linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "693aa091",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data = pd.read_csv(\"Salary_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87a65fda",
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279f3ac3",
   "metadata": {},
   "source": [
    "# ml with ammar baba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4118603",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "337e829e",
   "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>Passengerid</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>zero</th>\n",
       "      <th>zero.1</th>\n",
       "      <th>zero.2</th>\n",
       "      <th>zero.3</th>\n",
       "      <th>zero.4</th>\n",
       "      <th>...</th>\n",
       "      <th>zero.12</th>\n",
       "      <th>zero.13</th>\n",
       "      <th>zero.14</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>zero.15</th>\n",
       "      <th>zero.16</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>zero.17</th>\n",
       "      <th>zero.18</th>\n",
       "      <th>2urvived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>39.0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>38.5</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Passengerid   Age      Fare  Sex  sibsp  zero  zero.1  zero.2  zero.3  \\\n",
       "0               1  22.0    7.2500    0      1     0       0       0       0   \n",
       "1               2  38.0   71.2833    1      1     0       0       0       0   \n",
       "2               3  26.0    7.9250    1      0     0       0       0       0   \n",
       "3               4  35.0   53.1000    1      1     0       0       0       0   \n",
       "4               5  35.0    8.0500    0      0     0       0       0       0   \n",
       "...           ...   ...       ...  ...    ...   ...     ...     ...     ...   \n",
       "1304         1305  28.0    8.0500    0      0     0       0       0       0   \n",
       "1305         1306  39.0  108.9000    1      0     0       0       0       0   \n",
       "1306         1307  38.5    7.2500    0      0     0       0       0       0   \n",
       "1307         1308  28.0    8.0500    0      0     0       0       0       0   \n",
       "1308         1309  28.0   22.3583    0      1     0       0       0       0   \n",
       "\n",
       "      zero.4  ...  zero.12  zero.13  zero.14  Pclass  zero.15  zero.16  \\\n",
       "0          0  ...        0        0        0       3        0        0   \n",
       "1          0  ...        0        0        0       1        0        0   \n",
       "2          0  ...        0        0        0       3        0        0   \n",
       "3          0  ...        0        0        0       1        0        0   \n",
       "4          0  ...        0        0        0       3        0        0   \n",
       "...      ...  ...      ...      ...      ...     ...      ...      ...   \n",
       "1304       0  ...        0        0        0       3        0        0   \n",
       "1305       0  ...        0        0        0       1        0        0   \n",
       "1306       0  ...        0        0        0       3        0        0   \n",
       "1307       0  ...        0        0        0       3        0        0   \n",
       "1308       0  ...        0        0        0       3        0        0   \n",
       "\n",
       "      Embarked  zero.17  zero.18  2urvived  \n",
       "0          2.0        0        0         0  \n",
       "1          0.0        0        0         1  \n",
       "2          2.0        0        0         1  \n",
       "3          2.0        0        0         1  \n",
       "4          2.0        0        0         0  \n",
       "...        ...      ...      ...       ...  \n",
       "1304       2.0        0        0         0  \n",
       "1305       0.0        0        0         0  \n",
       "1306       2.0        0        0         0  \n",
       "1307       2.0        0        0         0  \n",
       "1308       0.0        0        0         0  \n",
       "\n",
       "[1309 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LOADING THE DATASET\n",
    "tayyab = pd.read_csv(\"train_and_test2.csv\")\n",
    "tayyab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21a617e4",
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Passengerid</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>5</td>\n",
