Upload 5 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+input_raw_data.xlsx filter=lfs diff=lfs merge=lfs -text

AajTak_Model.ipynb

@@ -0,0 +1,2098 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5849e5b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import required packages\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, train_test_split\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
+    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b77e2c3",
+   "metadata": {},
+   "source": [
+    "## Preporcessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3725e933",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Week Day</th>\n",
+       "      <th>TimeBand</th>\n",
+       "      <th>Share</th>\n",
+       "      <th>AMA</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>daily reach</th>\n",
+       "      <th>cume reach</th>\n",
+       "      <th>ATS</th>\n",
+       "      <th>Unrolled</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>02:00:00 - 02:30:00</td>\n",
+       "      <td>0.081305</td>\n",
+       "      <td>0.123363</td>\n",
+       "      <td>0.000433</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>3.700893</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>02:30:00 - 03:00:00</td>\n",
+       "      <td>0.469995</td>\n",
+       "      <td>0.394070</td>\n",
+       "      <td>0.001383</td>\n",
+       "      <td>11.82</td>\n",
+       "      <td>11.822103</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>03:00:00 - 03:30:00</td>\n",
+       "      <td>1.723084</td>\n",
+       "      <td>0.361537</td>\n",
+       "      <td>0.001269</td>\n",
+       "      <td>10.85</td>\n",
+       "      <td>10.846120</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>03:30:00 - 04:00:00</td>\n",
+       "      <td>2.019206</td>\n",
+       "      <td>0.251790</td>\n",
+       "      <td>0.000884</td>\n",
+       "      <td>7.55</td>\n",
+       "      <td>7.553692</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>04:00:00 - 04:30:00</td>\n",
+       "      <td>1.163916</td>\n",
+       "      <td>0.333603</td>\n",
+       "      <td>0.001171</td>\n",
+       "      <td>10.01</td>\n",
+       "      <td>10.008100</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.000000</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12091</th>\n",
+       "      <td>15'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Friday</td>\n",
+       "      <td>23:30:00 - 24:00:00</td>\n",
+       "      <td>0.315975</td>\n",
+       "      <td>6.315608</td>\n",
+       "      <td>0.028382</td>\n",
+       "      <td>52.33</td>\n",
+       "      <td>52.334241</td>\n",
+       "      <td>00:03:37</td>\n",
+       "      <td>1.870176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12092</th>\n",
+       "      <td>15'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Friday</td>\n",
+       "      <td>24:00:00 - 24:30:00</td>\n",
+       "      <td>0.690376</td>\n",
+       "      <td>8.010992</td>\n",
+       "      <td>0.036001</td>\n",
+       "      <td>33.65</td>\n",
+       "      <td>33.651447</td>\n",
+       "      <td>00:07:09</td>\n",
+       "      <td>6.204409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12093</th>\n",
+       "      <td>15'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Friday</td>\n",
+       "      <td>24:30:00 - 25:00:00</td>\n",
+       "      <td>1.313761</td>\n",
+       "      <td>8.575085</td>\n",
+       "      <td>0.038536</td>\n",
+       "      <td>26.97</td>\n",
+       "      <td>26.974041</td>\n",
+       "      <td>00:09:32</td>\n",
+       "      <td>6.526442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12094</th>\n",
+       "      <td>15'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Friday</td>\n",
+       "      <td>25:00:00 - 25:30:00</td>\n",
+       "      <td>1.141046</td>\n",
+       "      <td>4.483507</td>\n",
+       "      <td>0.020149</td>\n",
+       "      <td>37.21</td>\n",
+       "      <td>37.214790</td>\n",
+       "      <td>00:03:37</td>\n",
+       "      <td>5.011646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12095</th>\n",
+       "      <td>15'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Friday</td>\n",
+       "      <td>25:30:00 - 26:00:00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>12096 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Unnamed: 0  Channel  Week Day             TimeBand     Share       AMA  \\\n",
+       "0           7'23  Aaj Tak  Saturday  02:00:00 - 02:30:00  0.081305  0.123363   \n",
+       "1           7'23  Aaj Tak  Saturday  02:30:00 - 03:00:00  0.469995  0.394070   \n",
+       "2           7'23  Aaj Tak  Saturday  03:00:00 - 03:30:00  1.723084  0.361537   \n",
+       "3           7'23  Aaj Tak  Saturday  03:30:00 - 04:00:00  2.019206  0.251790   \n",
+       "4           7'23  Aaj Tak  Saturday  04:00:00 - 04:30:00  1.163916  0.333603   \n",
+       "...          ...      ...       ...                  ...       ...       ...   \n",
+       "12091      15'23  Aaj Tak    Friday  23:30:00 - 24:00:00  0.315975  6.315608   \n",
+       "12092      15'23  Aaj Tak    Friday  24:00:00 - 24:30:00  0.690376  8.010992   \n",
+       "12093      15'23  Aaj Tak    Friday  24:30:00 - 25:00:00  1.313761  8.575085   \n",
+       "12094      15'23  Aaj Tak    Friday  25:00:00 - 25:30:00  1.141046  4.483507   \n",
+       "12095      15'23  Aaj Tak    Friday  25:30:00 - 26:00:00  0.000000  0.000000   \n",
+       "\n",
+       "           rate  daily reach  cume reach       ATS  Unrolled  \n",
