Hackthon Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Installing specfied Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:36.060445Z",
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     "iopub.status.idle": "2022-02-13T08:08:37.156521Z",
     "shell.execute_reply": "2022-02-13T08:08:37.155801Z",
     "shell.execute_reply.started": "2022-02-13T08:08:36.060413Z"
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   },
   "outputs": [],
   "source": [
    "# Essentials\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import random\n",
    "\n",
    "# Plots\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Models\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import mean_absolute_error as mae\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:37.158259Z",
     "iopub.status.busy": "2022-02-13T08:08:37.158006Z",
     "iopub.status.idle": "2022-02-13T08:08:37.329364Z",
     "shell.execute_reply": "2022-02-13T08:08:37.328679Z",
     "shell.execute_reply.started": "2022-02-13T08:08:37.158224Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((89197, 10), (11121, 9))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv(\"../input/traindata/train_0OECtn8.csv\")\n",
    "test=pd.read_csv(\"../input/testdata/test_1zqHu22.csv\")\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:37.330865Z",
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     "iopub.status.idle": "2022-02-13T08:08:37.355030Z",
     "shell.execute_reply": "2022-02-13T08:08:37.354360Z",
     "shell.execute_reply.started": "2022-02-13T08:08:37.330825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>profession</th>\n",
       "      <th>followers</th>\n",
       "      <th>views</th>\n",
       "      <th>engagement_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19990</td>\n",
       "      <td>37</td>\n",
       "      <td>128</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>180</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5304</td>\n",
       "      <td>32</td>\n",
       "      <td>132</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>330</td>\n",
       "      <td>714</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1840</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>180</td>\n",
       "      <td>138</td>\n",
       "      <td>4.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>12597</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>220</td>\n",
       "      <td>613</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>13626</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>220</td>\n",
       "      <td>613</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89192</th>\n",
       "      <td>89193</td>\n",
       "      <td>23996</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other</td>\n",
       "      <td>340</td>\n",
       "      <td>662</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89193</th>\n",
       "      <td>89194</td>\n",
       "      <td>20466</td>\n",
       "      <td>20</td>\n",
       "      <td>47</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other</td>\n",
       "      <td>240</td>\n",
       "      <td>892</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89194</th>\n",
       "      <td>89195</td>\n",
       "      <td>13655</td>\n",
       "      <td>16</td>\n",
       "      <td>97</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>270</td>\n",
       "      <td>462</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89195</th>\n",
       "      <td>89196</td>\n",
       "      <td>24840</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>230</td>\n",
       "      <td>819</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89196</th>\n",
       "      <td>89197</td>\n",
       "      <td>27183</td>\n",
       "      <td>25</td>\n",
       "      <td>150</td>\n",
       "      <td>13</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>240</td>\n",
       "      <td>317</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89197 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       row_id  user_id  category_id  video_id  age  gender  \\\n",
       "0           1    19990           37       128   24    Male   \n",
       "1           2     5304           32       132   14  Female   \n",
       "2           3     1840           12        24   19    Male   \n",
       "3           4    12597           23       112   19    Male   \n",
       "4           5    13626           23       112   27    Male   \n",
       "...       ...      ...          ...       ...  ...     ...   \n",
       "89192   89193    23996           15        32   25    Male   \n",
       "89193   89194    20466           20        47   31    Male   \n",
       "89194   89195    13655           16        97   25    Male   \n",
       "89195   89196    24840            9        18   35    Male   \n",
       "89196   89197    27183           25       150   13    Male   \n",
       "\n",
       "                 profession  followers  views  engagement_score  \n",
       "0                   Student        180   1000              4.33  \n",
       "1                   Student        330    714              1.79  \n",
       "2                   Student        180    138              4.35  \n",
       "3                   Student        220    613              3.77  \n",
       "4      Working Professional        220    613              3.13  \n",
       "...                     ...        ...    ...               ...  \n",
       "89192                 Other        340    662              3.91  \n",
       "89193                 Other        240    892              3.56  \n",
       "89194               Student        270    462              4.23  \n",
       "89195  Working Professional        230    819              3.77  \n",
       "89196               Student        240    317              4.31  \n",
       "\n",
       "[89197 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA ON DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data type and Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:37.357082Z",
     "iopub.status.busy": "2022-02-13T08:08:37.356844Z",
     "iopub.status.idle": "2022-02-13T08:08:37.366191Z",
     "shell.execute_reply": "2022-02-13T08:08:37.365476Z",
     "shell.execute_reply.started": "2022-02-13T08:08:37.357049Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_id                int64\n",
       "user_id               int64\n",
       "category_id           int64\n",
       "video_id              int64\n",
       "age                   int64\n",
       "gender               object\n",
       "profession           object\n",
       "followers             int64\n",
       "views                 int64\n",
       "engagement_score    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:37.367866Z",
     "iopub.status.busy": "2022-02-13T08:08:37.367521Z",
     "iopub.status.idle": "2022-02-13T08:08:37.394957Z",
     "shell.execute_reply": "2022-02-13T08:08:37.394259Z",
     "shell.execute_reply.started": "2022-02-13T08:08:37.367830Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_id              0\n",
       "user_id             0\n",
       "category_id         0\n",
       "video_id            0\n",
       "age                 0\n",
       "gender              0\n",
       "profession          0\n",
       "followers           0\n",
       "views               0\n",
       "engagement_score    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:37.396627Z",
     "iopub.status.busy": "2022-02-13T08:08:37.396156Z",
     "iopub.status.idle": "2022-02-13T08:08:37.410230Z",
     "shell.execute_reply": "2022-02-13T08:08:37.409410Z",
     "shell.execute_reply.started": "2022-02-13T08:08:37.396591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5198     10\n",
       "13218    10\n",
       "1448     10\n",
       "13410    10\n",
       "7157     10\n",
       "         ..\n",
       "21507     2\n",
       "4404      2\n",
       "21430     2\n",
       "17541     2\n",
       "11279     2\n",
       "Name: user_id, Length: 27734, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"user_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T06:56:33.287795Z",
     "iopub.status.busy": "2022-02-13T06:56:33.287225Z",
     "iopub.status.idle": "2022-02-13T06:56:33.291559Z",
     "shell.execute_reply": "2022-02-13T06:56:33.290978Z",
     "shell.execute_reply.started": "2022-02-13T06:56:33.287754Z"
    }
   },
   "outputs": [],
   "source": [
    "#train['user_id'].value_counts().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:37.425374Z",
     "iopub.status.busy": "2022-02-13T08:08:37.425166Z",
     "iopub.status.idle": "2022-02-13T08:08:37.432103Z",
     "shell.execute_reply": "2022-02-13T08:08:37.431339Z",
     "shell.execute_reply.started": "2022-02-13T08:08:37.425350Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     8104\n",
       "8     6313\n",
       "25    4880\n",
       "19    4679\n",
       "21    4043\n",
       "12    3889\n",
       "11    3766\n",
       "4     3678\n",
       "34    3297\n",
       "16    3264\n",
       "13    2777\n",
       "32    2712\n",
       "23    2671\n",
       "18    2241\n",
       "42    2178\n",
       "15    2164\n",
       "9     1886\n",
       "7     1885\n",
       "31    1855\n",
       "3     1845\n",
       "1     1810\n",
       "6     1399\n",
       "39    1339\n",
       "28    1309\n",
       "10    1217\n",
       "17    1208\n",
       "26    1156\n",
       "27    1103\n",
       "36    1025\n",
       "43    1019\n",
       "35     986\n",
       "37     962\n",
       "29     916\n",
       "20     702\n",
       "30     548\n",
       "41     545\n",
       "14     536\n",
       "38     534\n",
       "24     524\n",
       "33     517\n",
       "40     458\n",
       "22     366\n",
       "46     236\n",
       "45     227\n",
       "44     214\n",
       "2      167\n",
       "47      47\n",
       "Name: category_id, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"category_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:42.293073Z",
     "iopub.status.busy": "2022-02-13T08:08:42.292522Z",
     "iopub.status.idle": "2022-02-13T08:08:42.300433Z",
