future prediction sales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "54ab2190",
   "metadata": {},
   "source": [
    "# Future Sales Prediction (Case Study)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a3a8e3d",
   "metadata": {},
   "source": [
    "#### TV: Advertising cost spent in dollars for advertising on TV;\n",
    "#### Radio: Advertising cost spent in dollars for advertising on Radio;\n",
    "#### Newspaper: Advertising cost spent in dollars for advertising on Newspaper;\n",
    "#### Sales: Number of units sold;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02dc280",
   "metadata": {},
   "source": [
    "### import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f0e92da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as mp\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d7081e",
   "metadata": {},
   "source": [
    "### load the dataset using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f48b11fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('advertising.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0e3189",
   "metadata": {},
   "source": [
    "### just a glimpse of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a0458f4",
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3   12.0\n",
       "3  151.5   41.3       58.5   16.5\n",
       "4  180.8   10.8       58.4   17.9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342c8736",
   "metadata": {},
   "source": [
    "### get the total number of rows & columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48c34dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca45d6b",
   "metadata": {},
   "source": [
    "### Check for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "998dde20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           0\n",
       "Radio        0\n",
       "Newspaper    0\n",
       "Sales        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37e5a8e6",
   "metadata": {},
   "source": [
    "hence, there is no missing values in this dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "717fed25",
   "metadata": {},
   "source": [
    "### Now let's visualize the relation of each item with the advertising cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30f24e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sales', ylabel='TV'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.scatterplot(x='Sales',y='TV',data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "376e2e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sales', ylabel='Radio'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.scatterplot(x='Sales', y='Radio',data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cab9105e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sales', ylabel='Newspaper'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.scatterplot(x='Sales',y='Newspaper',data=dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad1e5c8",
   "metadata": {},
   "source": [
    "### let's get the Maximum spent on each item for their advertisiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba1f54cb",
   "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>TV</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>276.9</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Sales\n",
       "175  276.9   27.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['TV','Sales']][dataset['Sales'] == dataset['Sales'].max()] # TV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c2c0d75",
   "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>Radio</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>48.9</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Radio  Sales\n",
       "175   48.9   27.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['Radio','Sales']][dataset['Sales'] == dataset['Sales'].max()] # radio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64d75be3",
   "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>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>41.8</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Newspaper  Sales\n",
       "175       41.8   27.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['Newspaper','Sales']][dataset['Sales'] == dataset['Sales'].max()] # Newspaper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2e4ba2",
   "metadata": {},
   "source": [
    "# Now Segregate the feature and the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d8ea2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop('Sales',axis=1)\n",
    "Y = dataset['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e5100c77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        TV  Radio  Newspaper\n",
      "0    230.1   37.8       69.2\n",
      "1     44.5   39.3       45.1\n",
      "2     17.2   45.9       69.3\n",
      "3    151.5   41.3       58.5\n",
      "4    180.8   10.8       58.4\n",
      "..     ...    ...        ...\n",
      "195   38.2    3.7       13.8\n",
      "196   94.2    4.9        8.1\n",
      "197  177.0    9.3        6.4\n",
      "198  283.6   42.0       66.2\n",
      "199  232.1    8.6        8.7\n",
      "\n",
      "[200 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "94872df0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      22.1\n",
      "1      10.4\n",
      "2      12.0\n",
      "3      16.5\n",
      "4      17.9\n",
      "       ... \n",
      "195     7.6\n",
      "196    14.0\n",
      "197    14.8\n",
      "198    25.5\n",
      "199    18.4\n",
      "Name: Sales, Length: 200, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "674bd499",
   "metadata": {},
   "source": [
    "### Now let's Train & Test the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3d3cfd31",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b9804755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 3) (160, 3) (40, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c082a7",
   "metadata": {},
   "source": [
    "### Create a Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bd7898ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "953c6d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e020ed6",
   "metadata": {},
   "source": [
    "### Check for Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2142847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.20706464620405"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train,Y_train)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694d3443",
