04-categorical-variables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df7b7df1",
   "metadata": {},
   "source": [
    "# Two types of categorical variables/features/predictors\n",
    "\n",
    "1. Nominal: Don't have any ordering between data  \n",
    "    Eg: (male,female), (red,green,blue)  \n",
    "    Soln: One hot encoding (Create separate column for each distinct value)\n",
    "2. Ordinal: Have ordering between data  \n",
    "    Eg: (satisified,neutral,dissatisfied), (high,medium,low)  \n",
    "    Soln: Assign numbers from 0 to k-1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112ce72d",
   "metadata": {},
   "source": [
    "## Import libraries and collect data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b608a1c0",
   "metadata": {
    "scrolled": true
   },
   "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>town</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2600</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>3000</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>3200</td>\n",
       "      <td>6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>3600</td>\n",
       "      <td>6800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chennai</td>\n",
       "      <td>4000</td>\n",
       "      <td>7250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2600</td>\n",
       "      <td>5850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2800</td>\n",
       "      <td>6150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kolkata</td>\n",
       "      <td>3300</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      town  area  price\n",
       "0    delhi  2600   5500\n",
       "1    delhi  3000   5650\n",
       "2   mumbai  3200   6100\n",
       "3   mumbai  3600   6800\n",
       "4  chennai  4000   7250\n",
       "5  chennai  2600   5850\n",
       "6  chennai  2800   6150\n",
       "7  kolkata  3300   6500"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "df = pd.DataFrame({\n",
    "    \"town\": [\"delhi\", \"delhi\", \"mumbai\", \"mumbai\", \"chennai\", \"chennai\", \"chennai\", \"kolkata\"],\n",
    "    \"area\": [2600,3000,3200,3600,4000,2600,2800,3300],\n",
    "    \"price\": [5500,5650,6100,6800,7250,5850,6150,6500]\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd0b066",
   "metadata": {},
   "source": [
    "## Analyze data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "110a14c4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='area', ylabel='price'>"
      ]
     },
     "execution_count": 68,
     "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": [
    "df.plot.scatter(x=\"area\", y=\"price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b71d1b4",
   "metadata": {},
   "source": [
    "## Clean data (one hot encoding, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2e4387a7",
   "metadata": {
    "scrolled": false
   },
   "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>chennai</th>\n",
       "      <th>delhi</th>\n",
       "      <th>kolkata</th>\n",
       "      <th>mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chennai  delhi  kolkata  mumbai\n",
       "0        0      1        0       0\n",
       "1        0      1        0       0\n",
       "2        0      0        0       1\n",
       "3        0      0        0       1\n",
       "4        1      0        0       0\n",
       "5        1      0        0       0\n",
       "6        1      0        0       0\n",
       "7        0      0        1       0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(df[\"town\"])\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9d7b7b61",
   "metadata": {
    "scrolled": true
   },
   "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>town</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>chennai</th>\n",
       "      <th>delhi</th>\n",
       "      <th>kolkata</th>\n",
       "      <th>mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>delhi</td>\n",
       "      <td>2600</td>\n",
       "      <td>5500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>3000</td>\n",
       "      <td>5650</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>3200</td>\n",
       "      <td>6100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>3600</td>\n",
       "      <td>6800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chennai</td>\n",
       "      <td>4000</td>\n",
       "      <td>7250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2600</td>\n",
       "      <td>5850</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chennai</td>\n",
       "      <td>2800</td>\n",
       "      <td>6150</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kolkata</td>\n",
       "      <td>3300</td>\n",
       "      <td>6500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      town  area  price  chennai  delhi  kolkata  mumbai\n",
       "0    delhi  2600   5500        0      1        0       0\n",
       "1    delhi  3000   5650        0      1        0       0\n",
       "2   mumbai  3200   6100        0      0        0       1\n",
       "3   mumbai  3600   6800        0      0        0       1\n",
       "4  chennai  4000   7250        1      0        0       0\n",
       "5  chennai  2600   5850        1      0        0       0\n",
       "6  chennai  2800   6150        1      0        0       0\n",
       "7  kolkata  3300   6500        0      0        1       0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df,dummies], axis=\"columns\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cd64d43e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>chennai</th>\n",
       "      <th>delhi</th>\n",
       "      <th>kolkata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>5500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>5650</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>6100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>6800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>7250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2600</td>\n",
       "      <td>5850</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2800</td>\n",
       "      <td>6150</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3300</td>\n",
       "      <td>6500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  price  chennai  delhi  kolkata\n",
       "0  2600   5500        0      1        0\n",
       "1  3000   5650        0      1        0\n",
       "2  3200   6100        0      0        0\n",
       "3  3600   6800        0      0        0\n",
       "4  4000   7250        1      0        0\n",
       "5  2600   5850        1      0        0\n",
       "6  2800   6150        1      0        0\n",
       "7  3300   6500        0      0        1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop([\"town\", \"mumbai\"], axis=\"columns\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a80a6e27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>area</th>\n",
       "      <th>chennai</th>\n",
       "      <th>delhi</th>\n",
       "      <th>kolkata</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2800</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  chennai  delhi  kolkata  price\n",
       "0  2600        0      1        0   5500\n",
       "1  3000        0      1        0   5650\n",
       "2  3200        0      0        0   6100\n",
       "3  3600        0      0        0   6800\n",
       "4  4000        1      0        0   7250\n",
       "5  2600        1      0        0   5850\n",
       "6  2800        1      0        0   6150\n",
       "7  3300        0      0        1   6500"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optional (reordering columns)\n",
    "df.insert(len(df.columns)-1,\"price\", df.pop(\"price\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aace2a6",
   "metadata": {},
   "source": [
    "## Create model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "407deb67",
   "metadata": {},
   "outputs": [],
   "source": [
    "mymodel = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "303a1006",
   "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>area</th>\n",
       "      <th>chennai</th>\n",
       "      <th>delhi</th>\n",
       "      <th>kolkata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2800</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  chennai  delhi  kolkata\n",
       "0  2600        0      1        0\n",
       "1  3000        0      1        0\n",
       "2  3200        0      0        0\n",
       "3  3600        0      0        0\n",
       "4  4000        1      0        0\n",
       "5  2600        1      0        0\n",
       "6  2800        1      0        0\n",
       "7  3300        0      0        1"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(\"price\", axis=\"columns\")\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1e25cf55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5500\n",
       "1    5650\n",
       "2    6100\n",
       "3    6800\n",
       "4    7250\n",
       "5    5850\n",
       "6    6150\n",
       "7    6500\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df[\"price\"]\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "268628cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymodel.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "20d8f389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5723.21428571])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymodel.predict([[2800,0,1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "730cf62e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666666"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymodel.score(X,Y)"
   ]
  }
 ],
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