     "shell.execute_reply": "2022-02-13T08:08:42.299810Z",
     "shell.execute_reply.started": "2022-02-13T08:08:42.293023Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112    1337\n",
       "53     1334\n",
       "1      1282\n",
       "65     1103\n",
       "42     1077\n",
       "       ... \n",
       "165      78\n",
       "171      78\n",
       "174      70\n",
       "166      66\n",
       "175      47\n",
       "Name: video_id, Length: 175, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"video_id\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Engagement_score: the variable we're trying to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:44.337387Z",
     "iopub.status.busy": "2022-02-13T08:08:44.336730Z",
     "iopub.status.idle": "2022-02-13T08:08:44.978622Z",
     "shell.execute_reply": "2022-02-13T08:08:44.977963Z",
     "shell.execute_reply.started": "2022-02-13T08:08:44.337347Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_color_codes(palette='deep')\n",
    "f, ax = plt.subplots(figsize=(8, 7))\n",
    "#Check the new distribution \n",
    "sns.distplot(train['engagement_score'], color=\"b\");\n",
    "ax.xaxis.grid(False)\n",
    "ax.set(ylabel=\"Frequency\")\n",
    "ax.set(xlabel=\"engagement_score\")\n",
    "ax.set(title=\"ES distribution\")\n",
    "sns.despine(trim=True, left=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:49.389646Z",
     "iopub.status.busy": "2022-02-13T08:08:49.389209Z",
     "iopub.status.idle": "2022-02-13T08:08:49.398669Z",
     "shell.execute_reply": "2022-02-13T08:08:49.397869Z",
     "shell.execute_reply.started": "2022-02-13T08:08:49.389610Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: -0.855630\n",
      "Kurtosis: 0.640584\n"
     ]
    }
   ],
   "source": [
    "# Skew and kurt\n",
    "print(\"Skewness: %f\" % train['engagement_score'].skew())\n",
    "print(\"Kurtosis: %f\" % train['engagement_score'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting category data to numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:51.984224Z",
     "iopub.status.busy": "2022-02-13T08:08:51.983694Z",
     "iopub.status.idle": "2022-02-13T08:08:52.019812Z",
     "shell.execute_reply": "2022-02-13T08:08:52.018963Z",
     "shell.execute_reply.started": "2022-02-13T08:08:51.984189Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      52397\n",
      "Female    36800\n",
      "Name: gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train['gender'].value_counts())\n",
    "dummies = pd.get_dummies(train['gender'],drop_first=True)\n",
    "train_data = pd.concat([train.drop('gender',axis=1),dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:14:01.668097Z",
     "iopub.status.busy": "2022-02-13T08:14:01.667561Z",
     "iopub.status.idle": "2022-02-13T08:14:01.679365Z",
     "shell.execute_reply": "2022-02-13T08:14:01.678673Z",
     "shell.execute_reply.started": "2022-02-13T08:14:01.668060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>age</th>\n",
       "      <th>followers</th>\n",
       "      <th>views</th>\n",
       "      <th>engagement_score</th>\n",
       "      <th>Male</th>\n",
       "      <th>Student</th>\n",
       "      <th>Working Professional</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19990</td>\n",
       "      <td>37</td>\n",
       "      <td>128</td>\n",
       "      <td>24</td>\n",
       "      <td>180</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5304</td>\n",
       "      <td>32</td>\n",
       "      <td>132</td>\n",
       "      <td>14</td>\n",
       "      <td>330</td>\n",
       "      <td>714</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1840</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>180</td>\n",
       "      <td>138</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12597</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>19</td>\n",
       "      <td>220</td>\n",
       "      <td>613</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13626</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>27</td>\n",
       "      <td>220</td>\n",
       "      <td>613</td>\n",
       "      <td>3.13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  category_id  video_id  age  followers  views  engagement_score  \\\n",
       "0    19990           37       128   24        180   1000              4.33   \n",
       "1     5304           32       132   14        330    714              1.79   \n",
       "2     1840           12        24   19        180    138              4.35   \n",
       "3    12597           23       112   19        220    613              3.77   \n",
       "4    13626           23       112   27        220    613              3.13   \n",
       "\n",
       "   Male  Student  Working Professional  \n",
       "0     1        1                     0  \n",
       "1     0        1                     0  \n",
       "2     1        1                     0  \n",
       "3     1        1                     0  \n",
       "4     1        0                     1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()    # 1- male ,0 - Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:08:56.913253Z",
     "iopub.status.busy": "2022-02-13T08:08:56.912490Z",
     "iopub.status.idle": "2022-02-13T08:08:56.949319Z",
     "shell.execute_reply": "2022-02-13T08:08:56.948481Z",
     "shell.execute_reply.started": "2022-02-13T08:08:56.913214Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student                 44638\n",
      "Other                   26840\n",
      "Working Professional    17719\n",
      "Name: profession, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['profession'].value_counts())\n",
    "dummies = pd.get_dummies(train_data['profession'],drop_first=True)\n",
    "train_data = pd.concat([train_data.drop('profession',axis=1),dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:14:10.443426Z",
     "iopub.status.busy": "2022-02-13T08:14:10.442831Z",
     "iopub.status.idle": "2022-02-13T08:14:10.454765Z",
     "shell.execute_reply": "2022-02-13T08:14:10.454040Z",
     "shell.execute_reply.started": "2022-02-13T08:14:10.443388Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>age</th>\n",
       "      <th>followers</th>\n",
       "      <th>views</th>\n",
       "      <th>engagement_score</th>\n",
       "      <th>Male</th>\n",
       "      <th>Student</th>\n",
       "      <th>Working Professional</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89192</th>\n",
       "      <td>23996</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>340</td>\n",
       "      <td>662</td>\n",
       "      <td>3.91</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89193</th>\n",
       "      <td>20466</td>\n",
       "      <td>20</td>\n",
       "      <td>47</td>\n",
       "      <td>31</td>\n",
       "      <td>240</td>\n",
       "      <td>892</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89194</th>\n",
       "      <td>13655</td>\n",
       "      <td>16</td>\n",
       "      <td>97</td>\n",
       "      <td>25</td>\n",
       "      <td>270</td>\n",
       "      <td>462</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89195</th>\n",
       "      <td>24840</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>230</td>\n",
       "      <td>819</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89196</th>\n",
       "      <td>27183</td>\n",
       "      <td>25</td>\n",
       "      <td>150</td>\n",
       "      <td>13</td>\n",
       "      <td>240</td>\n",
       "      <td>317</td>\n",
       "      <td>4.31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  category_id  video_id  age  followers  views  \\\n",
       "89192    23996           15        32   25        340    662   \n",
       "89193    20466           20        47   31        240    892   \n",
       "89194    13655           16        97   25        270    462   \n",
       "89195    24840            9        18   35        230    819   \n",
       "89196    27183           25       150   13        240    317   \n",
       "\n",
       "       engagement_score  Male  Student  Working Professional  \n",
       "89192              3.91     1        0                     0  \n",
       "89193              3.56     1        0                     0  \n",
       "89194              4.23     1        1                     0  \n",
       "89195              3.77     1        0                     1  \n",
       "89196              4.31     1        1                     0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail()    # 10 -student,01-wp,00-others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:15:55.196371Z",
     "iopub.status.busy": "2022-02-13T08:15:55.195745Z",
     "iopub.status.idle": "2022-02-13T08:15:56.037981Z",
     "shell.execute_reply": "2022-02-13T08:15:56.037332Z",
     "shell.execute_reply.started": "2022-02-13T08:15:55.196335Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = train_data, x = \"age\", kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:16:27.335940Z",
     "iopub.status.busy": "2022-02-13T08:16:27.335404Z",
     "iopub.status.idle": "2022-02-13T08:16:28.035036Z",
     "shell.execute_reply": "2022-02-13T08:16:28.034365Z",
     "shell.execute_reply.started": "2022-02-13T08:16:27.335903Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='followers', ylabel='Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = train_data, x = \"followers\", kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:17:13.988956Z",
     "iopub.status.busy": "2022-02-13T08:17:13.988376Z",
     "iopub.status.idle": "2022-02-13T08:17:14.583999Z",
     "shell.execute_reply": "2022-02-13T08:17:14.583307Z",