+       "0      0.000433         3.70    3.700893  00:01:00  0.000000  \n",
+       "1      0.001383        11.82   11.822103  00:01:00  0.000000  \n",
+       "2      0.001269        10.85   10.846120  00:01:00  0.000000  \n",
+       "3      0.000884         7.55    7.553692  00:01:00  0.000000  \n",
+       "4      0.001171        10.01   10.008100  00:01:00  0.000000  \n",
+       "...         ...          ...         ...       ...       ...  \n",
+       "12091  0.028382        52.33   52.334241  00:03:37  1.870176  \n",
+       "12092  0.036001        33.65   33.651447  00:07:09  6.204409  \n",
+       "12093  0.038536        26.97   26.974041  00:09:32  6.526442  \n",
+       "12094  0.020149        37.21   37.214790  00:03:37  5.011646  \n",
+       "12095  0.000000         0.00    0.000000         0  0.000000  \n",
+       "\n",
+       "[12096 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read the dataset\n",
+    "\n",
+    "df = pd.read_excel(\"input_raw_data.xlsx\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cc260fc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.rename(columns={'Unnamed: 0':'Week number'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bfee3282",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Week number</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Week Day</th>\n",
+       "      <th>TimeBand</th>\n",
+       "      <th>Share</th>\n",
+       "      <th>AMA</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>daily reach</th>\n",
+       "      <th>cume reach</th>\n",
+       "      <th>ATS</th>\n",
+       "      <th>Unrolled</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>02:00:00 - 02:30:00</td>\n",
+       "      <td>0.081305</td>\n",
+       "      <td>0.123363</td>\n",
+       "      <td>0.000433</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>3.700893</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>02:30:00 - 03:00:00</td>\n",
+       "      <td>0.469995</td>\n",
+       "      <td>0.394070</td>\n",
+       "      <td>0.001383</td>\n",
+       "      <td>11.82</td>\n",
+       "      <td>11.822103</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>03:00:00 - 03:30:00</td>\n",
+       "      <td>1.723084</td>\n",
+       "      <td>0.361537</td>\n",
+       "      <td>0.001269</td>\n",
+       "      <td>10.85</td>\n",
+       "      <td>10.846120</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>03:30:00 - 04:00:00</td>\n",
+       "      <td>2.019206</td>\n",
+       "      <td>0.251790</td>\n",
+       "      <td>0.000884</td>\n",
+       "      <td>7.55</td>\n",
+       "      <td>7.553692</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>04:00:00 - 04:30:00</td>\n",
+       "      <td>1.163916</td>\n",
+       "      <td>0.333603</td>\n",
+       "      <td>0.001171</td>\n",
+       "      <td>10.01</td>\n",
+       "      <td>10.008100</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Week number  Channel  Week Day             TimeBand     Share       AMA  \\\n",
+       "0        7'23  Aaj Tak  Saturday  02:00:00 - 02:30:00  0.081305  0.123363   \n",
+       "1        7'23  Aaj Tak  Saturday  02:30:00 - 03:00:00  0.469995  0.394070   \n",
+       "2        7'23  Aaj Tak  Saturday  03:00:00 - 03:30:00  1.723084  0.361537   \n",
+       "3        7'23  Aaj Tak  Saturday  03:30:00 - 04:00:00  2.019206  0.251790   \n",
+       "4        7'23  Aaj Tak  Saturday  04:00:00 - 04:30:00  1.163916  0.333603   \n",
+       "\n",
+       "       rate  daily reach  cume reach       ATS  Unrolled  \n",
+       "0  0.000433         3.70    3.700893  00:01:00       0.0  \n",
+       "1  0.001383        11.82   11.822103  00:01:00       0.0  \n",
+       "2  0.001269        10.85   10.846120  00:01:00       0.0  \n",
+       "3  0.000884         7.55    7.553692  00:01:00       0.0  \n",
+       "4  0.001171        10.01   10.008100  00:01:00       0.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e53ee7c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 12096 entries, 0 to 12095\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   Week number  12096 non-null  object \n",
+      " 1   Channel      12096 non-null  object \n",
+      " 2   Week Day     12096 non-null  object \n",
+      " 3   TimeBand     12096 non-null  object \n",
+      " 4   Share        12096 non-null  float64\n",
+      " 5   AMA          12096 non-null  float64\n",
+      " 6   rate         12096 non-null  float64\n",
+      " 7   daily reach  12096 non-null  float64\n",
+      " 8   cume reach   12096 non-null  float64\n",
+      " 9   ATS          12096 non-null  object \n",
+      " 10  Unrolled     12096 non-null  float64\n",
+      "dtypes: float64(6), object(5)\n",
+      "memory usage: 1.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "31fd40e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Share</th>\n",
+       "      <th>AMA</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>daily reach</th>\n",
+       "      <th>cume reach</th>\n",
+       "      <th>Unrolled</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>12096.000000</td>\n",
+       "      <td>12096.000000</td>\n",
+       "      <td>12096.000000</td>\n",
+       "      <td>12096.000000</td>\n",
+       "      <td>12096.000000</td>\n",
+       "      <td>12096.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.904877</td>\n",
+       "      <td>3.638381</td>\n",
+       "      <td>0.031671</td>\n",
+       "      <td>30.726294</td>\n",
+       "      <td>30.726317</td>\n",
+       "      <td>3.487959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.773260</td>\n",
