filesession.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mkGMnYQ3ktwK"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rU-lx04AktwS"
   },
   "outputs": [],
   "source": [
    "data  = pd.read_csv(r'C:\\Users\\GM\\Desktop\\Advertising.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-GjR-u2PktwT",
    "outputId": "b8b3e852-d79b-4db4-ccc8-488fc0fef098"
   },
   "outputs": [
    {
     "data": {
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       "</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>1</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>2</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>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "1    230.1   37.8       69.2   22.1\n",
       "2     44.5   39.3       45.1   10.4\n",
       "3     17.2   45.9       69.3    9.3\n",
       "4    151.5   41.3       58.5   18.5\n",
       "5    180.8   10.8       58.4   12.9\n",
       "..     ...    ...        ...    ...\n",
       "196   38.2    3.7       13.8    7.6\n",
       "197   94.2    4.9        8.1    9.7\n",
       "198  177.0    9.3        6.4   12.8\n",
       "199  283.6   42.0       66.2   25.5\n",
       "200  232.1    8.6        8.7   13.4\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vWeY2lcnktwW",
    "outputId": "d3954aa5-1558-4258-8274-47a24a633f80"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           float64\n",
       "Radio        float64\n",
       "Newspaper    float64\n",
       "Sales        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z5ssgtb2ktwX",
    "outputId": "ba5dac40-8e18-4360-83d4-b42a8efb97ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 200 entries, 1 to 200\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   TV         200 non-null    float64\n",
      " 1   Radio      200 non-null    float64\n",
      " 2   Newspaper  200 non-null    float64\n",
      " 3   Sales      200 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 7.8 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fiffsk-YktwZ",
    "outputId": "09f83ccc-ee93-47c7-bbcc-31c8ce4b1949"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TV       Radio   Newspaper       Sales\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   147.042500   23.264000   30.554000   14.022500\n",
       "std     85.854236   14.846809   21.778621    5.217457\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.375000    9.975000   12.750000   10.375000\n",
       "50%    149.750000   22.900000   25.750000   12.900000\n",
       "75%    218.825000   36.525000   45.100000   17.400000\n",
       "max    296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TGQ9M1Vyktwc",
    "outputId": "b339d6b3-2309-48ef-c547-2ae4e03fc1fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TV', 'Radio', 'Newspaper', 'Sales'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XmdRbtQHktwe"
   },
   "outputs": [],
   "source": [
    "x = data[['TV','Radio','Newspaper']]\n",
    "y= data[['Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vWNJSP8uktwf",
    "outputId": "e47241bf-98ec-4413-a32c-f1c104ca209d"
   },
   "outputs": [
    {
     "data": {
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper\n",
       "1    230.1   37.8       69.2\n",
       "2     44.5   39.3       45.1\n",
       "3     17.2   45.9       69.3\n",
       "4    151.5   41.3       58.5\n",
       "5    180.8   10.8       58.4\n",
       "..     ...    ...        ...\n",
       "196   38.2    3.7       13.8\n",
       "197   94.2    4.9        8.1\n",
       "198  177.0    9.3        6.4\n",
       "199  283.6   42.0       66.2\n",
       "200  232.1    8.6        8.7\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wTclyDq6ktwj",
    "outputId": "779aa806-6c6e-4c7a-d504-83a77884623b"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales\n",
       "1     22.1\n",
       "2     10.4\n",
       "3      9.3\n",
       "4     18.5\n",
       "5     12.9\n",
       "..     ...\n",
       "196    7.6\n",
       "197    9.7\n",
       "198   12.8\n",
       "199   25.5\n",
       "200   13.4\n",
       "\n",
       "[200 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tiRrAr2Fktwl",
    "outputId": "3be7f2e4-20e5-4d26-e3bc-07299b993c06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vk_16adEktwl",
    "outputId": "b3f27376-b8d1-4f19-cdc4-3aef186149f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GdMotbnsktwm",
    "outputId": "d3b6301c-e3f9-48fd-956a-069650e7e617"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0TmBhP--ktwn"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y ,test_size=0.2) # 20 -30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mrFimyjqktwn",
    "outputId": "a7a284ad-7fca-48dd-e327-a44e7083b1c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 3)\n",
      "(40, 3)\n",
      "\n",
      "(160, 1)\n",
      "(40, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print()\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_ueTi10kktwn"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W1mZ2m2Tktwo",
    "outputId": "d8196bc6-a23d-4747-f820-5dd14c0919da"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QMHeOqLUktwo"
   },
   "outputs": [],
   "source": [
    "# y = b0 + b1x1 + b2x2 + b3x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZwtYmWoMktwp",
    "outputId": "153f30e7-ab75-4d7b-883f-a4653d268b1d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.04618225 0.18808105 0.00272521]]\n"
     ]
    }
   ],
   "source": [
    "print(lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sLx_4Oetktwp",
    "outputId": "c3a6a672-ea74-4820-c9f3-9bebc0f6d4fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.83842971]\n"
     ]
    }
   ],
   "source": [
    "print(lm.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Qznd5UARktwp",
    "outputId": "7f60e332-bdab-4b25-f2a7-ec26ffb36917"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MQDnwgznktwp"
   },
   "outputs": [],
   "source": [
    "x1 =250\n",
    "x2= 14\n",
    "x3 = 67\n",
    "\n",
    "y = 2.85645114 + (0.04713151*x1)+(0.17860138*x2)+(0.00258433*x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VM2vFqHGktwq",
    "outputId": "d2ad3cbd-0c9c-4de8-9ce8-6e0fb6de1dc4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.31289807"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CGJLBeajktwq"