     "shell.execute_reply.started": "2022-02-13T08:17:13.988916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='views', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ObGws0NC9NWrUKKsWJoQQovW7ZoicOXOGCxcu8Kc//YkNGzawe/duAIYMGcK9995rkwKFEEK0XtfszlqwYAHu7u5Aw5nls2fPZvbs2ej1ehYsWGCTAoUQQrRe1wyRCxcu0K9fv0bt/fr1Iy8vz2pFCSGEaBuuGSKlpaVXva2ysrLFixFCCNG2XDNEBgwYwIoVKxq1r1y5ktDQUKsVJYQQom245sD6Sy+9xHPPPcd3331nDo0DBw5QU1PD4sWLbVKgEEKI1uuaIeLn58dXX33Ftm3bOH78OAB33nkn4eHhNilOCCFE69as80RGjhzJyJEjrV2LEEKINsai64kIIYQQICEihBDiBkiICCGEsJiEiBBCCItJiAghhLCYhIgQQgiL2SVESkpKmDZtGmPGjGHs2LHs3buX4uJipkyZQnR0NFOmTMFkMgEN1zKZN28eer2eCRMmcPDgQfPzJCYmEh0dTXR0NImJifbYFCGE6NDsEiLz58/njjvu4Pvvv+ebb76hV69eLF26lPDwcDZs2EB4eDhLly4FIC0tjezsbDZs2MAbb7zB3LlzASguLmbx4sWsWLGClStXsnjxYnPwCCGEsA2bh0hpaSk7d+4kPj4eAGdnZzw9PUlNTSUuLg6AuLg4Nm7cCGBuVxSFIUOGUFJSQkFBARkZGURERODt7Y2XlxcRERGkp6fbenOEnZVX1VJYVtXkn/KqWnuXJ0S716wz1ltSbm4uPj4+zJ49myNHjhAaGsrLL79MYWEhAQEBAPj7+1NYWAiA0WhEp9OZH6/T6TAajY3atVotRqPRthsj7K6ypo7lW880edsj4cF0drH5R1yIDsXmRyK1tbUcOnSIBx98kKSkJNzc3MxdV5cpioKiKLYuTQghxHWyeYjodDp0Oh2DBw8GYMyYMRw6dAhfX18KCgoAKCgowMfHB2g4wjAYDObHGwwGtFpto3aj0YhWq7XhlgghhLB5iPj7+6PT6Th16hQAW7dupVevXkRFRZGUlARAUlISo0ePBjC3q6pKZmYmHh4eBAQEEBkZSUZGBiaTCZPJREZGBpGRkbbeHLuQcQAhRGthlw7jV155hT/+8Y/U1NTQvXt33nzzTerr65k+fTqrVq0iKCiIRYsWAQ1Lz2/ZsgW9Xo+bm5v52u7e3t48++yz5gH6qVOn4u3tbY/NsTkZBxBCtBZ2+bYJCQlhzZo1jdo//fTTRm2KovDqq682+Tzx8fHmEBFCCGF7csa6EEIIi0mICCGEsJiEiBBCCItJiAghhLCYhIgQQgiLyVxQIVpAeVUtlTV1Td7m6uQg065FuyWfbCFaQHs5d+fk+TK2HDuPm5MDfbUePBIebO+SRCsn3VlCCAAO55eQnJWPi6MGB41CxokLrNiVY++yRCsnISKEoOhSNT8eLSDI2437h3Unfmg3bvbrzF9TjrL7zEV7lydaMQkR0WrImmD2s3zrGWrqVO7u54+TgwaNojAmVEeXTs68+8Mxe5cnWrG20VErmk2jKBSWVTVqbwuDu+1lXKGtKSit5OudOfTXeeDr7mJud3bU8Eh4MO9tPM6u7IsM6+FjxypFayX/K9uZ6tp6vthxtlG7fAmLq1m9O4+q2npG3Nw4JCbf1o3lW8/wf5tO8OljI+xQnWjtpDtLiA7um8w8BnXzoksn50a3uTk78Eh4D7YcO09u0SU7VCdaOwkRITqwY8ZSjhhKGROqu+p9Jg/tCsCaPXm2Kku0IRIiQnRg32aeQ6PAPSEBV71Pty6duL2XL6t256Kqqg2rax/a+4QR6SQXogNL3p9PRG+/KwbUmxI/tBszV+xjx+mLhPX0tVF17UN7nzAiRyJCdFDZF8o5faGce0K0v3rfMQN0uDppWJuVb4PKRFsiISJEB5V2/DwAd/b1/9X7dnJ25O5+AXx/0EBdvXRpif+wW4jU1dURFxfH73//ewBycnJISEhAr9czffp0qqurAaiurmb69Ono9XoSEhLIzc01P8eSJUvQ6/XExMSQnp5ul+0Qoq3afPQ8wb6d6OHXuVn3HzswkPOlVezKljPYxX/YLUSWL19Or169zP9euHAhjz76KD/88AOenp6sWrUKgJUrV+Lp6ckPP/zAo48+ysKFCwE4ceIEycnJJCcns2zZMl577TXq6ppeRVUIcaXKmjq2nixs1lHIZVH9A3Bx1LD+gMGKlYm2xi4hYjAY2Lx5M/Hx8QCoqsq2bduIiYkBYOLEiaSmpgKwadMmJk6cCEBMTAxbt25FVVVSU1OJjY3F2dmZ7t27ExwcTFZWlj02R4g2Z1d2ERU1ddzVr/kh4u7iyJ19/Vl/IJ966dIS/2aXEFmwYAGzZs1Co2l4+aKiIjw9PXF0bJiloNPpMBqNABiNRgIDAwFwdHTEw8ODoqIijEYjOt1/5rZrtVrzY4QQ1/bzyQs4aBTCbr6+mVbjBgZiLKliz9kiK1Um2hqbh8iPP/6Ij48PAwYMsPVLCyH+bcfpiwzs6nXd00tHhwTg7KBh3X7p0hINbD5Bec+ePWzatIm0tDSqqqooKytj/vz5lJSUUFtbi6OjIwaDAa22YdqhVqslPz8fnU5HbW0tpaWldOnSBa1Wi8Hwnw+y0Wg0P0YIcXUV1XXsyy3mscibr/uxHq5OjOrrx/oD+fwlNgSNRrFChaItsfmRyB/+8AfS0tLYtGkT7777LiNHjuSdd94hLCyMlJQUABITE4mKigIgKiqKxMREAFJSUhg5ciSKohAVFUVycjLV1dXk5OSQnZ3NoEGDbL05QrQ5e88WUVOnEtbEgovNMW5gIPmmSjJzi1u2MNEmtZrzRGbNmsXHH3+MXq+nuLiYhIQEAOLj4ykuLkav1/Pxxx/zxz/+EYA+ffowduxYxo0bxxNPPMGcOXNwcHCw5yYI0SZsP30RjYLFS7uPDtHi5KCwfr+ceCjsvOxJWFgYYWFhAHTv3t08rfeXXFxceP/995t8/DPPPMMzzzxj1RqFaG+2ny7kliBPPF2dLHq8l5sTkb39WLffwEvjQlAU+3ZplVfVUlnT9PT+tnAdnbZO3l0hOpCq2jr2ni3mt2HBN/Q84wYG8uPRLLJyTQzu7t0yxVmova9N1drJuytEB5KVa6Kqtp6wnjd2lUL9LVocNQrrDuTbPURamhzZXB95N4ToQLafKgRgxA1e6ta7kzO39/Zj/X4Dfx7T3+5dWi1JjmyuT6sZWBdCWN/20xfpr/OgS+fGVzG8XrEDdZy9eImD50paoDLRVkmICNFB1NTVs/tMUZPXUreE/hYdDhqFdTJLq0OTEBGigzh4roRL1XXXvdTJ1fh0dia8py/r9ufLFQ87MAkRIToI83hICx2JQMMsrezCSxzOL22x5xRti4wQtRO1dfUUXaphZ/ZFDKZKunR2wsWx+SdfqqrK/jwTGw4aOX2hnHpVZWhwF27vJZdCbS+2nSqkd4A7/h7XvhTu9YgO1fKXpP18u+8ctwR5NusxV5v9JDOf2ibZY23chbIqdp8p4nhBGXX1Kl/sOGu+zc/dmUAvN4K8XMktuoRPZ+crZtFU1tSx+0wRGw4a2HDISL6pEgeNQrBvJ+rqVdYfMODm5EBkbz9CAj3a1Qycjqa2rp6d2UXE3RrUos/r5+7C6BAtK3flMEPfp1k/XK42+0lmPrVNssfaKFVV2ZldxPbThThqNNwS6ElXbzfuHRzE+gP5FJRWkW+q5KihlP15JlIOGfFzd6GHbydcnRy4UFbFqfPlVNfV4+qkYVQff/4Y3Y+o/gHmmTunzpfx4qosfjhspLSyhrCeclTSVh08V0JZVS0jrbAP/2dkMD8cMpJy0Mi9g1s2pETrJyHSBtXU1bPhkJEjhlL6at25q18Abk4NvwCHBnfhqLGUnv7uANSrKoVl1QT7duKooRRDSSXl1bV09Xbjzr7+jLjZh9t7+eHm3PgXZE9/d/7+29v43Uc72Hb6Il6dnOiva16XhWhdtv17PKSlBtV/6Y7eftzk04nPtp2REOmAJETaGFVVefXbgxwxlBLey5fhwV2u2c2kURT8PVyIH9oNX/fr7wt30CiMDtFSUllL6uECAr3c8HKzbM0lYT/WGA+5TKNR+J+RN7Fg3REyc4oZ0s7OYG9rbD3mJLOz2phFG4+TctDI7b18GdHDxybjFA4ahZhQLYoCPx4pkOmcbczl8ZCRN7jUybU8FBaMdycn3k89brXXEM1zeczpv/9cbSmXGyUh0oakHjbyt9TjTBgUyLDgLjZ9bQ9XJ27v5ceZi5c4XlBm09cWN8aa4yGXubs48kTkzWw6UkCWXGekQ5HurDaioKSSWauyCAn05KVxIXy1M8fmNQzq5sXBcyZ+PllIr3+PudhKUXk1B86VcCDPRM7FS+SbKjGYKimrqqW8qhZ3V0cCPFzo6edOkLerzCT7BWuOh/zS727vwbKM08xPPsxXT42UfdBBSIi0Aaqq8qfVWZRX1fL+A0NwdrTPAaRGUQjv5ct3+/I5ZIP1kgrLqjh4roRTF8r52y+6SfzcXQj0cuUm3044aRSOF5RRWlnLvhwTe84WE+DhQkRvP6vX11ZYczzklzxcnXgxpj8vJe5n9Z484od2s+rridZBQqQN+HbfOX48ep5Xxt9CH60HhWVVdqvlZt/OBHq5sj27kKpa6/SxllTWkHbsPCfPl+OgKHT3cWNKRA9G3OxDaJDXFQP7hWVV5nMOqmvrOWYsZWf2RRL35uHiqOH1+wa0eOgeN5by8c/ZZJ4tRqOB227qwm+GdW/R12gp1jo/5GoeGN6d1XtyWbDuMHf08UPr6WqT171e11ruvV7G/K6LhEgrV3ypmte/O8Tgbl48ensPe5eDoiiE9/Rlzd48vsk8xzN39W7R5z9uLGXj4QJUGq4BPri7N25ODjw8MvhXZ5c5O2oY0NWL/joPfjpRyFc7czhTeIl/PDy0RWaU1dbV89eUoyzLOI2ro4bbgrtQr6p8vTOH1XtyuaO3P/10Hjf8Oi3JFuMhv6TRKLw1eSD3Lv6JZz7bzZdPjbyulRNs5VrLvT804ibz5BHpkvt1Ng+R/Px8XnzxRQoLC1EUhfvvv5/f/e53FBcXM2PGDPLy8ujatSuLFi3Cy8sLVVWZP38+W7ZswdXVlf/93/8lNDQUgMTERD744AOg4VK5EydOtPXmWN385MMUV9Twr8fDcNC0jg90ty4NZ8F/8lM2j0Xe3GJfEl/uOMu6AwYCvVyJCdVZ/MXv6KDhzn7+TB7aldfXHuI3S7by+RNhFk1xvqyksoan/7Wbn08WMvHWrky9uxddOjWclJlXVMGcbw/w/UEDDhqF3gG2HS+6lp9P2mY85Jd6B3jwTsJgnvl8Dy+uyuLd+4e0ms9uU+rqVXKKLpFz8RIGUyXLt2ZTXFGDqoKzgwbvTk74ubsQ4OHC/u7ehPf2/dXPfEllDceNpRhMVRRdqiavuAJ/dxe7dUVbk81DxMHBgT//+c+EhoZSVlbG5MmTiYiIYM2aNYSHh/PUU0+xdOlSli5dyqxZs0hLSyM7O5sNGzawb98+5s6dy8qVKykuLmbx4sWsXr0aRVGYNGkSUVFReHl52XqTrObnExdYuTuXZ+7q1ex1iWxBURRG3OxDUuY5Vu/O46Gwm274OT/bdoaFG47Ry78zY0J1ODrc+H+2cQMD6eHXmSc+3cWD/9zG50+MtGhcwFRRwyMfbufAuRLuCQngJp9OfLfvyuXP//abW3ngn9v4/qCBeNdu6FpJN86WYwWEBHpaZTzkWl1Ct/f2Y+rdvfh/P56ksqaO1+4Nxenf+7S1dBedL63iUH4JRw2lVNTU4aBRCPBwYVRff84VV6AoClU1dRRdquHUhTIO5Zew+dj5hiPeIE9CAj3p2sUNNycHqmrrOV9axfGCMo4bS8k3VTZ6PUeNQn+dByNu9sHDwuvbt0Y2D5GAgAACAgIAcHd3p2fPnhiNRlJTU/nXv/4FQFxcHA8//DCzZs0iNTWVuLg4FEVhyJAhlJSUUFBQwI4dO4iIiMDb2xuAiIgI0tPTGT9+vK03ySoqqut4KXE/wb6deGF0H3uX08hNPp0Y0NWT//fjCRKGdTN/QVhi4yEjc745wB19/BjSzRtNC/5qvaOPPx8/OpzHP93FA0u38uWTIwm4ji/44kvVPPzhDo4aSlmYMIjsC5eavJ+rkwMTBgfxxfaz/HDQyIMjurdIEN6I0soadmUX8eSonlZ5/l/rEnLUaIjo5UvKQSP7ckyMG6jDw9WJh0bc+I+OG7H9VCHvbTzGtlMX0Shws19nQgI9CfbphKODhodG3HTFGnTQMLmlrKqW/joPjheUsedsMcn78ym+VGO+j6uThp5+7ozs6UsfrTv9tB507eJGWWUty7ee4eT5Mg4bSjlRUIb+Fq15VYm2zq5jIrm5uRw+fJjBgwdTWFhoDhd/f38KCxsOw41GIzqdzvwYnU6H0Whs1K7VajEajbbdACtatPEY2YWX+OLJMFydWl+fsqIoPHlHT174KpPEPXncP9yygeWci5eYsSKT0CAv3pw4kJW7c1u40oZfxZ9MGc6UT3bywNJtfPHkSHRevx4kF8ur+Z9l2zlxvowlDw9lUDcvsi80/aUJ4ObkwD0hASRlnmP76Yt2nyH204kL1Nar3NXX3241DOvhg5ebEz8cNvLljhzGDND9+oOs5ECeidfXHmLH6Yv4dHYmopcvoV29zEsGXYuiKHi4OjE6RMv9w/8TghXVdVTW1OHsqKGTs0OTYyiFZVXc7NeZm/06MzS4C98fMPBdVj4xodoW3T57sdtPpfLycqZNm8ZLL72Eu/uViawoSoce0MrKLeaf6ad4cER3bu/VeqeqRvTyZWBXLxb/eILauvrrfnxNXT3Pf7kXgL//9rYm1+9qKWE9fVn+2AiMJZU8sHQrORebPqK4LOfiJeL/8TMnzpfxz0eGcXf/gGa9TrBvZ24J9GTP2SKKLlW3ROkW23z0PB6ujtxm4xNT/1sfrQcPDL8JNycHkvbm8fFPp2266kFJZQ2vfnOAexdncOp8OXMn3MJ3z0UwrIdPswLkWtycHejS2ZnOLo7N+s7q0smZ+KHd6NbFjQ0HjWw+WnBDr98a2CVEampqmDZtGhMmTCA6OhoAX19fCgoa3tCCggJ8fBqWaNBqtRgMBvNjDQYDWq22UbvRaESrbfvJXl1bz4ursvD3cOHPY0PsXc41KYrCtNF9OHvxEt9knrvuxy9MOUpmTjFvTR5Ed59OVqjwSsN6+PCvJ8K4WF7N+P/LYOOhpo9cUw8bmfj3n7hQWsXnT4Rx53X+kr+9ly8OGoWfTlxoibItoqoqm4+e544+fjfU1dhSfDo785vh3emjdecfaaf4Zt85Kqy0DMcvbT9VyOh3tvCvbWd4JLwHm/54J49G3GyVo/vyqloKy6oa/fnvMSAnBw33Dg4iwNOFOd8c5NT5tr0ChM0/Xaqq8vLLL9OzZ0+mTJlibo+KiiIpKQmApKQkRo8efUW7qqpkZmbi4eFBQEAAkZGRZGRkYDKZMJlMZGRkEBkZaevNaXH/2HKSI4ZS5sUNbBMLHd4TEkBIoCeLfzxBXX3zf11uPlrAkrRT/DbsJsYNDLRihVe67aYufPd8JEHebjyxfBcPf7idNXty2X3mIt/uO8fvPtrB45/uws/dhTXP3s7wHte/3lRnF0eGBftw8nw5eUUVVtiKX5eZU4yhpJKo/q3nh5Wzo4YxoTpejOlH7sUKvth+lvOl1jnnSVVVdmRf5Lkv99KlkxPfTI1k7r2heFpxQPtqa1bVN3GQ7uSgIXZgII4OGp75bI/V1rWyBZuPiezevZtvvvmGvn37ct999wEwc+ZMnnrqKaZPn86qVasICgpi0aJFANx5551s2bIFvV6Pm5sbCxYsAMDb25tnn32W+Ph4AKZOnWoeZG+r9pwt4v3U40wYHIT+ltbzn/9aFEXhhdG9efqzPXy7L4+Jt/76WcrGkkr+sGIf/XUevDL+FhtUeaVg384kPns7n207wwebT5J+/D9HDAEeLvwxui9Pjup5Q1OXb7vJm6zcYraeKrTLgpXrDxhwclBa3edIURQm39aNsxcvsTYrn1W7cxk/KLBFj0Sra+tJOWjg1IVyYm7R8u5vhrTKi115uDrxxn2hTPsqk/dTj/PimP72LskiNn9nhw0bxtGjR5u87dNPP23UpigKr776apP3j4+PN4dIW1d8qZrnv9hLoLcr8+IG2Luc6xJ9i44BXT15c90RRodor/lrr65eZcbXmVyqrmPxQ7fabdKAq5MDT9zRkykRN3PMWIrBVIm/hwv9dB4t0v3j6KBheA8fNh87z87sIsba8GhLVVWSs/KJ7O3Xao9mtZ6u3D+sG0mZ5/gm8xzRLTTIXF5Vy7f7znG+rIpRffyYP3FAqwyQyyJ6+xE/tBtL0k4xbmAgA7q2vVMU7N9ZKqisqeOpf+3mfGkVix+8rdX+x78ajUZhftxAzpdVsTCl6R8Il739/RF+PlnIa/eF0jvA/md3O2gUQgI9ubt/AAO6erXo+EFoV0/cXRz5YMtJmx6N7M8zkVdcYdPgsoSHqxMJQ7uh9XJh/QEDX9/goqKmihpW7s7lYnk1EwYFcetN177WTmvxSuwt+HR25sVVWdRYMEHF3iRE7Ky6tp4ZX2ey4/RFFt4/mMFt9II+g7t787vwHvxr2xnW789v8j5f7TjLkrRTPBIezP2tdK2pluSo0TCihw9ZuSY2Hztvs9f9bt85HDUK0a2sK6sprk4OTBzSlZ5+nXk75SiLNx23KHALSitZsSuHypo6Jt3WlZv9OluhWuvw6uTEG/cN4FB+CUu2nLR3OddNQqSFXG1mRnlV7TUf8+TyXaw/YOAvsSFt/tKifx7bn1u7ezP960wyfjHOoKoqS9NO8uc1+7mjjx9z7DAOYi+3BHkS5OXKez8cs8nRSHVtPWv25DE6JADvfy/L0to5/nuQOXagjoUbjrFg3eHreq/Sjp9n1e5cNIpCwtBuBHq5WbFa6xgzQEfswEDeTz3BcWOpvcu5Lq23s7AV+rWVPz/bdrZR+6O396CypvEMlMycYuZ+d5C8ogr+d9JAHrDzWbwtwdXJgWW/G078P37mfz7czvhBgfTyd2fTkQL255mIHRjIO/cPtvuZ3LbkoFF44o6beX3tYTYeLrD6QHfqYSOF5dU8MLxtfZ40GoW594bi5+7CP9NPk2+q5K/xg3/13KFPf87mte8O4ufuwoTBQbi34vGPX/PafaH8fPICL67OYtXTt7fq9cZ+qe2+43bwa8s8NKW6tt68hMLlhd6yck2cvlBOoJcrXz45kjAbrbBqCz6dnVn7fCTvp57g821nWJuVT0//zrw5aSC/Gda9RZc0uRHX+kHQ0teijh0UyPKtZ3j3h2OM7h9g1ffgq505BHq5MsqOZ6lbSqM0BInOy423U45woqCMt+MHMaibd6P7FpVX89p3B0nKPMedff0IDWrZ8Sx78HN34dUJoUz/OpOPfzrNE3dYZ7maliYhYgX1qoqpooaL5dV8ueMsPx4t4GJZNcbSSmrqVFwcNYT39OWtyQO5ybft9N02VydnR/48tj9/Htuf2rp6HDStbwWCa/0geCQ8uEVDxFGj4YV7+jDj632kHDRYbcD79IVy0o6f5/moPm3mV+x/UxSFZ+7qRUigBy+uyiLu//3E2AGBTBgcRLBvJwrLqtlyrICvd+ZwqbqOF0b34bdhN/H59sa9AG3RfUOC+G7fORZuOMo9IVp6tIGxHQmRFqCqKnvPFpF2/DzniisoLKum9hcn3jk7aOjS2YkQnSfBvp24ybcTjhpNq5562FI6UtfVtdw7uCuLN53gvY3HiA7V3dCX/NWOov628RjODhoeHhl8I6W2Cnf1C2DjH+7k/206wde7ckj+xWQNB43CmAE6no/qTX+dp10v0tbSFEVh/sSB6N/dwp9WZ/HlkyNbzdH71bT/bzErqqtXOXjOxN6zxRRX1OCgKOi8XBnQ1Qs/d2d8O7vweMTNrN1/rtX9EreXjnpFOQeNwvR7+vL8l3tJ3p9/Q5MomjqKKq2s4dt955h8WzerXwbXVjxdnZg9LoQZ+r4cPFdCvqkCn07O9A/0xKezfSYNaBTlqqHVUp9fnZcrr4y/hRdXZ7H4xxNMa4WreP+ShIiFsi+Us/nYeUwVNeg8XZl7Tx+MJVWNLjrj1cnJogBpr1+2lowrtRexAwNZvOkEizYeY9yAlrlmymW7sotQaeiKa29cnRwYGtwFsO9CknDlGOd/a8nPb8Kwbmz795L1A7p6tqrla/6bhMh1qqmr58ejBRzOL6VLJyfu+3df7dgBgVf9cFmiI3/ZtlcajcIMfR+e/mwPa/bmtdi5MudLq9ifZ2JAVy+CvNve9FbR2OVuraPGUp79fA8f/m54sy4tUF+vNiy3c7KQC2VVlFfX4qhp6E7vp3Vn3KCWP41AOqyvQ3ZhOV/vzOFwfinDe3ThobCb6OHXWbqqRLNF36JjSHdv3v7+CKZfXNDIUqqqsuXY+YbJGr3azyw/0bDM/PLHRhDs05nHPtnJlzvOXvX8mXxTBX/beJw73v6RKZ/sYueZixRX1ODi6ICKyjFDGa+vPWyVOuVIpJl+PFLA1C/2UK+qxA0JIrgdzqoS1qfRKMyfOIB7F//E/35/hDcnDbyh59uX27DESVS/gBu+NoZofXzdXfjiyTCmfbWX2Wv2s2p3LglDu9ErwJ3q2nqOGErZdMTI1pOF1KsQ2duPp+/sSW5RxRXr0tWrKr9tgctYN0VCpJmSMvPop/Xg1pu829X1kYXthQZ5MeX2HizLOE1MqJa7+jXvglf/raC0kozjF7jZrzMDunq2cJWitfB1d+Ffj4Xx+Y6zfJRxmj+v2X/F7b0D3Jl6d2/uH9ad7j6dKCyratQVrlEUq51HIyHSTH974NYmd44QlvhjTD8yTlxg5op9rH/hDrTXcd13gJKKGr7bl4+bswP6EK10qbZzGo3CwyOD+Z+wmzheUIbBVImDRqGnf2e7L/MiYyKi3bo8HbM5V5uzNVcnBxY/dBuVNXU89slOTBXNHx85V1zBmr151NTVc+/gIKteVli0Loqi0Ffrwai+/kT09rN7gIAciYh2zFbTMS3VO8Cdv//2Np5cvotHP97BPx8Zhp/7tc/x2HqykGc/301lTR33DQlqN+eEiLZLjkSEsKO7+gWw+KHbOHSuhNj309l4yNjkDJyci5f406osHvznNjxdnfjN8O6t4leoEHIkIoSdxYTqWPPs7Tz/xV6eWL6Lvlp3Inv7E+DpQmllDXvOFLP9dCGKovD7UT15eGQwK3fn2rtsIQAJESFahdAgL1JmjOKbzHOs2JnDFzvOUFlTj0aBkEBPnr2rNw+F3USQt1u7WitKtH1tPkTS0tKYP38+9fX1JCQk8NRTT9m7pGazxTo8ou1wctAQP7Qb8UO7UVtXT02dipODYtdFLOUzKn5Nmw6Ruro6Xn/9dT7++GO0Wi3x8fFERUXRu3dve5fWLK194FfYj6ODBsdWMOlKPqPi17TpEMnKyiI4OJju3RvWIIqNjSU1NbXNhEhbd7VfqS19USchROulqLa48LOVfP/996SnpzN//nwAkpKSyMrKYs6cOVd9TFhYGF27drVViUII0S7k5eWxffv2Ru0d7udiU2+CEEIIy7Tp80S0Wi0Gg8H8b6PRiFbbetfdF0KI9qZNh8jAgQPJzs4mJyeH6upqkpOTiYqKsndZQgjRYbTp7ixHR0fmzJnDE088QV1dHZMnT6ZPn9Z9KUkhhGhP2vTAuhBCCPtq091ZQggh7EtCRAghhMU6fIikpaURExODXq9n6dKl9i6nxeXn5/Pwww8zbtw4YmNj+fTTTwEoLi5mypQpREdHM2XKFEwmE9Bwze558+ah1+uZMGECBw8etGf5N6yuro64uDh+//vfA5CTk0NCQgJ6vZ7p06dTXV0NQHV1NdOnT0ev15OQkEBubtte4LCkpIRp06YxZswYxo4dy969ezvMPv/kk0+IjY1l/PjxzJw5k6qqqna732fPnk14eDjjx483t1mynxMTE4mOjiY6OprExMTrK0LtwGpra9XRo0erZ8+eVauqqtQJEyaox48ft3dZLcpoNKoHDhxQVVVVS0tL1ejoaPX48ePqW2+9pS5ZskRVVVVdsmSJ+vbbb6uqqqqbN29WH3/8cbW+vl7du3evGh8fb7faW8JHH32kzpw5U33qqadUVVXVadOmqWvXrlVVVVVfeeUV9fPPP1dVVVU/++wz9ZVXXlFVVVXXrl2rvvDCC3apt6W8+OKL6ooVK1RVVdWqqirVZDJ1iH1uMBjUu+++W62oqFBVtWF/r169ut3u9x07dqgHDhxQY2NjzW3Xu5+LiorUqKgotaioSC0uLlajoqLU4uLiZtfQoY9EfrlsirOzs3nZlPYkICCA0NBQANzd3enZsydGo5HU1FTi4uIAiIuLY+PGjQDmdkVRGDJkCCUlJRQUFNir/BtiMBjYvHkz8fHxQMMvsW3bthETEwPAxIkTzft706ZNTJw4EYCYmBi2bt3a5HU92oLS0lJ27txp3m5nZ2c8PT07xD6HhqPPyspKamtrqaysxN/fv93u9+HDh+Pl5XVF2/Xu54yMDCIiIvD29sbLy4uIiAjS09ObXUOHDhGj0YhOpzP/W6vVYjQa7ViRdeXm5nL48GEGDx5MYWEhAQEBAPj7+1NYWAg0fk90Ol2bfU8WLFjArFmz0GgaPuZFRUV4enri6Ngws/2X22Y0GgkMDAQapo57eHhQVFRkn8JvUG5uLj4+PsyePZu4uDhefvllLl261CH2uVar5bHHHuPuu+8mMjISd3d3QkNDO8R+v+x69/ONfg926BDpSMrLy5k2bRovvfQS7u7uV9ymKAqKotipMuv48ccf8fHxYcCAAfYuxeZqa2s5dOgQDz74IElJSbi5uTUa72uP+xzAZDKRmppKamoq6enpVFRUXNev6vbGFvu5Q4dIR1k2paamhmnTpjFhwgSio6MB8PX1NXdZFBQU4OPjAzR+TwwGQ5t8T/bs2cOmTZuIiopi5syZbNu2jfnz51NSUkJtbS1w5bZptVry8/OBhi/h0tJSunTpYrf6b4ROp0On0zF48GAAxowZw6FDh9r9Pgf4+eef6datGz4+Pjg5OREdHc2ePXs6xH6/7Hr3841+D3boEOkIy6aoqsrLL79Mz549mTJlirk9KiqKpKQkoGH149GjR1/RrqoqmZmZeHh4mA+N25I//OEPpKWlsWnTJt59911GjhzJO++8Q1hYGCkpKUDDjJTL+zsqKso8KyUlJYWRI0e22V/q/v7+6HQ6Tp06BcDWrVvp1atXu9/nAEFBQezbt4+KigpUVWXr1q307t27Q+z3y653P0dGRpKRkYHJZMJkMpGRkUFkZGSzX6/Dn7G+ZcsWFixYYF425ZlnnrF3SS1q165d/Pa3v6Vv377msYGZM2cyaNAgpk+fTn5+PkFBQSxatAhvb29UVeX1118nPT0dNzc3FixYwMCBA+28FTdm+/btfPTRRyxZsoScnBxmzJiByWQiJCSEhQsX4uzsTFVVFbNmzeLw4cN4eXnx3nvvma9T0xYdPnyYl19+mZqaGrp3786bb75JfX19h9jn77//PuvWrcPR0ZGQkBDmz5+P0Whsl/t95syZ7Nixg6KiInx9fXn++ee55557rns/r1q1iiVLlgDw9NNPM3ny5GbX0OFDRAghhOU6dHeWEEKIGyMhIoQQwmISIkIIISwmISKEEMJiEiJCCCEsJiEihA0YjUamTZtm7zKEaHEyxVcIIYTF5EhEiBa2cOFCPv/8c/O//+///o8PP/zQfM2Huro63nrrLSZPnsyECRP46quvAHjttdfMq8tOnTqV2bNnAw0ngr333ntcunSJp556invvvZfx48ezbt06G2+ZEI1JiAjRwsaNG8f69evN/16/fr15HStoCAUPDw9Wr17N6tWrWbFiBTk5OQwbNoxdu3YBDd1fJ0+eBGD37t0MGzaM9PR0AgIC+Pbbb1m7di133HGHbTdMiCZIiAjRwm655RYKCwsxGo0cOXIET0/PK5ba/umnn/jmm2+47777SEhIoLi4mDNnzjBs2DB2797NiRMn6N27t3khvb1793LrrbfSt29ffv75Z/7617+ya9cuPDw87LiVQjRwtHcBQrRHY8aMISUlhQsXLjBu3LgrblNVlb/85S9NHkmUlJSQnp7OsGHDMJlMrF+/nk6dOuHu7o67uztr1qxhy5YtLFq0iJEjR/Lcc8/ZapOEaJIciQhhBePGjWPdunWkpKQwZsyYK26LjIzkyy+/pKamBoDTp09z6dIlAIYMGcKnn37K8OHDGTZsGB999BHDhg0DGrq43NzcuO+++3j88cc5dOiQbTdKiCbIkYgQVtCnTx/Ky8sJCAggICCA3Nxc820JCQnk5eUxadIkVFWlS5cu/P3vfwdg6NChZGRkEBwcTFBQECaTyRwix44d4+2330aj0eDo6MjcuXPtsWlCXEGm+AohhLCYdGcJIYSwmISIEEIIi0mICCGEsJiEiBBCCItJiAghhLCYhIgQQgiLSYgIIYSw2P8H7g9aHhGQlLYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = train_data, x = \"views\", kde = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping unnecessary coloumns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:09:02.291088Z",
     "iopub.status.busy": "2022-02-13T08:09:02.290419Z",
     "iopub.status.idle": "2022-02-13T08:09:02.302417Z",
     "shell.execute_reply": "2022-02-13T08:09:02.301592Z",
     "shell.execute_reply.started": "2022-02-13T08:09:02.291050Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((89197, 10), (11121, 9))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.drop(['row_id'], axis=1, inplace=True)\n",
    "#test.drop(['row_id','user_id'], axis=1, inplace=True)\n",
    "train_data.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:17:25.013645Z",
     "iopub.status.busy": "2022-02-13T08:17:25.013038Z",
     "iopub.status.idle": "2022-02-13T08:17:25.027196Z",
     "shell.execute_reply": "2022-02-13T08:17:25.026419Z",
     "shell.execute_reply.started": "2022-02-13T08:17:25.013611Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>age</th>\n",
       "      <th>followers</th>\n",
       "      <th>views</th>\n",
       "      <th>engagement_score</th>\n",
       "      <th>Male</th>\n",
       "      <th>Student</th>\n",
       "      <th>Working Professional</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89192</th>\n",
       "      <td>23996</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>340</td>\n",
       "      <td>662</td>\n",
       "      <td>3.91</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89193</th>\n",
       "      <td>20466</td>\n",
       "      <td>20</td>\n",
       "      <td>47</td>\n",
       "      <td>31</td>\n",
       "      <td>240</td>\n",
       "      <td>892</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89194</th>\n",
       "      <td>13655</td>\n",
       "      <td>16</td>\n",
       "      <td>97</td>\n",
       "      <td>25</td>\n",
       "      <td>270</td>\n",
       "      <td>462</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89195</th>\n",
       "      <td>24840</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>230</td>\n",
       "      <td>819</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89196</th>\n",
       "      <td>27183</td>\n",
       "      <td>25</td>\n",
       "      <td>150</td>\n",
       "      <td>13</td>\n",
       "      <td>240</td>\n",
       "      <td>317</td>\n",
       "      <td>4.31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  category_id  video_id  age  followers  views  \\\n",
       "89192    23996           15        32   25        340    662   \n",
       "89193    20466           20        47   31        240    892   \n",
       "89194    13655           16        97   25        270    462   \n",
       "89195    24840            9        18   35        230    819   \n",
       "89196    27183           25       150   13        240    317   \n",
       "\n",
       "       engagement_score  Male  Student  Working Professional  \n",
       "89192              3.91     1        0                     0  \n",
       "89193              3.56     1        0                     0  \n",
       "89194              4.23     1        1                     0  \n",
       "89195              3.77     1        0                     1  \n",
       "89196              4.31     1        1                     0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:17:29.780470Z",
     "iopub.status.busy": "2022-02-13T08:17:29.780173Z",
     "iopub.status.idle": "2022-02-13T08:17:29.797780Z",
     "shell.execute_reply": "2022-02-13T08:17:29.796320Z",
     "shell.execute_reply.started": "2022-02-13T08:17:29.780435Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>profession</th>\n",
       "      <th>followers</th>\n",
       "      <th>views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11116</th>\n",
       "      <td>100314</td>\n",
       "      <td>26336</td>\n",
       "      <td>25</td>\n",
       "      <td>140</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>240</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11117</th>\n",
       "      <td>100315</td>\n",
       "      <td>6772</td>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>280</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11118</th>\n",
       "      <td>100316</td>\n",
       "      <td>2042</td>\n",
       "      <td>16</td>\n",
       "      <td>98</td>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>270</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11119</th>\n",
       "      <td>100317</td>\n",
       "      <td>24626</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other</td>\n",
       "      <td>280</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11120</th>\n",
       "      <td>100318</td>\n",
       "      <td>967</td>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>280</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       row_id  user_id  category_id  video_id  age  gender  \\\n",
       "11116  100314    26336           25       140   21    Male   \n",
       "11117  100315     6772            8       100   19  Female   \n",
       "11118  100316     2042           16        98   22    Male   \n",
       "11119  100317    24626            8        16   33    Male   \n",
       "11120  100318      967            8        74   33    Male   \n",
       "\n",
       "                 profession  followers  views  \n",
       "11116               Student        240    317  \n",
       "11117               Student        280    628  \n",
       "11118               Student        270    462  \n",
       "11119                 Other        280    628  \n",
       "11120  Working Professional        280    628  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.tail()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:09:09.503445Z",
     "iopub.status.busy": "2022-02-13T08:09:09.503189Z",
     "iopub.status.idle": "2022-02-13T08:09:10.191044Z",
     "shell.execute_reply": "2022-02-13T08:09:10.190345Z",
     "shell.execute_reply.started": "2022-02-13T08:09:09.503416Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = train_data.corr()\n",
    "plt.subplots(figsize=(15,12))\n",
    "dataplot = sns.heatmap(corr, cmap=\"YlGnBu\", annot=True) \n",
    "# displaying heatmap\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:17:38.858633Z",