+       "      <td>4.987969</td>\n",
+       "      <td>0.074512</td>\n",
+       "      <td>33.505783</td>\n",
+       "      <td>33.505793</td>\n",
+       "      <td>5.746293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.089353</td>\n",
+       "      <td>0.122776</td>\n",
+       "      <td>0.003831</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.002531</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.199747</td>\n",
+       "      <td>2.192741</td>\n",
+       "      <td>0.015068</td>\n",
+       "      <td>22.730000</td>\n",
+       "      <td>22.732177</td>\n",
+       "      <td>0.974788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.482635</td>\n",
+       "      <td>5.174398</td>\n",
+       "      <td>0.029070</td>\n",
+       "      <td>46.930000</td>\n",
+       "      <td>46.932208</td>\n",
+       "      <td>4.620285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>42.072407</td>\n",
+       "      <td>1.356598</td>\n",
+       "      <td>229.330000</td>\n",
+       "      <td>229.334577</td>\n",
+       "      <td>60.765814</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Share           AMA          rate   daily reach    cume reach  \\\n",
+       "count  12096.000000  12096.000000  12096.000000  12096.000000  12096.000000   \n",
+       "mean       0.904877      3.638381      0.031671     30.726294     30.726317   \n",
+       "std        3.773260      4.987969      0.074512     33.505783     33.505793   \n",
+       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%        0.089353      0.122776      0.003831      3.000000      3.002531   \n",
+       "50%        0.199747      2.192741      0.015068     22.730000     22.732177   \n",
+       "75%        0.482635      5.174398      0.029070     46.930000     46.932208   \n",
+       "max      100.000000     42.072407      1.356598    229.330000    229.334577   \n",
+       "\n",
+       "           Unrolled  \n",
+       "count  12096.000000  \n",
+       "mean       3.487959  \n",
+       "std        5.746293  \n",
+       "min        0.000000  \n",
+       "25%        0.000000  \n",
+       "50%        0.974788  \n",
+       "75%        4.620285  \n",
+       "max       60.765814  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "741765e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Week number\n",
+       "7'23     1344\n",
+       "8'23     1344\n",
+       "9'23     1344\n",
+       "10'23    1344\n",
+       "11'23    1344\n",
+       "12'23    1344\n",
+       "13'23    1344\n",
+       "14'23    1344\n",
+       "15'23    1344\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Count values of Week number\n",
+    "df['Week number'].value_counts()   # we have records of from 7 to 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "894d2430",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Channel\n",
+       "Aaj Tak    12096\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Count values of Channel\n",
+    "df['Channel'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "abbc65aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Week Day\n",
+       "Saturday     1728\n",
+       "Sunday       1728\n",
+       "Monday       1728\n",
+       "Tuesday      1728\n",
+       "Wednesday    1728\n",
+       "Thursday     1728\n",
+       "Friday       1728\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Count values of Week Day\n",
+    "df['Week Day'].value_counts() # from Sunday to Monday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "24a0ea3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TimeBand\n",
+       "02:00:00 - 02:30:00    252\n",
+       "02:30:00 - 03:00:00    252\n",
+       "15:00:00 - 15:30:00    252\n",
+       "15:30:00 - 16:00:00    252\n",
+       "16:00:00 - 16:30:00    252\n",
+       "16:30:00 - 17:00:00    252\n",
+       "17:00:00 - 17:30:00    252\n",
+       "17:30:00 - 18:00:00    252\n",
+       "18:00:00 - 18:30:00    252\n",
+       "18:30:00 - 19:00:00    252\n",
+       "19:00:00 - 19:30:00    252\n",
+       "19:30:00 - 20:00:00    252\n",
+       "20:00:00 - 20:30:00    252\n",
+       "20:30:00 - 21:00:00    252\n",
+       "21:00:00 - 21:30:00    252\n",
+       "21:30:00 - 22:00:00    252\n",
+       "22:00:00 - 22:30:00    252\n",
+       "22:30:00 - 23:00:00    252\n",
+       "23:00:00 - 23:30:00    252\n",
+       "23:30:00 - 24:00:00    252\n",
+       "24:00:00 - 24:30:00    252\n",
+       "24:30:00 - 25:00:00    252\n",
+       "25:00:00 - 25:30:00    252\n",
+       "14:30:00 - 15:00:00    252\n",
+       "14:00:00 - 14:30:00    252\n",
+       "13:30:00 - 14:00:00    252\n",
+       "07:30:00 - 08:00:00    252\n",
+       "03:00:00 - 03:30:00    252\n",
+       "03:30:00 - 04:00:00    252\n",
+       "04:00:00 - 04:30:00    252\n",
+       "04:30:00 - 05:00:00    252\n",
+       "05:00:00 - 05:30:00    252\n",
+       "05:30:00 - 06:00:00    252\n",
+       "06:00:00 - 06:30:00    252\n",
+       "06:30:00 - 07:00:00    252\n",
+       "07:00:00 - 07:30:00    252\n",
+       "08:00:00 - 08:30:00    252\n",
+       "13:00:00 - 13:30:00    252\n",
+       "08:30:00 - 09:00:00    252\n",
+       "09:00:00 - 09:30:00    252\n",
+       "09:30:00 - 10:00:00    252\n",
+       "10:00:00 - 10:30:00    252\n",
+       "10:30:00 - 11:00:00    252\n",
+       "11:00:00 - 11:30:00    252\n",
+       "11:30:00 - 12:00:00    252\n",
+       "12:00:00 - 12:30:00    252\n",
+       "12:30:00 - 13:00:00    252\n",
+       "25:30:00 - 26:00:00    252\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# count values of TimeBand\n",
