Anuj Pathak SalaryPrediction Project.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "90f93c8a",
   "metadata": {},
   "source": [
    "# Name:- Anuj Pathak\n",
    "\n",
    "Enrollment No:- EN20CS303015\n",
    "\n",
    "Project:- Salary Prediction Model\n",
    "\n",
    "Concept Used:- Supervised Machine Learning using LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0be0e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1799a29c",
   "metadata": {},
   "source": [
    "## Reading dataset from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2151160b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5c90d49",
   "metadata": {},
   "outputs": [
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       "  <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
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       "      <td>4.5</td>\n",
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       "      <td>4.9</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
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       "    <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",
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       "    <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",
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      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1805927",
   "metadata": {},
   "source": [
    "importing of Dataset successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab263cd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   YearsExperience  30 non-null     float64\n",
      " 1   Salary           30 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 608.0 bytes\n"
     ]
    }
   ],
   "source": [
    "#checking more information from Dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "13104137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience        5.9\n",
       "Salary             81363.0\n",
       "Name: 18, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accessing the elemensts from dataset using loc function\n",
    "data.loc[18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21555bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience         7.9\n",
       "Salary             101302.0\n",
       "Name: 22, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f2f511e6",
   "metadata": {},
   "outputs": [
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       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0\n",
       "2              1.5  37731.0\n",
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    "data.head()"
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       "    YearsExperience    Salary\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"
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "data.tail()"
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  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12d3e278",
   "metadata": {},
   "outputs": [
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       "(30, 2)"
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "data.shape"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a9ae07c",
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   "outputs": [
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       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>22</th>\n",
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       "      <th>23</th>\n",
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       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience  Salary\n",
       "0             False   False\n",
       "1             False   False\n",
       "2             False   False\n",
       "3             False   False\n",
       "4             False   False\n",
       "5             False   False\n",
       "6             False   False\n",
       "7             False   False\n",
       "8             False   False\n",
       "9             False   False\n",
       "10            False   False\n",
       "11            False   False\n",
       "12            False   False\n",
       "13            False   False\n",
       "14            False   False\n",
       "15            False   False\n",
       "16            False   False\n",
       "17            False   False\n",
       "18            False   False\n",
       "19            False   False\n",
       "20            False   False\n",
       "21            False   False\n",
       "22            False   False\n",
       "23            False   False\n",
       "24            False   False\n",
       "25            False   False\n",
       "26            False   False\n",
       "27            False   False\n",
       "28            False   False\n",
       "29            False   False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking Null values of Dataset\n",
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1217f753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    False\n",
       "Salary             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5a03f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46b50637",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "63c29aa9",
   "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"
    }
   ],
   "source": [
    "#visualizing of data by plotting the graph of dataset (yearsExperience vs Salary)\n",
    "data.plot(x = 'YearsExperience', y = 'Salary',style = 'o',markersize = 10)\n",
    "plt.title('plot of yearsExperience vs Salary')\n",
    "plt.xlabel('yearsExperience')\n",
    "plt.ylabel('Salary')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f964d55",
   "metadata": {},
   "source": [
    "## there's a linear relation between YearsExperience and Salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "822988e6",
   "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>YearsExperience</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.978242</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 YearsExperience    Salary\n",
       "YearsExperience         1.000000  0.978242\n",
       "Salary                  0.978242  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using .corr() function to check corelation between variables\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "95fe85d9",