     "iopub.status.busy": "2022-02-13T08:17:38.857982Z",
     "iopub.status.idle": "2022-02-13T08:17:38.865083Z",
     "shell.execute_reply": "2022-02-13T08:17:38.864186Z",
     "shell.execute_reply.started": "2022-02-13T08:17:38.858597Z"
    }
   },
   "outputs": [],
   "source": [
    "X=train_data.drop(['engagement_score'], axis=1)\n",
    "y=train_data['engagement_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:17:41.677179Z",
     "iopub.status.busy": "2022-02-13T08:17:41.676907Z",
     "iopub.status.idle": "2022-02-13T08:17:41.687580Z",
     "shell.execute_reply": "2022-02-13T08:17:41.686846Z",
     "shell.execute_reply.started": "2022-02-13T08:17:41.677151Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>age</th>\n",
       "      <th>followers</th>\n",
       "      <th>views</th>\n",
       "      <th>Male</th>\n",
       "      <th>Student</th>\n",
       "      <th>Working Professional</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19990</td>\n",
       "      <td>37</td>\n",
       "      <td>128</td>\n",
       "      <td>24</td>\n",
       "      <td>180</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5304</td>\n",
       "      <td>32</td>\n",
       "      <td>132</td>\n",
       "      <td>14</td>\n",
       "      <td>330</td>\n",
       "      <td>714</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1840</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>180</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12597</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>19</td>\n",
       "      <td>220</td>\n",
       "      <td>613</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13626</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>27</td>\n",
       "      <td>220</td>\n",
       "      <td>613</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  category_id  video_id  age  followers  views  Male  Student  \\\n",
       "0    19990           37       128   24        180   1000     1        1   \n",
       "1     5304           32       132   14        330    714     0        1   \n",
       "2     1840           12        24   19        180    138     1        1   \n",
       "3    12597           23       112   19        220    613     1        1   \n",
       "4    13626           23       112   27        220    613     1        0   \n",
       "\n",
       "   Working Professional  \n",
       "0                     0  \n",
       "1                     0  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:17:43.861918Z",
     "iopub.status.busy": "2022-02-13T08:17:43.861284Z",
     "iopub.status.idle": "2022-02-13T08:17:43.870260Z",
     "shell.execute_reply": "2022-02-13T08:17:43.869431Z",
     "shell.execute_reply.started": "2022-02-13T08:17:43.861877Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.33\n",
       "1    1.79\n",
       "2    4.35\n",
       "3    3.77\n",
       "4    3.13\n",
       "Name: engagement_score, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:17:59.689017Z",
     "iopub.status.busy": "2022-02-13T08:17:59.688471Z",
     "iopub.status.idle": "2022-02-13T08:17:59.710116Z",
     "shell.execute_reply": "2022-02-13T08:17:59.709447Z",
     "shell.execute_reply.started": "2022-02-13T08:17:59.688980Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:02.468445Z",
     "iopub.status.busy": "2022-02-13T08:18:02.468194Z",
     "iopub.status.idle": "2022-02-13T08:18:02.474762Z",
     "shell.execute_reply": "2022-02-13T08:18:02.474020Z",
     "shell.execute_reply.started": "2022-02-13T08:18:02.468416Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training input is (71357, 9)\n",
      "The size of training output is (71357,)\n",
      "**************************************************\n",
      "The size of testing input is (17840, 9)\n",
      "The size of testing output is (17840,)\n"
     ]
    }
   ],
   "source": [
    "print(\"The size of training input is\", X_train.shape)\n",
    "print(\"The size of training output is\", y_train.shape)\n",
    "print(50 *'*')\n",
    "print(\"The size of testing input is\", X_test.shape)\n",
    "print(\"The size of testing output is\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:05.391041Z",
     "iopub.status.busy": "2022-02-13T08:18:05.390457Z",
     "iopub.status.idle": "2022-02-13T08:18:08.481899Z",
     "shell.execute_reply": "2022-02-13T08:18:08.480817Z",
     "shell.execute_reply.started": "2022-02-13T08:18:05.391006Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Ridge(), n_jobs=-1,\n",
       "             param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000,\n",
       "                                   10000, 100000]},\n",
       "             scoring='neg_mean_absolute_error')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params ={'alpha' :[0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]}\n",
    "ridge_regressor =GridSearchCV(Ridge(), params ,cv =5,scoring = 'neg_mean_absolute_error', n_jobs =-1)\n",
    "ridge_regressor.fit(X_train ,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:14.466192Z",
     "iopub.status.busy": "2022-02-13T08:18:14.465495Z",
     "iopub.status.idle": "2022-02-13T08:18:14.480092Z",
     "shell.execute_reply": "2022-02-13T08:18:14.479230Z",
     "shell.execute_reply.started": "2022-02-13T08:18:14.466156Z"
    }
   },
   "outputs": [],
   "source": [
    "y_train_pred =ridge_regressor.predict(X_train) ##Predict train result\n",
    "y_test_pred =ridge_regressor.predict(X_test) ##Predict test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:16.732106Z",
     "iopub.status.busy": "2022-02-13T08:18:16.731475Z",
     "iopub.status.idle": "2022-02-13T08:18:16.745173Z",
     "shell.execute_reply": "2022-02-13T08:18:16.744524Z",
     "shell.execute_reply.started": "2022-02-13T08:18:16.732063Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Ridge Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.7360760525443968\n",
      "Mean absolute error:  0.5680515208205575\n",
      "R-squared:  0.27092743621194526\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Ridge Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"Mean absolute error: \",mae(y_train.values, y_train_pred))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:22.102083Z",
     "iopub.status.busy": "2022-02-13T08:18:22.101559Z",
     "iopub.status.idle": "2022-02-13T08:18:22.111927Z",
     "shell.execute_reply": "2022-02-13T08:18:22.111110Z",
     "shell.execute_reply.started": "2022-02-13T08:18:22.102047Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Ridge Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.7386171417905056\n",
      "Mean absolute error:  0.5687148260816715\n",
      "R-squared:  0.2778788616815382\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Ridge Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"Mean absolute error: \",mae(y_test, y_test_pred))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:33.731162Z",
     "iopub.status.busy": "2022-02-13T08:18:33.730902Z",
     "iopub.status.idle": "2022-02-13T08:18:37.532802Z",
     "shell.execute_reply": "2022-02-13T08:18:37.532101Z",
     "shell.execute_reply.started": "2022-02-13T08:18:33.731132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=15, estimator=Lasso(), n_jobs=-1,\n",
       "             param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000,\n",
       "                                   10000, 100000]},\n",
       "             scoring='neg_mean_absolute_error')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params ={'alpha' :[0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]}\n",
    "lasso_regressor =GridSearchCV(Lasso(), params ,cv =15,scoring = 'neg_mean_absolute_error', n_jobs =-1)\n",
    "lasso_regressor.fit(X_train ,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:37.534853Z",
     "iopub.status.busy": "2022-02-13T08:18:37.534187Z",
     "iopub.status.idle": "2022-02-13T08:18:37.556421Z",
     "shell.execute_reply": "2022-02-13T08:18:37.555755Z",
     "shell.execute_reply.started": "2022-02-13T08:18:37.534815Z"
    }
   },
   "outputs": [],
   "source": [
    "y_train_pred =lasso_regressor.predict(X_train) ##Predict train result\n",
    "y_test_pred =lasso_regressor.predict(X_test) ##Predict test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:37.666083Z",
     "iopub.status.busy": "2022-02-13T08:18:37.665730Z",
     "iopub.status.idle": "2022-02-13T08:18:37.678913Z",
     "shell.execute_reply": "2022-02-13T08:18:37.677804Z",
     "shell.execute_reply.started": "2022-02-13T08:18:37.666044Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for lasso Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.7360761844026186\n",
      "Mean absolute error:  0.5680696484979897\n",
      "R-squared:  0.2709271750046416\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for lasso Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"Mean absolute error: \",mae(y_train.values, y_train_pred))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:41.494417Z",
     "iopub.status.busy": "2022-02-13T08:18:41.494163Z",
     "iopub.status.idle": "2022-02-13T08:18:41.504293Z",
     "shell.execute_reply": "2022-02-13T08:18:41.503414Z",
     "shell.execute_reply.started": "2022-02-13T08:18:41.494389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for lasso Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.7386187907991228\n",
      "Mean absolute error:  0.5687363668822549\n",
      "R-squared:  0.27787563731740805\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for lasso Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"Mean absolute error: \",mae(y_test, y_test_pred))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Neighbors Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:18:47.030367Z",
     "iopub.status.busy": "2022-02-13T08:18:47.030121Z",
     "iopub.status.idle": "2022-02-13T08:19:43.747286Z",
     "shell.execute_reply": "2022-02-13T08:19:43.746452Z",
     "shell.execute_reply.started": "2022-02-13T08:18:47.030341Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsRegressor(),\n",
       "             param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                         13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