   "metadata": {},
   "source": [
    "91% accuracy in training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98cd655c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.07131803267819"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dfd62e5",
   "metadata": {},
   "source": [
    "84% accuracy in testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4b92be",
   "metadata": {},
   "source": [
    "### now let's predict the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f613451",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9179a46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>17.1</td>\n",
       "      <td>15.817942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>10.150324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>8.7</td>\n",
       "      <td>8.267378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>18.4</td>\n",
       "      <td>18.255807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>19.6</td>\n",
       "      <td>18.064664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>20.2</td>\n",
       "      <td>17.243546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.6</td>\n",
       "      <td>8.812945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>20.2</td>\n",
       "      <td>22.045131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>12.6</td>\n",
       "      <td>12.382082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>17.8</td>\n",
       "      <td>21.036911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.2</td>\n",
       "      <td>9.582875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>19.4</td>\n",
       "      <td>20.137920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.7</td>\n",
       "      <td>10.696927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.6</td>\n",
       "      <td>9.052170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>16.5</td>\n",
       "      <td>17.194038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.336535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>8.5</td>\n",
       "      <td>8.634905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.5</td>\n",
       "      <td>17.273898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>20.9</td>\n",
       "      <td>18.249307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19.0</td>\n",
       "      <td>19.292791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20.5</td>\n",
       "      <td>18.940260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.0</td>\n",
       "      <td>19.461683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.8</td>\n",
       "      <td>11.112987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>10.8</td>\n",
       "      <td>10.667581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>16.9</td>\n",
       "      <td>18.031649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>16.7</td>\n",
       "      <td>14.584149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>16.1</td>\n",
       "      <td>16.583238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>11.3</td>\n",
       "      <td>9.279118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>19.8</td>\n",
       "      <td>18.821604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>16.6</td>\n",
       "      <td>17.185589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>16.8</td>\n",
       "      <td>20.112118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.229506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.6</td>\n",
       "      <td>15.879363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>13.2</td>\n",
       "      <td>14.055080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5.3</td>\n",
       "      <td>7.896999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>12.4</td>\n",
       "      <td>12.124556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>24.7</td>\n",
       "      <td>21.746080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>20.2</td>\n",
       "      <td>21.407698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>17.7</td>\n",
       "      <td>19.582250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>21.2</td>\n",
       "      <td>19.500427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual   Predicted \n",
       "112     17.1   15.817942\n",
       "29      10.5   10.150324\n",
       "182      8.7    8.267378\n",
       "199     18.4   18.255807\n",
       "193     19.6   18.064664\n",
       "85      20.2   17.243546\n",
       "10      12.6    8.812945\n",
       "54      20.2   22.045131\n",
       "115     12.6   12.382082\n",
       "35      17.8   21.036911\n",
       "12       9.2    9.582875\n",
       "92      19.4   20.137920\n",
       "13      13.7   10.696927\n",
       "126      6.6    9.052170\n",
       "174     16.5   17.194038\n",
       "2       12.0   10.336535\n",
       "44       8.5    8.634905\n",
       "3       16.5   17.273898\n",
       "113     20.9   18.249307\n",
       "14      19.0   19.292791\n",
       "23      20.5   18.940260\n",
       "25      17.0   19.461683\n",
       "6       11.8   11.112987\n",
       "134     10.8   10.667581\n",
       "165     16.9   18.031649\n",
       "173     16.7   14.584149\n",
       "45      16.1   16.583238\n",
       "65      11.3    9.279118\n",
       "48      19.8   18.821604\n",
       "122     16.6   17.185589\n",
       "178     16.8   20.112118\n",
       "64      16.0   16.229506\n",
       "9       15.6   15.879363\n",
       "57      13.2   14.055080\n",
       "78       5.3    7.896999\n",
       "71      12.4   12.124556\n",
       "128     24.7   21.746080\n",
       "176     20.2   21.407698\n",
       "131     17.7   19.582250\n",
       "53      21.2   19.500427"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_check = pd.DataFrame({'Actual ' : Y_test, 'Predicted ' : prediction})\n",
    "cross_check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "181eac7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.29072756])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[230.1,37.8,69.2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58b20b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Conclusion :\n",
    "    - It's a Linear Regression Model\n",
    "    - It will predict sales on the advertisment of an item\n",
    "    - This model is not predicting with 100% accuracy but not that bad prediction"
   ]
  }
 ],
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