   "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>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.313333</td>\n",
       "      <td>76003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>56720.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>65237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>122391.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.313333   76003.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.100000   37731.000000\n",
       "25%           3.200000   56720.750000\n",
       "50%           4.700000   65237.000000\n",
       "75%           7.700000  100544.750000\n",
       "max          10.500000  122391.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#disciption of statistical Parameters \n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b53a3b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "#conversion of datset elements into array using iloc function\n",
    "x = data.iloc[:, : -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "95fc76d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.1],\n",
       "       [ 1.3],\n",
       "       [ 1.5],\n",
       "       [ 2. ],\n",
       "       [ 2.2],\n",
       "       [ 2.9],\n",
       "       [ 3. ],\n",
       "       [ 3.2],\n",
       "       [ 3.2],\n",
       "       [ 3.7],\n",
       "       [ 3.9],\n",
       "       [ 4. ],\n",
       "       [ 4. ],\n",
       "       [ 4.1],\n",
       "       [ 4.5],\n",
       "       [ 4.9],\n",
       "       [ 5.1],\n",
       "       [ 5.3],\n",
       "       [ 5.9],\n",
       "       [ 6. ],\n",
       "       [ 6.8],\n",
       "       [ 7.1],\n",
       "       [ 7.9],\n",
       "       [ 8.2],\n",
       "       [ 8.7],\n",
       "       [ 9. ],\n",
       "       [ 9.5],\n",
       "       [ 9.6],\n",
       "       [10.3],\n",
       "       [10.5]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a82814c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2ce3bfd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 39343.,  46205.,  37731.,  43525.,  39891.,  56642.,  60150.,\n",
       "        54445.,  64445.,  57189.,  63218.,  55794.,  56957.,  57081.,\n",
       "        61111.,  67938.,  66029.,  83088.,  81363.,  93940.,  91738.,\n",
       "        98273., 101302., 113812., 109431., 105582., 116969., 112635.,\n",
       "       122391., 121872.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c749490b",
   "metadata": {},
   "source": [
    "## Splitting the dataset into testing and training of Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c39e062c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#training of Model\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2bdf4178",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "afff6689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train,y_train)\n",
    "# Training Complete"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c32195",
   "metadata": {},
   "source": [
    "## Plotting Regression Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3f35af25",
   "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"
    }
   ],
   "source": [
    "#plotting regression line of test data\n",
    "line = regressor.coef_*x+regressor.intercept_\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,line,color = 'black');\n",
    "plt.grid()                           # y = mx + c\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "417190ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.5]\n",
      " [10.3]\n",
      " [ 4.1]\n",
      " [ 3.9]\n",
      " [ 9.5]\n",
      " [ 8.7]]\n"
     ]
    }
   ],
   "source": [
    "#predicting the Value of test set results\n",
    "print(x_test)\n",
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0000d89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#comparison between Actual vs predicted salary\n",
    "data_frame = pd.DataFrame({\"Actual Salary\" : y_test, \"predicted Salary\" : y_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b069abd",
   "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>Actual Salary</th>\n",
       "      <th>predicted Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37731.0</td>\n",
       "      <td>40748.961841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122391.0</td>\n",
       "      <td>122699.622956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57081.0</td>\n",
       "      <td>64961.657170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63218.0</td>\n",
       "      <td>63099.142145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116969.0</td>\n",
       "      <td>115249.562855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>109431.0</td>\n",
       "      <td>107799.502753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual Salary  predicted Salary\n",
       "0        37731.0      40748.961841\n",
       "1       122391.0     122699.622956\n",
       "2        57081.0      64961.657170\n",
       "3        63218.0      63099.142145\n",
       "4       116969.0     115249.562855\n",
       "5       109431.0     107799.502753"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a8d8ea",
   "metadata": {},
   "source": [
    "# Testing with our own data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "884bffb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Years of experience of employee is: [[9.0]]\n",
      "Regressor model predicted Salary of employee is: [110593.27529119]\n"
     ]
    }
   ],
   "source": [
    "yearofExperience = [[9.0]]\n",
    "my_prediction = regressor.predict(yearofExperience)\n",
    "print(\"Years of experience of employee is:\",yearofExperience)\n",
    "print(\"Regressor model predicted Salary of employee is:\",my_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f3583295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Years of experience of employee is: [[4.0]]\n",
      "regressor model predicted Salary of employee is: [64030.39965754]\n"
     ]
    }
   ],
   "source": [
    "yearofExperience = [[4.0]]\n",
    "my_prediction = regressor.predict(yearofExperience)\n",
    "print(\"Years of experience of employee is:\",yearofExperience)\n",
    "print(\"regressor model predicted Salary of employee is:\",my_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfc693e4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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