       "      <td>0</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>1304</th>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>39.0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>38.5</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>28.0</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age      Fare  Passengerid  Sex\n",
       "0     22.0    7.2500            1    0\n",
       "1     38.0   71.2833            2    1\n",
       "2     26.0    7.9250            3    1\n",
       "3     35.0   53.1000            4    1\n",
       "4     35.0    8.0500            5    0\n",
       "...    ...       ...          ...  ...\n",
       "1304  28.0    8.0500         1305    0\n",
       "1305  39.0  108.9000         1306    1\n",
       "1306  38.5    7.2500         1307    0\n",
       "1307  28.0    8.0500         1308    0\n",
       "1308  28.0   22.3583         1309    0\n",
       "\n",
       "[1309 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tayyab_selected_column = tayyab[[\"Age\", \"Fare\", \"Passengerid\", \"Sex\"]]\n",
    "tayyab_selected_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "61a5a37c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tayyab_selected_column.iloc[:, :-3].values #get a copy of dataset exclude last column\n",
    "y = tayyab_selected_column.iloc[:, [1]].values #get array of dataset in column 1st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "265c5b2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22. ],\n",
       "       [38. ],\n",
       "       [26. ],\n",
       "       ...,\n",
       "       [38.5],\n",
       "       [28. ],\n",
       "       [28. ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a814b44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.25  ],\n",
       "       [71.2833],\n",
       "       [ 7.925 ],\n",
       "       ...,\n",
       "       [ 7.25  ],\n",
       "       [ 8.05  ],\n",
       "       [22.3583]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77a21b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c939ea9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Simple Linear Regression to the Training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6d9ae42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting the testset results\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "36fa272d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[31.70018352],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [25.95300857],\n",
       "       [27.38980231],\n",
       "       [32.41858038],\n",
       "       [30.98178665],\n",
       "       [31.70018352],\n",
       "       [30.98178665],\n",
       "       [24.51621483],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [24.51621483],\n",
       "       [31.70018352],\n",
       "       [31.70018352],\n",
       "       [36.01056472],\n",
       "       [25.95300857],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [33.85537412],\n",
       "       [12.30346808],\n",
       "       [15.17705555],\n",
       "       [30.98178665],\n",
       "       [41.75773967],\n",
       "       [30.98178665],\n",
       "       [37.44735846],\n",
       "       [22.36102423],\n",
       "       [56.12567703],\n",
       "       [25.95300857],\n",
       "       [46.78651774],\n",
       "       [30.98178665],\n",
       "       [41.0393428 ],\n",
       "       [38.16575533],\n",
       "       [39.60254906],\n",
       "       [39.60254906],\n",
       "       [30.98178665],\n",
       "       [46.06812088],\n",
       "       [25.2346117 ],\n",
       "       [38.8841522 ],\n",
       "       [15.17705555],\n",
       "       [43.91293027],\n",
       "       [33.13697725],\n",
       "       [16.61384929],\n",
       "       [30.98178665],\n",
       "       [23.0794211 ],\n",
       "       [53.97048642],\n",
       "       [26.67140544],\n",
       "       [33.13697725],\n",
       "       [32.41858038],\n",
       "       [46.78651774],\n",
       "       [30.98178665],\n",
       "       [41.0393428 ],\n",
       "       [28.82659604],\n",
       "       [39.60254906],\n",
       "       [27.38980231],\n",
       "       [30.98178665],\n",
       "       [38.8841522 ],\n",
       "       [25.2346117 ],\n",
       "       [46.78651774],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [24.51621483],\n",
