epoch 55/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6068 - accuracy: 0.6861\n",
      "Epoch 56/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6066 - accuracy: 0.6861\n",
      "Epoch 57/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6061 - accuracy: 0.6906\n",
      "Epoch 58/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6056 - accuracy: 0.6816\n",
      "Epoch 59/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6050 - accuracy: 0.6861\n",
      "Epoch 60/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6048 - accuracy: 0.6816\n",
      "Epoch 61/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.6034 - accuracy: 0.6816\n",
      "Epoch 62/250\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6033 - accuracy: 0.6771\n",
      "Epoch 63/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6035 - accuracy: 0.6816\n",
      "Epoch 64/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6026 - accuracy: 0.6816\n",
      "Epoch 65/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6017 - accuracy: 0.6816\n",
      "Epoch 66/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.6009 - accuracy: 0.6816\n",
      "Epoch 67/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.6011 - accuracy: 0.6771\n",
      "Epoch 68/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5997 - accuracy: 0.6771\n",
      "Epoch 69/250\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6001 - accuracy: 0.6816\n",
      "Epoch 70/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5981 - accuracy: 0.6771\n",
      "Epoch 71/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5978 - accuracy: 0.6816\n",
      "Epoch 72/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5974 - accuracy: 0.6771\n",
      "Epoch 73/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5976 - accuracy: 0.6861\n",
      "Epoch 74/250\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.5970 - accuracy: 0.6816\n",
      "Epoch 75/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5946 - accuracy: 0.6861\n",
      "Epoch 76/250\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.5948 - accuracy: 0.6861\n",
      "Epoch 77/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5939 - accuracy: 0.6861\n",
      "Epoch 78/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5930 - accuracy: 0.6951\n",
      "Epoch 79/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5927 - accuracy: 0.6861\n",
      "Epoch 80/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5929 - accuracy: 0.6861\n",
      "Epoch 81/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5911 - accuracy: 0.6861\n",
      "Epoch 82/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5921 - accuracy: 0.6861\n",
      "Epoch 83/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5916 - accuracy: 0.6951\n",
      "Epoch 84/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5897 - accuracy: 0.6861\n",
      "Epoch 85/250\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 0.5879 - accuracy: 0.6996\n",
      "Epoch 86/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5885 - accuracy: 0.6996\n",
      "Epoch 87/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5870 - accuracy: 0.6996\n",
      "Epoch 88/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5868 - accuracy: 0.6951\n",
      "Epoch 89/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5864 - accuracy: 0.6996\n",
      "Epoch 90/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5856 - accuracy: 0.7040\n",
      "Epoch 91/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5856 - accuracy: 0.6996\n",
      "Epoch 92/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5854 - accuracy: 0.6906\n",
      "Epoch 93/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5839 - accuracy: 0.6996\n",
      "Epoch 94/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5846 - accuracy: 0.7040\n",
      "Epoch 95/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5840 - accuracy: 0.6996\n",
      "Epoch 96/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5831 - accuracy: 0.6951\n",
      "Epoch 97/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5821 - accuracy: 0.7040\n",
      "Epoch 98/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5824 - accuracy: 0.7040\n",
      "Epoch 99/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5811 - accuracy: 0.7040\n",
      "Epoch 100/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5810 - accuracy: 0.6996\n",
      "Epoch 101/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5799 - accuracy: 0.6951\n",
      "Epoch 102/250\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.5803 - accuracy: 0.6951\n",
      "Epoch 103/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5796 - accuracy: 0.6951\n",
      "Epoch 104/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5789 - accuracy: 0.7040\n",
      "Epoch 105/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5784 - accuracy: 0.6996\n",
      "Epoch 106/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5796 - accuracy: 0.6951\n",
      "Epoch 107/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5775 - accuracy: 0.7085\n",
      "Epoch 108/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5792 - accuracy: 0.6996\n",
      "Epoch 109/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5783 - accuracy: 0.7085\n",
      "Epoch 110/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5765 - accuracy: 0.6996\n",
      "Epoch 111/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5785 - accuracy: 0.6996\n",