+    "df['TimeBand'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be8183bd",
+   "metadata": {},
+   "source": [
+    "## Label Encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "877e32b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Week number', 'Channel', 'Week Day', 'TimeBand', 'Share', 'AMA',\n",
+       "       'rate', 'daily reach', 'cume reach', 'ATS', 'Unrolled'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9f922296",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 12096 entries, 0 to 12095\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   Week number  12096 non-null  object \n",
+      " 1   Channel      12096 non-null  object \n",
+      " 2   Week Day     12096 non-null  object \n",
+      " 3   TimeBand     12096 non-null  object \n",
+      " 4   Share        12096 non-null  float64\n",
+      " 5   AMA          12096 non-null  float64\n",
+      " 6   rate         12096 non-null  float64\n",
+      " 7   daily reach  12096 non-null  float64\n",
+      " 8   cume reach   12096 non-null  float64\n",
+      " 9   ATS          12096 non-null  object \n",
+      " 10  Unrolled     12096 non-null  float64\n",
+      "dtypes: float64(6), object(5)\n",
+      "memory usage: 1.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "109ffb8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Need to Label Encode columns like: \n",
+    "# As of now Channel is not needed to encode as we are checking with AajTak only\n",
+    "# 1: Week Day\n",
+    "# 2: TimeBand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e4fd0b0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1: Week Day\n",
+    "\n",
+    "weekDay_le = LabelEncoder()\n",
+    "df['Week_Day_Encoded'] = weekDay_le.fit_transform(df['Week Day'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9b10dc13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# L1 = list(weekDay_le.inverse_transform(df['Week_Day_Encoded']))\n",
+    "# d1 = dict(zip(weekDay_le.classes_, weekDay_le.transform(weekDay_le.classes_)))\n",
+    "# print (d1)\n",
+    "\n",
+    "# # Output: {'Friday': 0, 'Monday': 1, 'Saturday': 2, 'Sunday': 3, 'Thursday': 4, 'Tuesday': 5, 'Wednesday': 6}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bc705800",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2: TimeBand\n",
+    "\n",
+    "timeBand_le = LabelEncoder()\n",
+    "df['Time_Band_Encoded'] = timeBand_le.fit_transform(df['TimeBand'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "16ac2be3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# L2 = list(timeBand_le.inverse_transform(df['Time_Band_Encoded']))\n",
+    "# d2 = dict(zip(timeBand_le.classes_, timeBand_le.transform(timeBand_le.classes_)))\n",
+    "# print(d2)\n",
+    "\n",
+    "# # # Output: {'02:00:00 - 02:30:00': 0, '02:30:00 - 03:00:00': 1, '03:00:00 - 03:30:00': 2, '03:30:00 - 04:00:00': 3, \n",
+    "#              '04:00:00 - 04:30:00': 4, '04:30:00 - 05:00:00': 5, '05:00:00 - 05:30:00': 6, '05:30:00 - 06:00:00': 7, \n",
+    "#              '06:00:00 - 06:30:00': 8, '06:30:00 - 07:00:00': 9, '07:00:00 - 07:30:00': 10, '07:30:00 - 08:00:00': 11, \n",
+    "#              '08:00:00 - 08:30:00': 12, '08:30:00 - 09:00:00': 13, '09:00:00 - 09:30:00': 14, '09:30:00 - 10:00:00': 15, \n",
+    "#              '10:00:00 - 10:30:00': 16, '10:30:00 - 11:00:00': 17, '11:00:00 - 11:30:00': 18, '11:30:00 - 12:00:00': 19, \n",
+    "#              '12:00:00 - 12:30:00': 20, '12:30:00 - 13:00:00': 21, '13:00:00 - 13:30:00': 22, '13:30:00 - 14:00:00': 23, \n",
+    "#              '14:00:00 - 14:30:00': 24, '14:30:00 - 15:00:00': 25, '15:00:00 - 15:30:00': 26, '15:30:00 - 16:00:00': 27, \n",
+    "#              '16:00:00 - 16:30:00': 28, '16:30:00 - 17:00:00': 29, '17:00:00 - 17:30:00': 30, '17:30:00 - 18:00:00': 31, \n",
+    "#              '18:00:00 - 18:30:00': 32, '18:30:00 - 19:00:00': 33, '19:00:00 - 19:30:00': 34, '19:30:00 - 20:00:00': 35, \n",
+    "#              '20:00:00 - 20:30:00': 36, '20:30:00 - 21:00:00': 37, '21:00:00 - 21:30:00': 38, '21:30:00 - 22:00:00': 39, \n",
+    "#              '22:00:00 - 22:30:00': 40, '22:30:00 - 23:00:00': 41, '23:00:00 - 23:30:00': 42, '23:30:00 - 24:00:00': 43, \n",
+    "#              '24:00:00 - 24:30:00': 44, '24:30:00 - 25:00:00': 45, '25:00:00 - 25:30:00': 46, '25:30:00 - 26:00:00': 47}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e65f3a9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Week number</th>\n",
+       "      <th>Channel</th>\n",
+       "      <th>Week Day</th>\n",
+       "      <th>TimeBand</th>\n",
+       "      <th>Share</th>\n",
+       "      <th>AMA</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>daily reach</th>\n",
+       "      <th>cume reach</th>\n",
+       "      <th>ATS</th>\n",
+       "      <th>Unrolled</th>\n",
+       "      <th>Week_Day_Encoded</th>\n",
+       "      <th>Time_Band_Encoded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>02:00:00 - 02:30:00</td>\n",
+       "      <td>0.081305</td>\n",
+       "      <td>0.123363</td>\n",
+       "      <td>0.000433</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>3.700893</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>02:30:00 - 03:00:00</td>\n",
+       "      <td>0.469995</td>\n",
+       "      <td>0.394070</td>\n",
+       "      <td>0.001383</td>\n",
+       "      <td>11.82</td>\n",
+       "      <td>11.822103</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>03:00:00 - 03:30:00</td>\n",
+       "      <td>1.723084</td>\n",
+       "      <td>0.361537</td>\n",
+       "      <td>0.001269</td>\n",
+       "      <td>10.85</td>\n",
+       "      <td>10.846120</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>03:30:00 - 04:00:00</td>\n",
+       "      <td>2.019206</td>\n",
+       "      <td>0.251790</td>\n",
+       "      <td>0.000884</td>\n",
+       "      <td>7.55</td>\n",
+       "      <td>7.553692</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7'23</td>\n",
+       "      <td>Aaj Tak</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>04:00:00 - 04:30:00</td>\n",
+       "      <td>1.163916</td>\n",
+       "      <td>0.333603</td>\n",
+       "      <td>0.001171</td>\n",
+       "      <td>10.01</td>\n",
+       "      <td>10.008100</td>\n",
+       "      <td>00:01:00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Week number  Channel  Week Day             TimeBand     Share       AMA  \\\n",
+       "0        7'23  Aaj Tak  Saturday  02:00:00 - 02:30:00  0.081305  0.123363   \n",
+       "1        7'23  Aaj Tak  Saturday  02:30:00 - 03:00:00  0.469995  0.394070   \n",
+       "2        7'23  Aaj Tak  Saturday  03:00:00 - 03:30:00  1.723084  0.361537   \n",
+       "3        7'23  Aaj Tak  Saturday  03:30:00 - 04:00:00  2.019206  0.251790   \n",
+       "4        7'23  Aaj Tak  Saturday  04:00:00 - 04:30:00  1.163916  0.333603   \n",
+       "\n",
+       "       rate  daily reach  cume reach       ATS  Unrolled  Week_Day_Encoded  \\\n",
+       "0  0.000433         3.70    3.700893  00:01:00       0.0                 2   \n",
+       "1  0.001383        11.82   11.822103  00:01:00       0.0                 2   \n",
+       "2  0.001269        10.85   10.846120  00:01:00       0.0                 2   \n",
+       "3  0.000884         7.55    7.553692  00:01:00       0.0                 2   \n",
+       "4  0.001171        10.01   10.008100  00:01:00       0.0                 2   \n",
+       "\n",
+       "   Time_Band_Encoded  \n",
+       "0                  0  \n",
+       "1                  1  \n",
+       "2                  2  \n",
+       "3                  3  \n",
+       "4                  4  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e604dbc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 12096 entries, 0 to 12095\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Week number        12096 non-null  object \n",
+      " 1   Channel            12096 non-null  object \n",
+      " 2   Week Day           12096 non-null  object \n",
+      " 3   TimeBand           12096 non-null  object \n",
+      " 4   Share              12096 non-null  float64\n",
+      " 5   AMA                12096 non-null  float64\n",
+      " 6   rate               12096 non-null  float64\n",
+      " 7   daily reach        12096 non-null  float64\n",
+      " 8   cume reach         12096 non-null  float64\n",
+      " 9   ATS                12096 non-null  object \n",
+      " 10  Unrolled           12096 non-null  float64\n",
+      " 11  Week_Day_Encoded   12096 non-null  int32  \n",
+      " 12  Time_Band_Encoded  12096 non-null  int32  \n",
+      "dtypes: float64(6), int32(2), object(5)\n",
+      "memory usage: 1.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcb0b705",
+   "metadata": {},
+   "source": [
+    "## Model Development : RandomForestRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f5af473f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Splitting into X and y \n",
+    "\n",
+    "X = df[['Share', 'AMA', 'rate','daily reach', 'cume reach','Week_Day_Encoded','Time_Band_Encoded']]\n",
+    "y = df[['Unrolled']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "8b74a5b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Splitting into training and testing datasets\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state = 42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "306b52f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((9676, 7), (2420, 7), (9676, 1), (2420, 1))"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "0d6b3c6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Share</th>\n",
+       "      <th>AMA</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>daily reach</th>\n",
+       "      <th>cume reach</th>\n",
+       "      <th>Week_Day_Encoded</th>\n",
+       "      <th>Time_Band_Encoded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11232</th>\n",
+       "      <td>0.043364</td>\n",
+       "      <td>0.080953</td>\n",
+       "      <td>0.000357</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>2.428586</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11118</th>\n",
+       "      <td>0.319280</td>\n",
+       "      <td>7.050287</td>\n",
+       "      <td>0.031111</td>\n",
+       "      <td>45.37</td>\n",
+       "      <td>45.372124</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9301</th>\n",
+       "      <td>0.090855</td>\n",
+       "      <td>5.284389</td>\n",
+       "      <td>0.023781</td>\n",
+       "      <td>60.32</td>\n",
+       "      <td>60.317940</td>\n",
+       "      <td>6</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3222</th>\n",
+       "      <td>0.402614</td>\n",
+       "      <td>0.207835</td>\n",
+       "      <td>0.000917</td>\n",
+       "      <td>4.82</td>\n",
+       "      <td>4.815343</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10322</th>\n",
+       "      <td>12.873856</td>\n",
+       "      <td>0.064336</td>\n",
+       "      <td>0.015220</td>\n",
+       "      <td>1.93</td>\n",
+       "      <td>1.930081</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Share       AMA      rate  daily reach  cume reach  \\\n",