       "       [30.98178665],\n",
       "       [30.26338978],\n",
       "       [13.74026181],\n",
       "       [31.70018352],\n",
       "       [23.79781797],\n",
       "       [27.38980231],\n",
       "       [48.22331148],\n",
       "       [30.98178665],\n",
       "       [40.32094593],\n",
       "       [50.73770052],\n",
       "       [28.10819918],\n",
       "       [30.98178665],\n",
       "       [33.13697725],\n",
       "       [25.95300857],\n",
       "       [14.45865868],\n",
       "       [28.10819918],\n",
       "       [24.1570164 ],\n",
       "       [52.53369269],\n",
       "       [28.82659604],\n",
       "       [26.67140544],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [38.16575533],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [38.8841522 ],\n",
       "       [28.10819918],\n",
       "       [30.26338978],\n",
       "       [37.44735846],\n",
       "       [30.98178665],\n",
       "       [30.26338978],\n",
       "       [30.98178665],\n",
       "       [28.10819918],\n",
       "       [22.36102423],\n",
       "       [29.54499291],\n",
       "       [30.98178665],\n",
       "       [34.57377099],\n",
       "       [33.85537412],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [32.41858038],\n",
       "       [30.98178665],\n",
       "       [47.50491461],\n",
       "       [38.16575533],\n",
       "       [39.60254906],\n",
       "       [13.74026181],\n",
       "       [36.01056472],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [11.58507121],\n",
       "       [14.45865868],\n",
       "       [28.10819918],\n",
       "       [35.29216786],\n",
       "       [36.72896159],\n",
       "       [43.1945334 ],\n",
       "       [34.57377099],\n",
       "       [56.8440739 ],\n",
       "       [20.92423049],\n",
       "       [30.98178665],\n",
       "       [24.51621483],\n",
       "       [49.66010522],\n",
       "       [46.06812088],\n",
       "       [28.82659604],\n",
       "       [30.98178665],\n",
       "       [47.50491461],\n",
       "       [34.57377099],\n",
       "       [26.67140544],\n",
       "       [30.98178665],\n",
       "       [13.74026181],\n",
       "       [32.41858038],\n",
       "       [30.98178665],\n",
       "       [20.92423049],\n",
       "       [28.10819918],\n",
       "       [30.98178665],\n",
       "       [28.82659604],\n",
       "       [27.38980231],\n",
       "       [30.98178665],\n",
       "       [46.06812088],\n",
       "       [49.66010522],\n",
       "       [50.37850208],\n",
       "       [30.98178665],\n",
       "       [20.92423049],\n",
       "       [40.32094593],\n",
       "       [27.38980231],\n",
       "       [25.2346117 ],\n",
       "       [26.67140544],\n",
       "       [26.67140544],\n",
       "       [28.10819918],\n",
       "       [30.98178665],\n",
       "       [42.47613654],\n",
       "       [46.78651774],\n",
       "       [28.82659604],\n",
       "       [27.03060387],\n",
       "       [38.16575533],\n",
       "       [43.91293027],\n",
       "       [23.79781797],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [28.82659604],\n",
       "       [25.95300857],\n",
       "       [30.98178665],\n",
       "       [32.77777882],\n",
       "       [46.06812088],\n",
       "       [31.70018352],\n",
       "       [29.54499291],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [31.70018352],\n",
       "       [22.36102423],\n",
       "       [11.46294374],\n",
       "       [36.01056472],\n",
       "       [30.26338978],\n",
       "       [34.57377099],\n",
       "       [33.85537412],\n",
       "       [30.98178665],\n",
       "       [20.20583363],\n",
       "       [24.51621483],\n",
       "       [24.51621483],\n",
       "       [38.8841522 ],\n",
       "       [30.98178665],\n",
       "       [35.29216786],\n",
       "       [11.58507121],\n",
       "       [38.8841522 ],\n",
       "       [28.10819918],\n",
       "       [41.75773967],\n",
       "       [23.79781797],\n",
       "       [44.63132714],\n",
       "       [46.78651774],\n",
       "       [33.85537412],\n",
       "       [23.79781797],\n",
       "       [28.10819918],\n",
       "       [35.29216786],\n",
       "       [25.95300857],\n",
       "       [20.92423049],\n",
       "       [30.98178665],\n",