      "Epoch 112/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5774 - accuracy: 0.6996\n",
      "Epoch 113/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5773 - accuracy: 0.6996\n",
      "Epoch 114/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5748 - accuracy: 0.7040\n",
      "Epoch 115/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5755 - accuracy: 0.7040\n",
      "Epoch 116/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5738 - accuracy: 0.7130\n",
      "Epoch 117/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5737 - accuracy: 0.7130\n",
      "Epoch 118/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5730 - accuracy: 0.7130\n",
      "Epoch 119/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5727 - accuracy: 0.7040\n",
      "Epoch 120/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5736 - accuracy: 0.7085\n",
      "Epoch 121/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5718 - accuracy: 0.7130\n",
      "Epoch 122/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5728 - accuracy: 0.7040\n",
      "Epoch 123/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5717 - accuracy: 0.7130\n",
      "Epoch 124/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5717 - accuracy: 0.7130\n",
      "Epoch 125/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5720 - accuracy: 0.7085\n",
      "Epoch 126/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5696 - accuracy: 0.7040\n",
      "Epoch 127/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5728 - accuracy: 0.7040\n",
      "Epoch 128/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5721 - accuracy: 0.7085\n",
      "Epoch 129/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5705 - accuracy: 0.7130\n",
      "Epoch 130/250\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5724 - accuracy: 0.7040\n",
      "Epoch 131/250\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5718 - accuracy: 0.7040\n",
      "Epoch 132/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5695 - accuracy: 0.7130\n",
      "Epoch 133/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5687 - accuracy: 0.7040\n",
      "Epoch 134/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5672 - accuracy: 0.7040\n",
      "Epoch 135/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5673 - accuracy: 0.7220\n",
      "Epoch 136/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5680 - accuracy: 0.7040\n",
      "Epoch 137/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5667 - accuracy: 0.7085\n",
      "Epoch 138/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5660 - accuracy: 0.7175\n",
      "Epoch 139/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5681 - accuracy: 0.7040\n",
      "Epoch 140/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5663 - accuracy: 0.7085\n",
      "Epoch 141/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5641 - accuracy: 0.7175\n",
      "Epoch 142/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5644 - accuracy: 0.7175\n",
      "Epoch 143/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5636 - accuracy: 0.7130\n",
      "Epoch 144/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5641 - accuracy: 0.7085\n",
      "Epoch 145/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5635 - accuracy: 0.7130\n",
      "Epoch 146/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5640 - accuracy: 0.7175\n",
      "Epoch 147/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5645 - accuracy: 0.7085\n",
      "Epoch 148/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5633 - accuracy: 0.7130\n",
      "Epoch 149/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5620 - accuracy: 0.7085\n",
      "Epoch 150/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5611 - accuracy: 0.7175\n",
      "Epoch 151/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5614 - accuracy: 0.7175\n",
      "Epoch 152/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5613 - accuracy: 0.7085\n",
      "Epoch 153/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5614 - accuracy: 0.7265\n",
      "Epoch 154/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5609 - accuracy: 0.7175\n",
      "Epoch 155/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5629 - accuracy: 0.7265\n",
      "Epoch 156/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5592 - accuracy: 0.7220\n",
      "Epoch 157/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5596 - accuracy: 0.7130\n",
      "Epoch 158/250\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.5604 - accuracy: 0.7175\n",
      "Epoch 159/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5585 - accuracy: 0.7220\n",
      "Epoch 160/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5589 - accuracy: 0.7130\n",
      "Epoch 161/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5583 - accuracy: 0.7175\n",
      "Epoch 162/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5591 - accuracy: 0.7265\n",
      "Epoch 163/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5581 - accuracy: 0.7265\n",
      "Epoch 164/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5583 - accuracy: 0.7130\n",
      "Epoch 165/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5568 - accuracy: 0.7265\n",
      "Epoch 166/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5565 - accuracy: 0.7220\n",
      "Epoch 167/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5583 - accuracy: 0.7354\n",
      "Epoch 168/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5588 - accuracy: 0.7265\n",