   },
   "outputs": [],
   "source": [
    "y_pred = lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ac_Zy8goktwq",
    "outputId": "511cdf0a-22fd-484a-b145-877f0ae015d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14.53045814],\n",
       "       [ 8.57346123],\n",
       "       [ 8.13527631],\n",
       "       [24.31278606],\n",
       "       [13.78453752],\n",
       "       [10.79580386],\n",
       "       [ 8.83490205],\n",
       "       [ 6.57369443],\n",
       "       [10.33538507],\n",
       "       [15.70398947],\n",
       "       [19.63891825],\n",
       "       [14.96262029],\n",
       "       [16.46751553],\n",
       "       [14.53874025],\n",
       "       [ 3.58841494],\n",
       "       [16.43870501],\n",
       "       [12.6417692 ],\n",
       "       [ 9.29051624],\n",
       "       [15.50585552],\n",
       "       [16.87949617],\n",
       "       [17.89406099],\n",
       "       [14.18826341],\n",
       "       [12.47561532],\n",
       "       [20.76301312],\n",
       "       [11.57796741],\n",
       "       [ 7.37942613],\n",
       "       [15.69116802],\n",
       "       [15.32348848],\n",
       "       [15.75912836],\n",
       "       [ 7.54155088],\n",
       "       [17.73663373],\n",
       "       [ 4.46578305],\n",
       "       [14.05382565],\n",
       "       [ 7.69122209],\n",
       "       [12.40856578],\n",
       "       [13.17143737],\n",
       "       [17.76221233],\n",
       "       [14.49547837],\n",
       "       [11.91241931],\n",
       "       [19.36919166]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pB8bF8Ayktwr",
    "outputId": "a8953301-d65f-4b0a-ea3b-e02ab9847d66"
   },
   "outputs": [
    {
     "data": {
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       "<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>actual sales</th>\n",
       "      <th>predicted sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>141.3</td>\n",
       "      <td>26.8</td>\n",
       "      <td>46.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.530458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>7.3</td>\n",
       "      <td>28.1</td>\n",
       "      <td>41.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.573461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>62.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>18.3</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.135276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>289.7</td>\n",
       "      <td>42.3</td>\n",
       "      <td>51.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>24.312786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>222.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>11.5</td>\n",
       "      <td>13.784538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>17.9</td>\n",
       "      <td>37.6</td>\n",
       "      <td>21.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.795804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>104.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>34.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>8.834902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>49.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.573694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>26.8</td>\n",
       "      <td>33.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>10.335385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>74.7</td>\n",
       "      <td>49.4</td>\n",
       "      <td>45.7</td>\n",
       "      <td>14.7</td>\n",
       "      <td>15.703989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>184.9</td>\n",
       "      <td>43.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>20.7</td>\n",
       "      <td>19.638918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>225.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>56.5</td>\n",
       "      <td>13.4</td>\n",
       "      <td>14.962620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>163.3</td>\n",
       "      <td>31.6</td>\n",
       "      <td>52.9</td>\n",
       "      <td>16.9</td>\n",
       "      <td>16.467516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>234.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>84.8</td>\n",
       "      <td>11.9</td>\n",
       "      <td>14.538740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>13.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>25.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.588415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>227.2</td>\n",
       "      <td>15.8</td>\n",
       "      <td>49.9</td>\n",
       "      <td>14.8</td>\n",
       "      <td>16.438705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.7</td>\n",
       "      <td>48.9</td>\n",
       "      <td>75.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>12.641769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>100.4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.7</td>\n",
       "      <td>9.290516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>187.8</td>\n",
       "      <td>21.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>15.505856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>239.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>15.7</td>\n",
       "      <td>16.879496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>199.1</td>\n",
       "      <td>30.6</td>\n",
       "      <td>38.7</td>\n",
       "      <td>18.3</td>\n",
       "      <td>17.894061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>147.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>19.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>14.188263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>131.7</td>\n",
       "      <td>18.4</td>\n",
       "      <td>34.6</td>\n",
       "      <td>12.9</td>\n",
       "      <td>12.475615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "      <td>20.763013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>76.3</td>\n",