+       "11232   0.043364  0.080953  0.000357         2.43    2.428586   \n",
+       "11118   0.319280  7.050287  0.031111        45.37   45.372124   \n",
+       "9301    0.090855  5.284389  0.023781        60.32   60.317940   \n",
+       "3222    0.402614  0.207835  0.000917         4.82    4.815343   \n",
+       "10322  12.873856  0.064336  0.015220         1.93    1.930081   \n",
+       "\n",
+       "       Week_Day_Encoded  Time_Band_Encoded  \n",
+       "11232                 5                  0  \n",
+       "11118                 2                 30  \n",
+       "9301                  6                 37  \n",
+       "3222                  6                  6  \n",
+       "10322                 4                  2  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "38e2d59b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unrolled</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11232</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11118</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9301</th>\n",
+       "      <td>6.285889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3222</th>\n",
+       "      <td>0.473240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10322</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unrolled\n",
+       "11232  0.000000\n",
+       "11118  0.000000\n",
+       "9301   6.285889\n",
+       "3222   0.473240\n",
+       "10322  0.000000"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "0fc7342f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Share</th>\n",
+       "      <th>AMA</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>daily reach</th>\n",
+       "      <th>cume reach</th>\n",
+       "      <th>Week_Day_Encoded</th>\n",
+       "      <th>Time_Band_Encoded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>0.152596</td>\n",
+       "      <td>9.820626</td>\n",
+       "      <td>0.043337</td>\n",
+       "      <td>94.61</td>\n",
+       "      <td>94.614234</td>\n",
+       "      <td>1</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11620</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>0.969294</td>\n",
+       "      <td>3.181874</td>\n",
+       "      <td>0.014043</td>\n",
+       "      <td>34.30</td>\n",
+       "      <td>34.298911</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5265</th>\n",
+       "      <td>0.064741</td>\n",
+       "      <td>2.991051</td>\n",
+       "      <td>0.013427</td>\n",
+       "      <td>41.62</td>\n",
+       "      <td>41.619074</td>\n",
+       "      <td>6</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7484</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Share       AMA      rate  daily reach  cume reach  \\\n",
+       "468    0.152596  9.820626  0.043337        94.61   94.614234   \n",
+       "11620  0.000000  0.000000  0.000000         0.00    0.000000   \n",
+       "538    0.969294  3.181874  0.014043        34.30   34.298911   \n",
+       "5265   0.064741  2.991051  0.013427        41.62   41.619074   \n",
+       "7484   0.000000  0.000000  0.000000         0.00    0.000000   \n",
+       "\n",
+       "       Week_Day_Encoded  Time_Band_Encoded  \n",
+       "468                   1                 36  \n",
+       "11620                 6                  4  \n",
+       "538                   6                 10  \n",
+       "5265                  6                 33  \n",
+       "7484                  3                 44  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "af5394e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unrolled</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>12.150886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11620</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>1.480424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5265</th>\n",
+       "      <td>5.781056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7484</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Unrolled\n",
+       "468    12.150886\n",
+       "11620   0.000000\n",
+       "538     1.480424\n",
+       "5265    5.781056\n",
+       "7484    0.000000"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "ad1452db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor(random_state=42)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train Random Forest Regression model\n",
+    "\n",
+    "model = RandomForestRegressor(random_state = 42)\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "58a025a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make predictions on train data\n",
+    "\n",
+    "y_pred_train = model.predict(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "403259f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Accuracy of Training Dataset is :  95.65798927048185\n"
+     ]
+    }
+   ],
+   "source": [
+    "acc_train = r2_score(y_train, y_pred_train)\n",
+    "print(\"The Accuracy of Training Dataset is : \",acc_train*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "ac553b1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make predictions on test data\n",
+    "\n",
+    "y_pred_test = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "bc359944",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Accuracy of Test Dataset is :  71.01332045918515\n"
+     ]
+    }
+   ],
+   "source": [
+    "acc_test = r2_score(y_test, y_pred_test)\n",
+    "print(\"The Accuracy of Test Dataset is : \",acc_test*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "fa33faec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Saving Model\n",
+    "\n",