       "       [30.26338978],\n",
       "       [30.98178665],\n",
       "       [28.82659604],\n",
       "       [43.1945334 ],\n",
       "       [33.13697725],\n",
       "       [24.51621483],\n",
       "       [29.54499291],\n",
       "       [30.98178665],\n",
       "       [15.89545242],\n",
       "       [28.10819918],\n",
       "       [51.81529582],\n",
       "       [28.10819918],\n",
       "       [23.0794211 ],\n",
       "       [26.67140544],\n",
       "       [48.94170835],\n",
       "       [27.38980231],\n",
       "       [56.12567703],\n",
       "       [30.98178665],\n",
       "       [33.13697725],\n",
       "       [30.98178665],\n",
       "       [36.72896159],\n",
       "       [32.41858038],\n",
       "       [25.59381014],\n",
       "       [28.82659604],\n",
       "       [49.66010522],\n",
       "       [39.60254906],\n",
       "       [30.98178665],\n",
       "       [50.37850208],\n",
       "       [30.98178665],\n",
       "       [17.33224615],\n",
       "       [32.41858038],\n",
       "       [31.70018352],\n",
       "       [31.70018352],\n",
       "       [30.98178665],\n",
       "       [22.36102423],\n",
       "       [30.98178665],\n",
       "       [27.38980231],\n",
       "       [30.98178665],\n",
       "       [24.51621483],\n",
       "       [34.57377099],\n",
       "       [30.26338978],\n",
       "       [27.38980231],\n",
       "       [21.64262736],\n",
       "       [54.68888329],\n",
       "       [32.41858038],\n",
       "       [46.06812088],\n",
       "       [28.82659604],\n",
       "       [25.2346117 ],\n",
       "       [42.47613654],\n",
       "       [30.98178665],\n",
       "       [29.54499291],\n",
       "       [34.21457255],\n",
       "       [26.67140544],\n",
       "       [21.28342893],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [23.79781797],\n",
       "       [38.8841522 ],\n",
       "       [26.67140544],\n",
       "       [27.38980231],\n",
       "       [15.89545242],\n",
       "       [30.98178665],\n",
       "       [26.67140544],\n",
       "       [29.54499291],\n",
       "       [30.98178665],\n",
       "       [24.51621483],\n",
       "       [37.44735846],\n",
       "       [45.34972401],\n",
       "       [22.36102423],\n",
       "       [19.48743676],\n",
       "       [23.0794211 ],\n",
       "       [44.63132714],\n",
       "       [36.01056472],\n",
       "       [32.41858038],\n",
       "       [28.82659604],\n",
       "       [41.75773967],\n",
       "       [45.34972401],\n",
       "       [22.36102423],\n",
       "       [38.16575533],\n",
       "       [52.53369269],\n",
       "       [20.20583363],\n",
       "       [49.66010522],\n",
       "       [33.85537412],\n",
       "       [25.2346117 ],\n",
       "       [13.74026181],\n",
       "       [30.98178665],\n",
       "       [11.58507121],\n",
       "       [55.40728016],\n",
       "       [43.1945334 ],\n",
       "       [30.98178665],\n",
       "       [12.30346808],\n",
       "       [30.98178665],\n",
       "       [45.34972401],\n",
       "       [30.98178665],\n",
       "       [43.1945334 ],\n",
       "       [65.46483631],\n",
       "       [43.1945334 ],\n",
       "       [23.79781797],\n",
       "       [53.25208956],\n",
       "       [30.98178665],\n",
       "       [32.41858038],\n",
       "       [28.10819918],\n",
       "       [12.30346808],\n",
       "       [32.41858038],\n",
       "       [43.1945334 ],\n",
       "       [35.29216786],\n",
       "       [30.98178665],\n",
       "       [23.79781797],\n",
       "       [32.41858038],\n",
       "       [46.78651774],\n",
       "       [36.01056472],\n",
       "       [34.57377099],\n",
       "       [25.2346117 ],\n",
       "       [50.37850208],\n",
       "       [48.94170835],\n",
       "       [41.0393428 ],\n",
       "       [43.91293027],\n",
       "       [24.51621483],\n",
       "       [11.10374531],\n",
       "       [43.1945334 ],\n",
       "       [24.51621483],\n",
       "       [34.57377099],\n",
       "       [28.10819918],\n",
       "       [26.67140544],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [25.95300857],\n",
       "       [26.67140544],\n",
       "       [24.51621483],\n",
       "       [51.81529582],\n",
       "       [32.41858038],\n",
       "       [28.10819918],\n",