       "      <td>27.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.577967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>50.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.379426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>262.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.691168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>240.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.2</td>\n",
       "      <td>15.323488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>110.7</td>\n",
       "      <td>40.6</td>\n",
       "      <td>63.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.759128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>95.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.541551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>280.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>21.4</td>\n",
       "      <td>14.8</td>\n",
       "      <td>17.736634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>27.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>20.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.465783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>222.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>49.8</td>\n",
       "      <td>11.7</td>\n",
       "      <td>14.053826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>97.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>7.691222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>39.5</td>\n",
       "      <td>41.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>12.408566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>102.7</td>\n",
       "      <td>29.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.171437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>17.762212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>164.5</td>\n",
       "      <td>20.9</td>\n",
       "      <td>47.4</td>\n",
       "      <td>14.5</td>\n",
       "      <td>14.495478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>88.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>73.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>11.912419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>220.5</td>\n",
       "      <td>33.2</td>\n",
       "      <td>37.9</td>\n",
       "      <td>20.1</td>\n",
       "      <td>19.369192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  actual sales  predicted sales\n",
       "121  141.3   26.8       46.2          15.5        14.530458\n",
       "57     7.3   28.1       41.4           5.5         8.573461\n",
       "25    62.3   12.6       18.3           9.7         8.135276\n",
       "99   289.7   42.3       51.2          25.4        24.312786\n",
       "175  222.4    3.4       13.1          11.5        13.784538\n",
       "167   17.9   37.6       21.6           8.0        10.795804\n",
       "144  104.6    5.7       34.4          10.4         8.834902\n",
       "23    13.2   15.9       49.6           5.6         6.573694\n",
       "73    26.8   33.0       19.3           8.8        10.335385\n",
       "38    74.7   49.4       45.7          14.7        15.703989\n",
       "140  184.9   43.9        1.7          20.7        19.638918\n",
       "111  225.8    8.2       56.5          13.4        14.962620\n",
       "96   163.3   31.6       52.9          16.9        16.467516\n",
       "166  234.5    3.4       84.8          11.9        14.538740\n",
       "109   13.1    0.4       25.6           5.3         3.588415\n",
       "49   227.2   15.8       49.9          14.8        16.438705\n",
       "6      8.7   48.9       75.0           7.2        12.641769\n",
       "52   100.4    9.6        3.6          10.7         9.290516\n",
       "155  187.8   21.1        9.5          15.6        15.505856\n",
       "63   239.3   15.5       27.3          15.7        16.879496\n",
       "71   199.1   30.6       38.7          18.3        17.894061\n",
       "20   147.3   23.9       19.1          14.6        14.188263\n",
       "160  131.7   18.4       34.6          12.9        12.475615\n",
       "1    230.1   37.8       69.2          22.1        20.763013\n",
       "87    76.3   27.5       16.0          12.0        11.577967\n",
       "171   50.0   11.6       18.4           8.4         7.379426\n",
       "26   262.9    3.5       19.5          12.0        15.691168\n",
       "147  240.1    7.3        8.7          13.2        15.323488\n",
       "88   110.7   40.6       63.2          16.0        15.759128\n",
       "35    95.7    1.4        7.4           9.5         7.541551\n",
       "103  280.2   10.1       21.4          14.8        17.736634\n",
       "77    27.5    1.6       20.7           6.9         4.465783\n",
       "101  222.4    4.3       49.8          11.7        14.053826\n",
       "33    97.2    1.5       30.0           9.6         7.691222\n",
       "191   39.5   41.1        5.8          10.8        12.408566\n",
       "64   102.7   29.6        8.4          14.0        13.171437\n",
       "4    151.5   41.3       58.5          18.5        17.762212\n",
       "172  164.5   20.9       47.4          14.5        14.495478\n",
       "89    88.3   25.5       73.4          12.9        11.912419\n",
       "143  220.5   33.2       37.9          20.1        19.369192"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = pd.DataFrame()\n",
    "new_df = x_test\n",
    "new_df['actual sales']= y_test\n",
    "new_df['predicted sales']= y_pred\n",
    "new_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mgWxvLH9ktwr",
    "outputId": "357a1984-6406-4467-de2c-b966b94e50a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.70933127458164"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(x_train,y_train)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JsaJW1pqktws",
    "outputId": "342579bd-ecbf-43cd-cb9f-8ca69455cb61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.8336685387811924\n",
      "RMSE: 1.8557837826182997\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "r2=r2_score(y_test,y_pred)\n",
    "print(\"R-squared:\",r2)\n",
    "\n",
    "rmse=np.sqrt(mean_squared_error(y_test,y_pred))\n",
    "print(\"RMSE:\",rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fcqmZwPrktws"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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",
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