+    "# import pickle\n",
+    "\n",
+    "# with open('aajTak_model.pkl','wb') as file1:\n",
+    "#     pickle.dump(model,file1)   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f30a678",
+   "metadata": {},
+   "source": [
+    "## Hyperparameter Tuning for Random Forest Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "44bd53a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hyperparameter Tuning\n",
+    "\n",
+    "hyp_model = RandomForestRegressor()\n",
+    "\n",
+    "hyp = {\n",
+    "\"n_estimators\": np.arange(10,50,10),\n",
+    "'criterion':[\"squared_error\", \"absolute_error\"],\n",
+    "'max_depth':np.arange(3,50),\n",
+    "# 'min_samples_split':np.arange(2,5),\n",
+    "# 'min_samples_leaf':np.arange(1,5),\n",
+    "'random_state':np.arange(0,100)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "b7c9e0ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
+       "                   param_distributions={&#x27;criterion&#x27;: [&#x27;squared_error&#x27;,\n",
+       "                                                      &#x27;absolute_error&#x27;],\n",
+       "                                        &#x27;max_depth&#x27;: array([ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,\n",
+       "       20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
+       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),\n",
+       "                                        &#x27;n_estimators&#x27;: array([10, 20, 30, 40]),\n",
+       "                                        &#x27;random_state&#x27;: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
+       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
+       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
+       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
+       "                   param_distributions={&#x27;criterion&#x27;: [&#x27;squared_error&#x27;,\n",
+       "                                                      &#x27;absolute_error&#x27;],\n",
+       "                                        &#x27;max_depth&#x27;: array([ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,\n",
+       "       20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
+       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),\n",
+       "                                        &#x27;n_estimators&#x27;: array([10, 20, 30, 40]),\n",
+       "                                        &#x27;random_state&#x27;: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
+       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
+       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
+       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
+       "                   param_distributions={'criterion': ['squared_error',\n",
+       "                                                      'absolute_error'],\n",
+       "                                        'max_depth': array([ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,\n",
+       "       20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n",
+       "       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),\n",
+       "                                        'n_estimators': array([10, 20, 30, 40]),\n",
+       "                                        'random_state': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
+       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
+       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
+       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])})"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rscv = RandomizedSearchCV(hyp_model, hyp, cv=5)\n",
+    "rscv.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "f0b0d172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'random_state': 49,\n",
+       " 'n_estimators': 20,\n",
+       " 'max_depth': 39,\n",
+       " 'criterion': 'absolute_error'}"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rscv.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "0252bdea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_model = rscv.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "b23a1e56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=39, n_estimators=20,\n",
+       "                      random_state=49)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=39, n_estimators=20,\n",
+       "                      random_state=49)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor(criterion='absolute_error', max_depth=39, n_estimators=20,\n",
+       "                      random_state=49)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c2d2e731",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ypredtn = best_model.predict(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "9308b1d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Accuracy of Training Dataset after hyperparameter tuning is :  94.41670975802535\n"
+     ]
+    }
+   ],
+   "source": [
+    "acctn = r2_score(y_train, ypredtn)\n",
+    "print(\"The Accuracy of Training Dataset after hyperparameter tuning is : \",acctn*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "23cf5580",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ypredts = best_model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "d88fdedb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Accuracy of Testing Dataset after hyperparameter tuning is :  69.97941529616791\n"
+     ]
+    }
+   ],
+   "source": [
+    "accts = r2_score(y_test, ypredts)\n",
+    "print(\"The Accuracy of Testing Dataset after hyperparameter tuning is : \",accts*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "e5298c37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Saving Model\n",