       "       [30.98178665],\n",
       "       [31.70018352],\n",
       "       [30.98178665],\n",
       "       [41.75773967],\n",
       "       [30.98178665],\n",
       "       [34.57377099],\n",
       "       [55.40728016],\n",
       "       [25.95300857],\n",
       "       [23.79781797],\n",
       "       [38.8841522 ],\n",
       "       [25.2346117 ],\n",
       "       [61.15445511],\n",
       "       [49.66010522],\n",
       "       [27.38980231],\n",
       "       [39.60254906],\n",
       "       [30.98178665],\n",
       "       [34.57377099],\n",
       "       [28.82659604],\n",
       "       [28.10819918],\n",
       "       [33.85537412],\n",
       "       [34.21457255],\n",
       "       [36.01056472],\n",
       "       [18.05064302],\n",
       "       [33.13697725],\n",
       "       [12.30346808],\n",
       "       [15.17705555],\n",
       "       [34.57377099],\n",
       "       [30.98178665],\n",
       "       [39.60254906],\n",
       "       [30.98178665],\n",
       "       [38.8841522 ],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [28.82659604],\n",
       "       [51.09689895],\n",
       "       [27.38980231],\n",
       "       [36.72896159],\n",
       "       [33.13697725],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [11.58507121],\n",
       "       [14.45865868],\n",
       "       [30.98178665],\n",
       "       [46.78651774],\n",
       "       [30.98178665],\n",
       "       [40.32094593],\n",
       "       [23.0794211 ],\n",
       "       [23.79781797],\n",
       "       [38.16575533],\n",
       "       [31.70018352],\n",
       "       [28.82659604],\n",
       "       [30.98178665],\n",
       "       [30.26338978],\n",
       "       [32.41858038],\n",
       "       [26.67140544],\n",
       "       [25.95300857],\n",
       "       [25.95300857],\n",
       "       [28.82659604],\n",
       "       [39.60254906],\n",
       "       [30.98178665],\n",
       "       [27.38980231],\n",
       "       [25.95300857],\n",
       "       [26.67140544],\n",
       "       [25.95300857],\n",
       "       [41.75773967],\n",
       "       [20.92423049],\n",
       "       [15.17705555],\n",
       "       [31.70018352],\n",
       "       [36.72896159],\n",
       "       [28.10819918],\n",
       "       [13.02186495],\n",
       "       [53.97048642],\n",
       "       [17.33224615],\n",
       "       [29.54499291],\n",
       "       [35.29216786],\n",
       "       [44.63132714],\n",
       "       [43.1945334 ],\n",
       "       [26.67140544],\n",
       "       [28.82659604],\n",
       "       [17.33224615],\n",
       "       [29.90419135],\n",
       "       [30.98178665],\n",
       "       [33.85537412],\n",
       "       [35.65136629],\n",
       "       [38.16575533],\n",
       "       [25.2346117 ],\n",
       "       [32.41858038],\n",
       "       [26.67140544],\n",
       "       [26.67140544],\n",
       "       [44.63132714],\n",
       "       [15.89545242],\n",
       "       [34.57377099],\n",
       "       [30.26338978],\n",
       "       [54.68888329],\n",
       "       [44.63132714],\n",
       "       [28.82659604],\n",
       "       [38.52495376],\n",
       "       [38.16575533],\n",
       "       [25.95300857],\n",
       "       [30.98178665],\n",
       "       [27.38980231],\n",
       "       [30.98178665],\n",
       "       [30.98178665],\n",
       "       [28.10819918],\n",
       "       [25.2346117 ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b09940d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "  # Visualizing the Training set results\n",
    "viz_train = plt\n",
    "viz_train.scatter(X_train, y_train, color='orange')\n",
    "viz_train.plot(X_train, regressor.predict(X_train), color='blue')\n",
    "viz_train.title('Age vs Fare (Training set)')\n",
    "viz_train.xlabel('Fare')\n",
    "viz_train.ylabel('Age')\n",
    "viz_train.show()\n",
    "\n",
    "# Visualizing the Test set results\n",
    "viz_test = plt\n",
    "viz_test.scatter(X_test, y_test, color='green')\n",
    "viz_test.plot(X_train, regressor.predict(X_train), color='blue')\n",
    "viz_test.title('Age vs Fare (Test set)')\n",
    "viz_test.xlabel('Age')\n",
    "viz_test.ylabel('Fare')\n",
    "viz_test.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3752fc76",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}