      "Epoch 169/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5559 - accuracy: 0.7265\n",
      "Epoch 170/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5604 - accuracy: 0.7265\n",
      "Epoch 171/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5566 - accuracy: 0.7220\n",
      "Epoch 172/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5563 - accuracy: 0.7175\n",
      "Epoch 173/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5572 - accuracy: 0.7309\n",
      "Epoch 174/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5574 - accuracy: 0.7220\n",
      "Epoch 175/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5606 - accuracy: 0.7175\n",
      "Epoch 176/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5591 - accuracy: 0.7309\n",
      "Epoch 177/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5547 - accuracy: 0.7265\n",
      "Epoch 178/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5541 - accuracy: 0.7309\n",
      "Epoch 179/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5531 - accuracy: 0.7309\n",
      "Epoch 180/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5531 - accuracy: 0.7309\n",
      "Epoch 181/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5531 - accuracy: 0.7265\n",
      "Epoch 182/250\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.5529 - accuracy: 0.7309\n",
      "Epoch 183/250\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.5543 - accuracy: 0.7399\n",
      "Epoch 184/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5523 - accuracy: 0.7399\n",
      "Epoch 185/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5516 - accuracy: 0.7309\n",
      "Epoch 186/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5544 - accuracy: 0.7354\n",
      "Epoch 187/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5523 - accuracy: 0.7309\n",
      "Epoch 188/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5503 - accuracy: 0.7309\n",
      "Epoch 189/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5505 - accuracy: 0.7354\n",
      "Epoch 190/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5501 - accuracy: 0.7354\n",
      "Epoch 191/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5501 - accuracy: 0.7399\n",
      "Epoch 192/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5542 - accuracy: 0.7309\n",
      "Epoch 193/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5528 - accuracy: 0.7399\n",
      "Epoch 194/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5501 - accuracy: 0.7354\n",
      "Epoch 195/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5500 - accuracy: 0.7354\n",
      "Epoch 196/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5486 - accuracy: 0.7309\n",
      "Epoch 197/250\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.5482 - accuracy: 0.7309\n",
      "Epoch 198/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5502 - accuracy: 0.7354\n",
      "Epoch 199/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5506 - accuracy: 0.7399\n",
      "Epoch 200/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5470 - accuracy: 0.7354\n",
      "Epoch 201/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5504 - accuracy: 0.7399\n",
      "Epoch 202/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5477 - accuracy: 0.7265\n",
      "Epoch 203/250\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.5471 - accuracy: 0.7309\n",
      "Epoch 204/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5471 - accuracy: 0.7444\n",
      "Epoch 205/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5445 - accuracy: 0.7444\n",
      "Epoch 206/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5453 - accuracy: 0.7399\n",
      "Epoch 207/250\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5461 - accuracy: 0.7354\n",
      "Epoch 208/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5476 - accuracy: 0.7354\n",
      "Epoch 209/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5487 - accuracy: 0.7220\n",
      "Epoch 210/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5447 - accuracy: 0.7399\n",
      "Epoch 211/250\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.5445 - accuracy: 0.7354\n",
      "Epoch 212/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5459 - accuracy: 0.7354\n",
      "Epoch 213/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5449 - accuracy: 0.7399\n",
      "Epoch 214/250\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5471 - accuracy: 0.7354\n",
      "Epoch 215/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5435 - accuracy: 0.7309\n",
      "Epoch 216/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5424 - accuracy: 0.7399\n",
      "Epoch 217/250\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.5433 - accuracy: 0.7444\n",
      "Epoch 218/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5409 - accuracy: 0.7399\n",
      "Epoch 219/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5411 - accuracy: 0.7354\n",
      "Epoch 220/250\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.5398 - accuracy: 0.7399\n",
      "Epoch 221/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5393 - accuracy: 0.7444\n",
      "Epoch 222/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5411 - accuracy: 0.7354\n",
      "Epoch 223/250\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.5400 - accuracy: 0.7444\n",
      "Epoch 224/250\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.5388 - accuracy: 0.7399\n",
      "Epoch 225/250\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.5409 - accuracy: 0.7354\n",