       "                                         23, 24, 25, 26, 27, 28, 29]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_range = list(range(1, 30))\n",
    "params = dict(n_neighbors = k_range)\n",
    "knn_regressor = GridSearchCV(KNeighborsRegressor(), params, cv =10, scoring = 'neg_mean_squared_error')\n",
    "knn_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:19:43.749381Z",
     "iopub.status.busy": "2022-02-13T08:19:43.749059Z",
     "iopub.status.idle": "2022-02-13T08:19:45.020197Z",
     "shell.execute_reply": "2022-02-13T08:19:45.019452Z",
     "shell.execute_reply.started": "2022-02-13T08:19:43.749345Z"
    }
   },
   "outputs": [],
   "source": [
    "y_train_pred =knn_regressor.predict(X_train) ##Predict train result\n",
    "y_test_pred =knn_regressor.predict(X_test) ##Predict test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:19:45.036044Z",
     "iopub.status.busy": "2022-02-13T08:19:45.035593Z",
     "iopub.status.idle": "2022-02-13T08:19:45.047901Z",
     "shell.execute_reply": "2022-02-13T08:19:45.047205Z",
     "shell.execute_reply.started": "2022-02-13T08:19:45.036009Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for knn Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.8227296200178328\n",
      "Mean absolute error:  0.6558255503048538\n",
      "R-squared:  0.08916513912834867\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for knn Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"Mean absolute error: \",mae(y_train.values, y_train_pred))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:19:45.049605Z",
     "iopub.status.busy": "2022-02-13T08:19:45.049199Z",
     "iopub.status.idle": "2022-02-13T08:19:45.060969Z",
     "shell.execute_reply": "2022-02-13T08:19:45.059306Z",
     "shell.execute_reply.started": "2022-02-13T08:19:45.049568Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for knn Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.8553706077899511\n",
      "Mean absolute error:  0.6823642338023814\n",
      "R-squared:  0.0315438999996932\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for knn Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"Mean absolute error: \",mae(y_test, y_test_pred))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:19:45.062793Z",
     "iopub.status.busy": "2022-02-13T08:19:45.062131Z",
     "iopub.status.idle": "2022-02-13T08:20:42.402876Z",
     "shell.execute_reply": "2022-02-13T08:20:42.402213Z",
     "shell.execute_reply.started": "2022-02-13T08:19:45.062745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeRegressor(),\n",
       "             param_grid={'max_depth': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,\n",
       "                                       15, 16, 17, 18, 19, 20, 21, 22, 23, 24,\n",
       "                                       25, 26, 27, 28, 29]})"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth  =list(range(3,30))\n",
    "param_grid =dict(max_depth =depth)\n",
    "tree =GridSearchCV(DecisionTreeRegressor(),param_grid,cv =10)\n",
    "tree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:20:42.404496Z",
     "iopub.status.busy": "2022-02-13T08:20:42.404253Z",
     "iopub.status.idle": "2022-02-13T08:20:42.420967Z",
     "shell.execute_reply": "2022-02-13T08:20:42.420332Z",
     "shell.execute_reply.started": "2022-02-13T08:20:42.404462Z"
    }
   },
   "outputs": [],
   "source": [
    "y_train_pred =tree.predict(X_train) ##Predict train result\n",
    "y_test_pred =tree.predict(X_test) ##Predict test result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:20:42.422560Z",
     "iopub.status.busy": "2022-02-13T08:20:42.422297Z",
     "iopub.status.idle": "2022-02-13T08:20:42.432905Z",
     "shell.execute_reply": "2022-02-13T08:20:42.432226Z",
     "shell.execute_reply.started": "2022-02-13T08:20:42.422514Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Decision Tree Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.6872357759917682\n",
      "Mean absolute error:  0.5179687217555204\n",
      "R-squared:  0.36446876847313847\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Decision Tree Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"Mean absolute error: \", mae(y_train.values, y_train_pred))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:20:42.434791Z",
     "iopub.status.busy": "2022-02-13T08:20:42.434328Z",
     "iopub.status.idle": "2022-02-13T08:20:42.444038Z",
     "shell.execute_reply": "2022-02-13T08:20:42.443198Z",
     "shell.execute_reply.started": "2022-02-13T08:20:42.434757Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Decision Tree Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.7086241287145016\n",
      "Mean absolute error:  0.5332242322782851\n",
      "R-squared:  0.33533445695674513\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Decision Tree Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"Mean absolute error: \", mae(y_test, y_test_pred))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RF REGRESSOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T08:20:42.447608Z",
     "iopub.status.busy": "2022-02-13T08:20:42.447414Z",
     "iopub.status.idle": "2022-02-13T09:16:37.413906Z",
     "shell.execute_reply": "2022-02-13T09:16:37.413235Z",
     "shell.execute_reply.started": "2022-02-13T08:20:42.447585Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500]},\n",
       "                   scoring='neg_mean_absolute_error')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_params = {'n_estimators': [100, 200, 300, 400, 500], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4]}\n",
    "random_regressor = RandomizedSearchCV(RandomForestRegressor(), tuned_params, n_iter = 20, scoring = 'neg_mean_absolute_error', cv = 5, n_jobs = -1)\n",
    "random_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:16:37.415350Z",
     "iopub.status.busy": "2022-02-13T09:16:37.415114Z",
     "iopub.status.idle": "2022-02-13T09:16:47.323406Z",
     "shell.execute_reply": "2022-02-13T09:16:47.322700Z",
     "shell.execute_reply.started": "2022-02-13T09:16:37.415318Z"
    }
   },
   "outputs": [],
   "source": [
    "y_train_pred = random_regressor.predict(X_train)\n",
    "y_test_pred = random_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:16:47.325002Z",
     "iopub.status.busy": "2022-02-13T09:16:47.324747Z",
     "iopub.status.idle": "2022-02-13T09:16:47.336565Z",
     "shell.execute_reply": "2022-02-13T09:16:47.335572Z",
     "shell.execute_reply.started": "2022-02-13T09:16:47.324970Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Results for Random Forest Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.44994705224247666\n",
      "Mean absolute error:  0.3358858490702489\n",
      "R-squared:  0.7275742219506862\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Results for Random Forest Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_train.values, y_train_pred)))\n",
    "print(\"Mean absolute error: \", mae(y_train.values, y_train_pred))\n",
    "print(\"R-squared: \", r2_score(y_train.values, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:16:47.338769Z",
     "iopub.status.busy": "2022-02-13T09:16:47.338212Z",
     "iopub.status.idle": "2022-02-13T09:16:47.349377Z",
     "shell.execute_reply": "2022-02-13T09:16:47.348464Z",
     "shell.execute_reply.started": "2022-02-13T09:16:47.338734Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results for Random Forest Regressor Model:\n",
      "--------------------------------------------------\n",
      "Root mean squared error:  0.6745914680459376\n",
      "Mean absolute  error:  0.5043107231742436\n",
      "R-squared:  0.3976443597641398\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Results for Random Forest Regressor Model:\")\n",
    "print(50 * '-')\n",
    "print(\"Root mean squared error: \", sqrt(mse(y_test, y_test_pred)))\n",
    "print(\"Mean absolute  error: \", mae(y_test, y_test_pred))\n",
    "print(\"R-squared: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:25:58.723605Z",
     "iopub.status.busy": "2022-02-13T09:25:58.723338Z",
     "iopub.status.idle": "2022-02-13T09:25:58.731216Z",
     "shell.execute_reply": "2022-02-13T09:25:58.730438Z",
     "shell.execute_reply.started": "2022-02-13T09:25:58.723575Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_id          int64\n",
       "user_id         int64\n",
       "category_id     int64\n",
       "video_id        int64\n",
       "age             int64\n",
       "gender         object\n",
       "profession     object\n",
       "followers       int64\n",
       "views           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:26:05.238856Z",
     "iopub.status.busy": "2022-02-13T09:26:05.238196Z",
     "iopub.status.idle": "2022-02-13T09:26:05.249265Z",
     "shell.execute_reply": "2022-02-13T09:26:05.248483Z",
     "shell.execute_reply.started": "2022-02-13T09:26:05.238819Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_id         0\n",
       "user_id        0\n",
       "category_id    0\n",
       "video_id       0\n",
       "age            0\n",
       "gender         0\n",
       "profession     0\n",
       "followers      0\n",
       "views          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:26:11.292641Z",
     "iopub.status.busy": "2022-02-13T09:26:11.292027Z",
     "iopub.status.idle": "2022-02-13T09:26:11.303731Z",
     "shell.execute_reply": "2022-02-13T09:26:11.303016Z",
     "shell.execute_reply.started": "2022-02-13T09:26:11.292599Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>profession</th>\n",
       "      <th>followers</th>\n",
       "      <th>views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89198</td>\n",
       "      <td>7986</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>180</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89199</td>\n",
       "      <td>11278</td>\n",
       "      <td>34</td>\n",
       "      <td>115</td>\n",
       "      <td>14</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>230</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89200</td>\n",
       "      <td>17245</td>\n",
       "      <td>8</td>\n",
       "      <td>110</td>\n",
       "      <td>44</td>\n",
