Day 48 of 75 DataSci.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "541c33e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85248aa",
   "metadata": {},
   "source": [
    "# Sci-kit Learn / sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b854ba3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1252b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('carprices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43c0a03a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24031f6e",
   "metadata": {},
   "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>Mileage</th>\n",
       "      <th>Age(yrs)</th>\n",
       "      <th>Sell Price($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69000</td>\n",
       "      <td>6</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35000</td>\n",
       "      <td>3</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57000</td>\n",
       "      <td>5</td>\n",
       "      <td>26100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22500</td>\n",
       "      <td>2</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46000</td>\n",
       "      <td>4</td>\n",
       "      <td>31500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mileage  Age(yrs)  Sell Price($)\n",
       "0    69000         6          18000\n",
       "1    35000         3          34000\n",
       "2    57000         5          26100\n",
       "3    22500         2          40000\n",
       "4    46000         4          31500"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bc75db23",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'Age(yrs)':'Age'},inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd14b99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'Sell Price($)':'Sell_Price'},inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66bdc871",
   "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>Mileage</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sell_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69000</td>\n",
       "      <td>6</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35000</td>\n",
       "      <td>3</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57000</td>\n",
       "      <td>5</td>\n",
       "      <td>26100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22500</td>\n",
       "      <td>2</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46000</td>\n",
       "      <td>4</td>\n",
       "      <td>31500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "      <td>26750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52000</td>\n",
       "      <td>5</td>\n",
       "      <td>32000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72000</td>\n",
       "      <td>6</td>\n",
       "      <td>19300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>91000</td>\n",
       "      <td>8</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67000</td>\n",
       "      <td>6</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83000</td>\n",
       "      <td>7</td>\n",
       "      <td>18700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>79000</td>\n",
       "      <td>7</td>\n",
       "      <td>19500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58780</td>\n",
       "      <td>4</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>82450</td>\n",
       "      <td>7</td>\n",
       "      <td>19400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25400</td>\n",
       "      <td>3</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>28000</td>\n",
       "      <td>2</td>\n",
       "      <td>35500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>69000</td>\n",
       "      <td>5</td>\n",
       "      <td>19700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>87600</td>\n",
       "      <td>8</td>\n",
       "      <td>12800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>52000</td>\n",
       "      <td>5</td>\n",
       "      <td>28200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mileage  Age  Sell_Price\n",
       "0     69000    6       18000\n",
       "1     35000    3       34000\n",
       "2     57000    5       26100\n",
       "3     22500    2       40000\n",
       "4     46000    4       31500\n",
       "5     59000    5       26750\n",
       "6     52000    5       32000\n",
       "7     72000    6       19300\n",
       "8     91000    8       12000\n",
       "9     67000    6       22000\n",
       "10    83000    7       18700\n",
       "11    79000    7       19500\n",
       "12    59000    5       26000\n",
       "13    58780    4       27500\n",
       "14    82450    7       19400\n",
       "15    25400    3       35000\n",
       "16    28000    2       35500\n",
       "17    69000    5       19700\n",
       "18    87600    8       12800\n",
       "19    52000    5       28200"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f530972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2020b28bf50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.Age,df.Sell_Price, color='red',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "20cebeb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X =df [['Mileage','Age']]\n",
    "y =df [['Sell_Price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c4c5f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8dc35d24",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3) # ,random_state=10) 20percent of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d059ff66",
   "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>Mileage</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>82450</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>69000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>28000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mileage  Age\n",
       "10    83000    7\n",
       "14    82450    7\n",
       "2     57000    5\n",
       "9     67000    6\n",
       "7     72000    6\n",
       "3     22500    2\n",
       "12    59000    5\n",
       "17    69000    5\n",
       "5     59000    5\n",
       "1     35000    3\n",
       "6     52000    5\n",
       "0     69000    6\n",
       "4     46000    4\n",
       "16    28000    2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "01ebfe7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "030906df",
   "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>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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cd4575c2",
   "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>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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" 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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4ad62f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36004.92390676],\n",
       "       [26794.80120892],\n",
       "       [13751.67555143],\n",
       "       [17512.85912602],\n",
       "       [27794.52942836],\n",
       "       [13141.21077851]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "51c79edd",
   "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>Sell_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sell_Price\n",
       "15       35000\n",
       "19       28200\n",
       "18       12800\n",
       "11       19500\n",
       "13       27500\n",
       "8        12000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "08db74c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9783928525470077"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b0f4cecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'def per(a,b):\\n    pee=(a*b)/100\\n    return pee\\n\\n#example \\na =int(input(\"Enter the number\"))\\nb= int(input(f\"Enter the persent you want for {a} :\"))\\nper(a,b)'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''def per(a,b):\n",
    "    pee=(a*b)/100\n",
    "    return pee\n",
    "\n",
    "#example \n",
    "a =int(input(\"Enter the number\"))\n",
    "b= int(input(f\"Enter the persent you want for {a} :\"))\n",
    "per(a,b)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "22eb22e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Example DataFrame\n",
    "data = {'A': [1, 2, np.nan, 4],\n",
    "        'B': [np.nan, 2, 3, 4],\n",
    "        'C': [1, 2, 3, np.nan]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "97f4c3ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a66b4e93",
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  NaN  1.0\n",
       "1  2.0  2.0  2.0\n",
       "2  NaN  3.0  3.0\n",
       "3  4.0  4.0  NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c1af740e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_A=df['A'].mean()\n",
    "mean_B=df['B'].mean()\n",
    "mean_C=df['C'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8badc59c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['A'].fillna(mean_A,inplace=True)\n",
    "df['B'].fillna(mean_B,inplace=True)\n",
    "df['C'].fillna(mean_C,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2592ebc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_null(a):\n",
    "    df['a'].fillna(df['a'].mean(), inplace=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "41766d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "da = {'A': [1, 2, np.nan, 4],\n",
    "        'B': [np.nan, 2, 3, 4],\n",
    "        'C': [1, 2, 3, np.nan]}\n",
    "\n",
    "df2 = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1809d9c3",
   "metadata": {},
   "outputs": [
    {
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