      "Epoch 226/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5400 - accuracy: 0.7399\n",
      "Epoch 227/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5429 - accuracy: 0.7399\n",
      "Epoch 228/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5412 - accuracy: 0.7444\n",
      "Epoch 229/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5374 - accuracy: 0.7444\n",
      "Epoch 230/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5366 - accuracy: 0.7444\n",
      "Epoch 231/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5363 - accuracy: 0.7444\n",
      "Epoch 232/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5359 - accuracy: 0.7399\n",
      "Epoch 233/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5350 - accuracy: 0.7489\n",
      "Epoch 234/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5357 - accuracy: 0.7399\n",
      "Epoch 235/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5344 - accuracy: 0.7444\n",
      "Epoch 236/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5371 - accuracy: 0.7444\n",
      "Epoch 237/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5359 - accuracy: 0.7399\n",
      "Epoch 238/250\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.5341 - accuracy: 0.7399\n",
      "Epoch 239/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5351 - accuracy: 0.7578\n",
      "Epoch 240/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5366 - accuracy: 0.7309\n",
      "Epoch 241/250\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.5345 - accuracy: 0.7444\n",
      "Epoch 242/250\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5332 - accuracy: 0.7534\n",
      "Epoch 243/250\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.5324 - accuracy: 0.7489\n",
      "Epoch 244/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5327 - accuracy: 0.7489\n",
      "Epoch 245/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5309 - accuracy: 0.7489\n",
      "Epoch 246/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5302 - accuracy: 0.7489\n",
      "Epoch 247/250\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 0.5308 - accuracy: 0.7489\n",
      "Epoch 248/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5302 - accuracy: 0.7444\n",
      "Epoch 249/250\n",
      "7/7 [==============================] - 0s 6ms/step - loss: 0.5312 - accuracy: 0.7578\n",
      "Epoch 250/250\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.5280 - accuracy: 0.7578\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f42e5ee0700>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=250)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XwP76wwiK5uw"
   },
   "source": [
    "We compiled the model with an appropriate optimizer, a loss function, and an evaluation metric. After compiling the model, we fit it on the training set, and we set the epoch count to 250. We can see that the accuracy improved over the 250 epochs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w6VAo-ZNK5uw"
   },
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:49:59.562231Z",
     "iopub.status.busy": "2022-12-27T18:49:59.561787Z",
     "iopub.status.idle": "2022-12-27T18:49:59.828505Z",
     "shell.execute_reply": "2022-12-27T18:49:59.827266Z",
     "shell.execute_reply.started": "2022-12-27T18:49:59.562197Z"
    },
    "id": "k2GEaWbtK5ux",
    "outputId": "90ff685a-f1bb-467f-97fe-9f03d1c0b6ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 0.5276 - accuracy: 0.7489\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5275814533233643, 0.7488788962364197]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-12-27T18:50:03.037461Z",
     "iopub.status.busy": "2022-12-27T18:50:03.036767Z",
     "iopub.status.idle": "2022-12-27T18:50:03.252629Z",
     "shell.execute_reply": "2022-12-27T18:50:03.251550Z",
     "shell.execute_reply.started": "2022-12-27T18:50:03.037425Z"
    },
    "id": "Rv1vr_EdK5ux",
    "outputId": "ffc7f898-ff5d-4cf6-96e2-36f27d5ae678"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 4ms/step - loss: 0.6419 - accuracy: 0.7396\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6418974995613098, 0.7395833134651184]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2vxOAc9uK5ux"
   },
   "source": [
    "The model evaluation output shows the performance of the model on both training and test data. The accuracy was approximately 75% on the training data and 74% on the test data. Ideally, the higher the accuracy value, the better the model is performing. It's noteworthy that the training and test set accuracies are close to each other, which shows that there is consistency and that the accuracy doesn't drop too much when we test the model on unseen data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M_sIj5bVK5ux"
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "In this project, we have built Deep Learning Classification models using the deep learning framework, Keras, in TensorFlow. We used a real-world IPO dataset and built a classifier algorithm to predict whether an IPO will list at profit or not.\n",
    "\n",
    "We used the Sequential API to build the model, which is achieving a decent accuracy of 75% and 74% on training and test data, respectively. We see that the accuracy is consistent across the training and test datasets, which is a promising sign. \n",
    "\n",
    "This is just one of the many ways to model this solution — you can try out different combinations to further improve model performance."
   ]
  }
 ],
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