       "      <td>Female</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>280</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89201</td>\n",
       "      <td>9851</td>\n",
       "      <td>16</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Student</td>\n",
       "      <td>270</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89202</td>\n",
       "      <td>16008</td>\n",
       "      <td>34</td>\n",
       "      <td>96</td>\n",
       "      <td>47</td>\n",
       "      <td>Female</td>\n",
       "      <td>Other</td>\n",
       "      <td>230</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id  user_id  category_id  video_id  age  gender            profession  \\\n",
       "0   89198     7986           12        42   14    Male               Student   \n",
       "1   89199    11278           34       115   14    Male               Student   \n",
       "2   89200    17245            8       110   44  Female  Working Professional   \n",
       "3   89201     9851           16       137   18    Male               Student   \n",
       "4   89202    16008           34        96   47  Female                 Other   \n",
       "\n",
       "   followers  views  \n",
       "0        180    138  \n",
       "1        230    840  \n",
       "2        280    628  \n",
       "3        270    462  \n",
       "4        230    840  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:26:14.253413Z",
     "iopub.status.busy": "2022-02-13T09:26:14.252611Z",
     "iopub.status.idle": "2022-02-13T09:26:14.266748Z",
     "shell.execute_reply": "2022-02-13T09:26:14.265856Z",
     "shell.execute_reply.started": "2022-02-13T09:26:14.253367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      6377\n",
      "Female    4744\n",
      "Name: gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(test['gender'].value_counts())\n",
    "dummies = pd.get_dummies(test['gender'],drop_first=True)\n",
    "test_data = pd.concat([test.drop('gender',axis=1),dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:26:20.781371Z",
     "iopub.status.busy": "2022-02-13T09:26:20.781117Z",
     "iopub.status.idle": "2022-02-13T09:26:20.791982Z",
     "shell.execute_reply": "2022-02-13T09:26:20.791257Z",
     "shell.execute_reply.started": "2022-02-13T09:26:20.781343Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>age</th>\n",
       "      <th>profession</th>\n",
       "      <th>followers</th>\n",
       "      <th>views</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89198</td>\n",
       "      <td>7986</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>Student</td>\n",
       "      <td>180</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89199</td>\n",
       "      <td>11278</td>\n",
       "      <td>34</td>\n",
       "      <td>115</td>\n",
       "      <td>14</td>\n",
       "      <td>Student</td>\n",
       "      <td>230</td>\n",
       "      <td>840</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89200</td>\n",
       "      <td>17245</td>\n",
       "      <td>8</td>\n",
       "      <td>110</td>\n",
       "      <td>44</td>\n",
       "      <td>Working Professional</td>\n",
       "      <td>280</td>\n",
       "      <td>628</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89201</td>\n",
       "      <td>9851</td>\n",
       "      <td>16</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "      <td>Student</td>\n",
       "      <td>270</td>\n",
       "      <td>462</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89202</td>\n",
       "      <td>16008</td>\n",
       "      <td>34</td>\n",
       "      <td>96</td>\n",
       "      <td>47</td>\n",
       "      <td>Other</td>\n",
       "      <td>230</td>\n",
       "      <td>840</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id  user_id  category_id  video_id  age            profession  \\\n",
       "0   89198     7986           12        42   14               Student   \n",
       "1   89199    11278           34       115   14               Student   \n",
       "2   89200    17245            8       110   44  Working Professional   \n",
       "3   89201     9851           16       137   18               Student   \n",
       "4   89202    16008           34        96   47                 Other   \n",
       "\n",
       "   followers  views  Male  \n",
       "0        180    138     1  \n",
       "1        230    840     1  \n",
       "2        280    628     0  \n",
       "3        270    462     1  \n",
       "4        230    840     0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:26:28.023574Z",
     "iopub.status.busy": "2022-02-13T09:26:28.023308Z",
     "iopub.status.idle": "2022-02-13T09:26:28.036894Z",
     "shell.execute_reply": "2022-02-13T09:26:28.036026Z",
     "shell.execute_reply.started": "2022-02-13T09:26:28.023539Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student                 5572\n",
      "Other                   3366\n",
      "Working Professional    2183\n",
      "Name: profession, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(test_data['profession'].value_counts())\n",
    "dummies = pd.get_dummies(test_data['profession'],drop_first=True)\n",
    "test_data = pd.concat([test_data.drop('profession',axis=1),dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:26:31.090328Z",
     "iopub.status.busy": "2022-02-13T09:26:31.089447Z",
     "iopub.status.idle": "2022-02-13T09:26:31.095946Z",
     "shell.execute_reply": "2022-02-13T09:26:31.095014Z",
     "shell.execute_reply.started": "2022-02-13T09:26:31.090287Z"
    }
   },
   "outputs": [],
   "source": [
    "test_data=test_data.drop(['row_id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:26:34.230007Z",
     "iopub.status.busy": "2022-02-13T09:26:34.229460Z",
     "iopub.status.idle": "2022-02-13T09:26:34.241276Z",
     "shell.execute_reply": "2022-02-13T09:26:34.240465Z",
     "shell.execute_reply.started": "2022-02-13T09:26:34.229965Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>category_id</th>\n",
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       "      <th>age</th>\n",
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       "      <th>0</th>\n",
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       "      <td>16008</td>\n",
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      ],
      "text/plain": [
       "   user_id  category_id  video_id  age  followers  views  Male  Student  \\\n",
       "0     7986           12        42   14        180    138     1        1   \n",
       "1    11278           34       115   14        230    840     1        1   \n",
       "2    17245            8       110   44        280    628     0        0   \n",
       "3     9851           16       137   18        270    462     1        1   \n",
       "4    16008           34        96   47        230    840     0        0   \n",
       "\n",
       "   Working Professional  \n",
       "0                     0  \n",
       "1                     0  \n",
       "2                     1  \n",
       "3                     0  \n",
       "4                     0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:27:04.734566Z",
     "iopub.status.busy": "2022-02-13T09:27:04.734281Z",
     "iopub.status.idle": "2022-02-13T09:27:06.288987Z",
     "shell.execute_reply": "2022-02-13T09:27:06.288289Z",
     "shell.execute_reply.started": "2022-02-13T09:27:04.734519Z"
    }
   },
   "outputs": [],
   "source": [
    "y_test_ridge_pred = ridge_regressor.predict(test_data)\n",
    "y_test_lasso_pred =lasso_regressor.predict(test_data)\n",
    "y_test_knn_pred = knn_regressor.predict(test_data)\n",
    "y_test_dt_pred = tree.predict(test_data)\n",
    "y_test_rf_pred = random_regressor.predict(test_data)\n",
    "# y_test_xgb_pred = model_xgb.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T07:56:34.400868Z",
     "iopub.status.busy": "2022-02-13T07:56:34.399917Z",
     "iopub.status.idle": "2022-02-13T07:56:34.40856Z",
     "shell.execute_reply": "2022-02-13T07:56:34.407727Z",
     "shell.execute_reply.started": "2022-02-13T07:56:34.40082Z"
    }
   },
   "outputs": [],
   "source": [
    "y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:27:19.453428Z",
     "iopub.status.busy": "2022-02-13T09:27:19.453168Z",
     "iopub.status.idle": "2022-02-13T09:27:19.461833Z",
     "shell.execute_reply": "2022-02-13T09:27:19.460852Z",
     "shell.execute_reply.started": "2022-02-13T09:27:19.453398Z"
    }
   },
   "outputs": [],
   "source": [
    "test_data['engagement_score_ridge'] = y_test_ridge_pred\n",
    "test_data['engagement_score_lasso'] = y_test_lasso_pred\n",
    "test_data['engagement_score_knn'] = y_test_knn_pred\n",
    "test_data['engagement_score_dt'] = y_test_dt_pred\n",
    "test_data['engagement_score_rf'] = y_test_rf_pred\n",
    "# test_data['engagement_score_xgb'] = y_test_xgb_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:27:22.245595Z",
     "iopub.status.busy": "2022-02-13T09:27:22.245318Z",
     "iopub.status.idle": "2022-02-13T09:27:22.267208Z",
     "shell.execute_reply": "2022-02-13T09:27:22.266447Z",
     "shell.execute_reply.started": "2022-02-13T09:27:22.245564Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>user_id</th>\n",
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       "      <th>age</th>\n",
       "      <th>followers</th>\n",
       "      <th>views</th>\n",
       "      <th>Male</th>\n",
       "      <th>Student</th>\n",
       "      <th>Working Professional</th>\n",
       "      <th>engagement_score_ridge</th>\n",
       "      <th>engagement_score_lasso</th>\n",
       "      <th>engagement_score_knn</th>\n",
       "      <th>engagement_score_dt</th>\n",
       "      <th>engagement_score_rf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>4.033378</td>\n",
       "      <td>3.727879</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16008</td>\n",
       "      <td>34</td>\n",
       "      <td>96</td>\n",
       "      <td>47</td>\n",
       "      <td>230</td>\n",
       "      <td>840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.738871</td>\n",
       "      <td>2.738313</td>\n",
       "      <td>3.018966</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.654284</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>11116</th>\n",