+    "\n",
+    "# import pickle\n",
+    "\n",
+    "# with open('aajTak_fineTune_model.pkl','wb') as file:\n",
+    "#     pickle.dump(best_model,file)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "7a5d25ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Saving the LabelEncoders for weekDay\n",
+    "\n",
+    "# with open('weekDay_le.pkl','wb') as f1:\n",
+    "#     pickle.dump(weekDay_le,f1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "6a268e27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Saving the LabelEncoders for timeBand\n",
+    "\n",
+    "# with open('timeBand_le.pkl','wb') as f2:\n",
+    "#     pickle.dump(timeBand_le,f2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57557ac1",
+   "metadata": {},
+   "source": [
+    "## UserTest Function - Prediction Script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8cf621c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import required packages\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, train_test_split\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
+    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "62be1870",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the saved model using pickle\n",
+    "with open('aajTak_model.pkl', 'rb') as f1:\n",
+    "    model1 = pickle.load(f1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0b4e2a7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # load the saved model using pickle\n",
+    "# with open('aajTak_fineTune_model.pkl', 'rb') as file:\n",
+    "#     model = pickle.load(file)\n",
+    "\n",
+    "# Load the saved weekDay label encoder object using pickle\n",
+    "with open('weekDay_le.pkl','rb') as file1:\n",
+    "    weekDay_le = pickle.load(file1)\n",
+    "\n",
+    "# Load the saved timeBand label encoder object using pickle\n",
+    "with open('timeBand_le.pkl','rb') as file2:\n",
+    "    timeBand_le = pickle.load(file2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e3a13c4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define the prediction function\n",
+    "# X = df[['Share', 'AMA', 'rate','daily reach', 'cume reach','Week_Day_Encoded','Time_Band_Encoded']]\n",
+    "# y = df[['Unrolled']]\n",
+    "\n",
+    "\n",
+    "def predict_unrolled_value(Share, AMA, rate, daily_reach, cume_reach, Week_Day, Time_Band):\n",
+    "    \n",
+    "    # create a DataFrame with the input variables\n",
+    "    \n",
+    "    # encode the Week_Day using the loaded LabelEncoder object\n",
+    "    weekDay_encoded = weekDay_le.transform([Week_Day])[0]\n",
+    "    \n",
+    "    # encode the Time_Band using the loaded LabelEncoder object\n",
+    "    Time_Band_encoded = timeBand_le.transform([Time_Band])[0]\n",
+    "    \n",
+    "    input_data = pd.DataFrame({'Share': [Share], \n",
+    "                               'AMA': [AMA], \n",
+    "                               'rate': [rate],\n",
+    "                               'daily reach': [daily_reach], \n",
+    "                               'cume reach': [cume_reach], \n",
+    "                               'Week_Day_Encoded': [weekDay_encoded], \n",
+    "                               'Time_Band_Encoded': [Time_Band_encoded]})\n",
+    "    \n",
+    "    # make the prediction using the loaded model and input data\n",
+    "    predicted_unrolled_value = model1.predict(input_data)\n",
+    "    \n",
+    "    # return the predicted unrolled value as output\n",
+    "    return predicted_unrolled_value[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df4390e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.123954"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Function calling\n",
+    "# 0.064741\t2.991051\t0.013427\t41.62\t41.619074\t'Wednesday'\t'18:30:00 - 19:00:00' --> test input data\n",
+    "# 5.781056 --> unrolled actual value\n",
+    "\n",
+    "predict_unrolled_value(0.064741, 2.991051, 0.013427, 41.62, 41.619074, 'Wednesday', '18:30:00 - 19:00:00')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5fadb125",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.738856000000002"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 0.152596\t9.820626\t0.043337\t94.61\t94.614234\t1\t'20:00:00 - 20:30:00'\n",
+    "# 12.150886\n",
+    "predict_unrolled_value(0.152596, 9.820626, 0.043337, 94.61, 94.614234, 'Monday', '20:00:00 - 20:30:00')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3ec5b3e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.3215619"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 0.611246\t4.196084\t0.018516\t36.23\t36.231006\t'Saturday'\t''08:00:00 - 08:30:00''\n",
+    "# 3.711884\n",
+    "predict_unrolled_value(0.611246, 4.196084, 0.018516, 36.23, 36.23, 'Saturday', '08:00:00 - 08:30:00')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83a75023",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1799f490",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

aajTak_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5f28cd030817fb6bbbcaed82f2170e9d81cdf415f4af8631620b60fed3d15b9
+size 8680525

input_raw_data.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f011103414f3360afe65aec973b3674514bc66d20a9a053c250ee04f68a03b46
+size 1057440

timeBand_le.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:74ea331adc8d7dcb4cc11585b75fa42a8f77b3fda8418c70cbe1042ef21c8c4a
+size 1298

weekDay_le.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:637bc881b0bb4cb2839589ff1292f7854daf011a20c6ca79549e323dc356f5cb
+size 313