       "      <td>26336</td>\n",
       "      <td>25</td>\n",
       "      <td>140</td>\n",
       "      <td>21</td>\n",
       "      <td>240</td>\n",
       "      <td>317</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>4.036608</td>\n",
       "      <td>3.384138</td>\n",
       "      <td>3.957541</td>\n",
       "      <td>3.426137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11117</th>\n",
       "      <td>6772</td>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>19</td>\n",
       "      <td>280</td>\n",
       "      <td>628</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.395434</td>\n",
       "      <td>3.395397</td>\n",
       "      <td>3.753793</td>\n",
       "      <td>3.488652</td>\n",
       "      <td>3.112287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11118</th>\n",
       "      <td>2042</td>\n",
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       "      <td>98</td>\n",
       "      <td>22</td>\n",
       "      <td>270</td>\n",
       "      <td>462</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.077724</td>\n",
       "      <td>4.077128</td>\n",
       "      <td>3.367241</td>\n",
       "      <td>3.884439</td>\n",
       "      <td>3.490755</td>\n",
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       "    <tr>\n",
       "      <th>11119</th>\n",
       "      <td>24626</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "      <td>280</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.667593</td>\n",
       "      <td>3.667342</td>\n",
       "      <td>3.397241</td>\n",
       "      <td>3.730905</td>\n",
       "      <td>3.856555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11120</th>\n",
       "      <td>967</td>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>33</td>\n",
       "      <td>280</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.454476</td>\n",
       "      <td>3.454760</td>\n",
       "      <td>3.603103</td>\n",
       "      <td>3.413830</td>\n",
       "      <td>3.324160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11121 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  category_id  video_id  age  followers  views  Male  Student  \\\n",
       "0         7986           12        42   14        180    138     1        1   \n",
       "1        11278           34       115   14        230    840     1        1   \n",
       "2        17245            8       110   44        280    628     0        0   \n",
       "3         9851           16       137   18        270    462     1        1   \n",
       "4        16008           34        96   47        230    840     0        0   \n",
       "...        ...          ...       ...  ...        ...    ...   ...      ...   \n",
       "11116    26336           25       140   21        240    317     1        1   \n",
       "11117     6772            8       100   19        280    628     0        1   \n",
       "11118     2042           16        98   22        270    462     1        1   \n",
       "11119    24626            8        16   33        280    628     1        0   \n",
       "11120      967            8        74   33        280    628     1        0   \n",
       "\n",
       "       Working Professional  engagement_score_ridge  engagement_score_lasso  \\\n",
       "0                         0                4.104768                4.104601   \n",
       "1                         0                3.839753                3.839578   \n",
       "2                         1                2.766573                2.766684   \n",
       "3                         0                4.085669                4.085269   \n",
       "4                         0                2.738871                2.738313   \n",
       "...                     ...                     ...                     ...   \n",
       "11116                     0                4.037159                4.036608   \n",
       "11117                     0                3.395434                3.395397   \n",
       "11118                     0                4.077724                4.077128   \n",
       "11119                     0                3.667593                3.667342   \n",
       "11120                     1                3.454476                3.454760   \n",
       "\n",
       "       engagement_score_knn  engagement_score_dt  engagement_score_rf  \n",
       "0                  3.401034             4.109443             4.079172  \n",
       "1                  3.164138             3.831422             3.370679  \n",
       "2                  3.661034             2.720686             2.592641  \n",
       "3                  3.630345             4.033378             3.727879  \n",
       "4                  3.018966             0.000000             1.654284  \n",
       "...                     ...                  ...                  ...  \n",
       "11116              3.384138             3.957541             3.426137  \n",
       "11117              3.753793             3.488652             3.112287  \n",
       "11118              3.367241             3.884439             3.490755  \n",
       "11119              3.397241             3.730905             3.856555  \n",
       "11120              3.603103             3.413830             3.324160  \n",
       "\n",
       "[11121 rows x 14 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:27:38.032452Z",
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     "shell.execute_reply.started": "2022-02-13T09:27:38.032417Z"
    }
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   "outputs": [],
   "source": [
    "test=pd.read_csv(\"../input/testdata/test_1zqHu22.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
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    "execution": {
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       "       row_id  user_id  category_id  video_id  age  gender  \\\n",
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  {
   "cell_type": "code",
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     "iopub.execute_input": "2022-02-13T09:27:48.616149Z",
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     "shell.execute_reply.started": "2022-02-13T09:27:48.616107Z"
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   "source": [
    "test_data['row_id']=test['row_id']"
   ]
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  {
   "cell_type": "code",
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     "iopub.execute_input": "2022-02-13T09:27:51.183039Z",
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       "<p>11121 rows × 15 columns</p>\n",
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      ],
      "text/plain": [
       "       user_id  category_id  video_id  age  followers  views  Male  Student  \\\n",
       "0         7986           12        42   14        180    138     1        1   \n",
       "1        11278           34       115   14        230    840     1        1   \n",
       "2        17245            8       110   44        280    628     0        0   \n",
       "3         9851           16       137   18        270    462     1        1   \n",
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       "11119    24626            8        16   33        280    628     1        0   \n",
       "11120      967            8        74   33        280    628     1        0   \n",
       "\n",
       "       Working Professional  engagement_score_ridge  engagement_score_lasso  \\\n",
       "0                         0                4.104768                4.104601   \n",
       "1                         0                3.839753                3.839578   \n",
       "2                         1                2.766573                2.766684   \n",
       "3                         0                4.085669                4.085269   \n",
       "4                         0                2.738871                2.738313   \n",
       "...                     ...                     ...                     ...   \n",
       "11116                     0                4.037159                4.036608   \n",
       "11117                     0                3.395434                3.395397   \n",
       "11118                     0                4.077724                4.077128   \n",
       "11119                     0                3.667593                3.667342   \n",
       "11120                     1                3.454476                3.454760   \n",
       "\n",
       "       engagement_score_knn  engagement_score_dt  engagement_score_rf  row_id  \n",
       "0                  3.401034             4.109443             4.079172   89198  \n",
       "1                  3.164138             3.831422             3.370679   89199  \n",
       "2                  3.661034             2.720686             2.592641   89200  \n",
       "3                  3.630345             4.033378             3.727879   89201  \n",
       "4                  3.018966             0.000000             1.654284   89202  \n",
       "...                     ...                  ...                  ...     ...  \n",
       "11116              3.384138             3.957541             3.426137  100314  \n",
       "11117              3.753793             3.488652             3.112287  100315  \n",
       "11118              3.367241             3.884439             3.490755  100316  \n",
       "11119              3.397241             3.730905             3.856555  100317  \n",
       "11120              3.603103             3.413830             3.324160  100318  \n",
       "\n",
       "[11121 rows x 15 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-13T09:27:57.714882Z",
     "iopub.status.busy": "2022-02-13T09:27:57.714333Z",
     "iopub.status.idle": "2022-02-13T09:27:57.849445Z",
     "shell.execute_reply": "2022-02-13T09:27:57.848754Z",
     "shell.execute_reply.started": "2022-02-13T09:27:57.714841Z"
    }
   },
   "outputs": [],
   "source": [
    "test_data.to_csv(\"predictions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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