Rishita Parnami.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salary Prediction Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rishita Parnami"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concept- Supervised Machine Learning and Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is imported\n"
     ]
    },
    {
     "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",
       "  </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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "url=\"https://raw.githubusercontent.com/CSCult/salary-prediction/main/Salary_Data.csv\"\n",
    "r_data=pd.read_csv(url)\n",
    "print(\"Data is imported\")\n",
    "r_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking statistical information about dataset\n",
    "r_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing dataset\n",
    "r_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking null values\n",
    "r_data.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 the distribution of salary\n",
    "r_data.plot(x='YearsExperience', y='Salary', style='o')\n",
    "plt.title('YearsExperience Vs Salary')\n",
    "plt.xlabel('YearsExperience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the data\n",
    "X=r_data.iloc[:,:1].values\n",
    "y=r_data.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing the data into Train and test\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training is complete.\n"
     ]
    }
   ],
   "source": [
    "#implementing the linear regression algorithms\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "print(\"Training is complete.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "line = regressor.coef_*X+regressor.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD4CAYAAADy46FuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAo4UlEQVR4nO3deXgV9dn/8fdNEkjCFvYlgAHZNwUigtSlaoW6gSCVLkKrLdbHX7X1KZvaR6q1oliXLmrd0VqVAiJaEVGquFAxiCYQCETWhC3sWxKyfH9/ZKIhnCwk52ROks/runJx8j0zc+7kIudzZr4z95hzDhERkbI08LsAEREJbwoKEREpl4JCRETKpaAQEZFyKShERKRckX4XEGytW7d2CQkJfpchIlKrrFq1aq9zrk2g5+pcUCQkJJCUlOR3GSIitYqZbS3rOR16EhGRcikoRESkXAoKEREpl4JCRETKpaAQEZFyKShERKRcCgoRESmXgkJEpA5Ymrqb1z7fFpJt17kL7kRE6pO9R3OZuWgtbyXvZHCXOMYP6UyDBhbU11BQiIjUQs45Fn6Zye/fTOV4bgG/vawnN114ZtBDAhQUIiK1TubBbO58PYUP0rIY3CWOB68dSPe2TUP2egoKEZFaorDQ8fLKbcx6ex2FDu6+qi8ThycQEYK9iJIUFCIitcCmrKNMn5/Cyi37Ob9Ha/54zQA6t4ytkddWUIiIhLH8gkKe+XgzjyzdQKPIBjw4biDjEzthFtq9iJIUFCIiYSp1x2Gmzv+KNZmHGdmvHeed2ZrH3t/ItPnJdIyLYcrIXowZFB/yOhQUIiJhJievgL8uS+fJD78mLrYhT/x4MLn5hcxYkEJ2XgFQNKE9Y0HKN+vMXpLGjoPZIQkQBYWISBhZtXU/U+cl83XWMcYN7sTvruxDXGxDRsxa9k1IFMvOK2DmorXk5hcGDJBghYWCQkQkDBzLzWf2kjTmrNhCx+YxzLlhKBf2/PbOpDsOZgdc72B23ilj2XkFzF6SpqAQEakrPtqYxYwFKWQezGbisDOYMqo3TRqd/PbcMS6GzDLCIpCygqUqKuz1ZGbPmdkeM1tTYmy2ma03s2Qze93M4ko8N8PM0s0szcxGlhgfYmYp3nN/Nm/K3swamdlr3vhnZpZQYp1JZrbR+5oUrB9aRCQcHDqex5R/fcX1z66kYWQD5t40nN+P7n9KSABMGdmLmKiIk8ZioiJoERsVcNsd42KCVmdlmgK+AIwqNbYU6O+cGwhsAGYAmFlfYALQz1vncTMr/smeACYDPbyv4m3eCBxwznUHHgEe8LbVErgbOBcYCtxtZi1O/0cUEQk/76zZyaWPfMiC1Zn8z0Vn8vat53NOQssylx8zKJ77xw4gPi4GA+LjYrh/7ADuvqpfwACZMrJX0Gqt8NCTc255yU/53ti7Jb79L3Ct93g08KpzLhfYbGbpwFAz2wI0c86tADCzF4ExwGJvnZne+vOAv3p7GyOBpc65/d46SykKl1dO+6cUEQkTe47kcPcba1m8Zhd9OzTj+Z+eQ//45pVad8yg+DLnHcL9rKcbgNe8x/EUBUexDG8sz3tcerx4ne0Azrl8MzsEtCo5HmCdk5jZZIr2VujSpUs1fhQRkdBwzjH/i0zufSuV7LwCpo7qxS/O70ZURPXv9lBegARDtYLCzO4E8oGXi4cCLObKGa/qOicPOvcU8BRAYmJiwGVERPySceA4d7y+huUbsjgnoQWzxg3kzDZN/C6r0qocFN7k8pXAJc654jfnDKBzicU6ATu88U4Bxkuuk2FmkUBzYL83flGpdT6oar0iIjWtsNDx0n+38sA76zHgntH9+Mm5Z4SkFXgoVWmfx8xGAdOAq51zx0s8tQiY4J3J1JWiSeuVzrmdwBEzG+bNP0wE3iixTvEZTdcCy7zgWQJcZmYtvEnsy7wxEZGwl77nKD/4+wruXrSWxISWLPnNBUwcnlDrQgIqsUdhZq9Q9Mm+tZllUHQm0gygEbDUO8v1v865Xzrn1prZXCCVokNStzjnii8lvJmiM6hiKJrEXuyNPwu85E1876forCmcc/vN7F7gc2+5e4ontkVEwsnC1ZnfTCZ3aB7NoC4tWJq6m5iGEfxp/FmMHRxfo038gs2+PWpUNyQmJrqkpCS/yxCRemLh6syTejAVO6tTc56ZdA5tmjbyqbLTY2arnHOJgZ6r/nS7iEg9NntJ2ikhAbD36IlaExIVUVCIiFRDWW01gtlCw2/q9SQiUgVHc/OZ/c76Mp8PZgsNvykoRERO0wdpe7jz9TXsOJTNBT1as3LzfnLyC795PtgtNPymoBCRsFXybKKavKNbWQ4cO8G9/05lwReZdG/bhHm/PI8hZ7QIuzqDTUEhImGp9NlEobghT2U551i8Zhf/98YaDh7P41cXd+f/XdydRpER39RTl4KhNAWFiISlQGcTBfuGPJWx53AOv3tjDUvW7mZAfHNevOFc+nZsVmOvHw4UFCISlso6a6imziZyzvGvpAzu/XcqJ/ILmf793vz8O12JrKCJX108DKWgEJGwVNYd3WribKLt+48zY0EKH6fvZWhCS2aNG0C3SjTxC6fDZcGk6yhEJCyVdUe3UJ5NVFDoeO7jzVz2yHJWbzvAvWP68+rkYZUKCSj/cFltpj0KEQlLxZ/Aa+owzsbdR5g6P5nV2w5yUa823HdN0d3kToffh8tCRUEhImGrJs4mOpFfyN8//Jq/LEuncaMIHr3ubEaf3bFKTfz8PFwWSgoKEam3kjMOMnVeMut3HeGKgR34/dX9aN2k6v2ZpozsdUqDwLpw8Z2CQkTqnZy8Ah5ZuoGnP9pE6yaNeOr6IVzWr321t1vTh8tqioJCROqsQKeqtm8ezfT5yWzZd5wJ53RmxuV9aB4TFbTXrIsX3ykoRKROCnSq6v/+6ysKCh1dWsby8s/PZUT31j5XWTsoKESkTgp0qmpBoaNJo0je+fX5xDbU219l6ToKEamTyjol9VhuvkLiNCkoRKTOcc4RFxt43qG2n6rqBwWFiNQpuw/n8IsXV3HgeB6lL4WoC6eq+kH7XyJSJzjneO3z7dz39jryCgq58/I+tGrckD8t3VCnTlX1g4JCRGq9rfuOMX1+Cis27WNYt5bMGjuQhNaNARg7pJPP1dV+CgoRqbUKCh3Pf7KZh95NI6pBA/54zQAmnNOZBg0q336jLrYFDzYFhYjUSmm7ipr4fbX9IJf0bssfrulPh+anN1FdV9uCB5uCQkRqlRP5hTz+QTp/+086TaOj+PMPB3HVwA5VauIXLnfRC3cKChGpNb7cfpBp85JJ232E0Wd35O6r+tGyccMqb6+utgUPNgWFiIS97BMFPLw0jWc/3kzbptE8OymRS/q0q/Z262pb8GDTdRQiEtY+/XovIx9dztMfbWbC0C68e/sFQQkJ8OcuerWR9ihEJCwdzsnj/rfX88rKbSS0iuWVXwxj+JmtgvoadbUteLApKEQk7LyXups7F6aQdSSXyRd04zeX9iSmYUTFK1ZBXWwLHmwKChEJG/uO5jLzzVTe/GoHvds35emJiQzsFOd3WfWegkJEalzpi9x+e1lPGjQwZi5ay9HcfG7/Xk9+eeGZNIzUNGo4UFCISI0q64ZChQ7O7hzHg9cOpGe7pj5XKSUpKESkRgW6yK3QQfPoKObffB4Rp9F+Q2pGhft1Zvacme0xszUlxlqa2VIz2+j926LEczPMLN3M0sxsZInxIWaW4j33Z/MuozSzRmb2mjf+mZkllFhnkvcaG81sUtB+ahHxTVkXsx3OyVNIhKnKHAB8ARhVamw68L5zrgfwvvc9ZtYXmAD089Z53MyKT1V4ApgM9PC+ird5I3DAOdcdeAR4wNtWS+Bu4FxgKHB3yUASkdonv6CQptGBD2ToIrfwVWFQOOeWA/tLDY8G5niP5wBjSoy/6pzLdc5tBtKBoWbWAWjmnFvhnHPAi6XWKd7WPOASb29jJLDUObffOXcAWMqpgSUitcS6nYcZ+8SnHM7Jp/SOgy5yC29VnaNo55zbCeCc22lmbb3xeOC/JZbL8MbyvMelx4vX2e5tK9/MDgGtSo4HWOckZjaZor0VunTpUsUfSURCITe/gL8tS+fxD74mLjaKv/1oMCfyC3joXd1QqLYI9mR2oAOMrpzxqq5z8qBzTwFPASQmJgZcRkQqFux7M3yx7QDT5iWzcc9Rxg6K53dX9qWF18TvmsG6oVBtUdWg2G1mHby9iQ7AHm88A+hcYrlOwA5vvFOA8ZLrZJhZJNCcokNdGcBFpdb5oIr1ikgFgnlvhuMn8nloyQae/3QzHZpF8/zPzuG7vdpWvKKEpapezbIIKD4LaRLwRonxCd6ZTF0pmrRe6R2mOmJmw7z5h4ml1ine1rXAMm8eYwlwmZm18CaxL/PGRCQEyrs3w+n4eGNRE7/nPtnM9cPO4N3bL1RI1HIV7lGY2SsUfbJvbWYZFJ2JNAuYa2Y3AtuA8QDOubVmNhdIBfKBW5xzxf/zbqboDKoYYLH3BfAs8JKZpVO0JzHB29Z+M7sX+Nxb7h7nXOlJdREJkurem+FQdh73/TuVuUkZdG3dmLk3DWdo15bBLFF8UmFQOOd+WMZTl5Sx/H3AfQHGk4D+AcZz8IImwHPPAc9VVKOIVF917s2wZO0ufrdwDfuOneDmi87ktkt6EB0VmiZ+UvPUSEVEgKrdmyHrSC63vPwFN720ilZNGrHwf0YwbVRvhUQdoxYeIgKc3r0ZnHO8vjqTe95K5XhuAVNG9mLyBd2IitBnz7pIQSEi36jMvRkyD2Zz5+spfJCWxeAuRU38urdVE7+6TEEhIpVSWOh4+bOtzFq8HgfMvKov1w9PUH+mekBBIeKjYF/gFiqbso4yfX4KK7fs5/werfnjNQPo3DLW77KkhigoRHwSzAvcQiW/oJCnP9rMI+9tIDqyAbOvHci1QzrhNX+WekJBIeKT8i5wC4egWLvjENPmJ7Mm8zCj+rXnnjH9aNs02u+yxAcKChGfVPcCt1DJySvgL8s28uSHm2gR25AnfjyY7w/o4GtN4i8FhYhPqnOBW6is2rqfqfOS+TrrGOMGd+J3V/YhLrahb/VIeFBQiPhkysheJ81RQOjuy1DRpPmx3HxmL0ljzootdGwew5wbhnJhzzZBr0NqJwWFiE9O5wK36qho0nz5hixmLEhhx6FsJg1P4Lcje9Gkkd4a5FtW1Ki17khMTHRJSUl+lyESNkbMWhbwEFeHZtGM6NGaeasy6NamMQ+OG0higpr41Vdmtso5lxjoOX1sEKnjypoc33k4h9dXZ3LLd8/kVxeriZ+UTUEhUseVNWkeFWEsvGUE/To296EqqU3UwUukjpsyshfRkSf/qUc2MGaNHaiQkEpRUIjUcUPOaMEZrRt/833bpo14aPxZjBuie1ZL5ejQk0gdVVjoeHHFFh5ckoYB947ux4/PPYMGauInp0lBIVIHpe85wrT5KazaeoALe7bhvmv606mFmvhJ1SgoROqQvIJCnlq+icfe20hsowge/sFZXDMoXk38pFoUFCJ1xJrMQ0ydl0zqzsNcMaADM6/uR5umjfwuS+oABYVILZeTV8Bj72/kqeWbaNm4IU/+ZAij+rf3uyypQxQUIrXY51v2M21eMpv2HuO6xM7ccXkfmsdG+V2W1DEKCpFa6GhuPg++s54XV2ylU4sY/nHjuXynR2u/y5I6SkEhUsv8J20Pdy5IYefhHG4Y0ZXfjuxJbEP9KUvo6H+XSC1x4NgJ7n0rlQWrM+netgnzfnkeQ85o4XdZUg8oKETCnHOOt1N2cfeiNRw8nsetF3fnlou70yhSTfykZigoRMLYnsM53LVwDe+m7mZAfHNevOFc+nZs5ndZUs8oKEQqoaI7xAWbc45/JWVw779TOZFfyIzv9+bG73QlMkLt2aTmKShEKlDRHeKCbfv+48xYkMLH6XsZ2rUls8YOoFubJkF/HZHKUlCIVGD2krST7msNkJ1XwOwlaUENioJCx5xPtzB7SRoRDYw/jOnPj4Z2URM/8Z2CQqQCZd0hrqzxqti4+whT5yezettBvturDfddM4COcTFB275IdSgoRCpQ1h3iqvpGXnK+o0PzaM7uHMd76/bQuFEEj153NqPP7qgmfhJWNDMmUoEpI3sRU+p+0jFREUwZ2eu0t1U835F5MBsH7DiUw9trdtGvYzOW3n4hY9TpVcKQ9ihEKlA8DxGMs54CzXcA7DmSS+sm6vQq4alaQWFmvwF+DjggBfgZEAu8BiQAW4AfOOcOeMvPAG4ECoBbnXNLvPEhwAtADPA2cJtzzplZI+BFYAiwD7jOObelOjWLVMWYQfFBmbgOdAgLgjvfIRJsVT70ZGbxwK1AonOuPxABTACmA+8753oA73vfY2Z9vef7AaOAx82seH/+CWAy0MP7GuWN3wgccM51Bx4BHqhqvSJ+OpKTx52vp5T5vCauJZxVd44iEogxs0iK9iR2AKOBOd7zc4Ax3uPRwKvOuVzn3GYgHRhqZh2AZs65Fc45R9EeRMl1irc1D7jEdABXapll63dz2SPLeWXlNi7q2YboyJP/7Ko63yFSU6ocFM65TOAhYBuwEzjknHsXaOec2+ktsxNo660SD2wvsYkMbyzee1x6/KR1nHP5wCGgVelazGyymSWZWVJWVlZVfySRoNp3NJfbXl3NDS8k0TQ6kvk3n8cLNwxl1riBxMfFYEB8XAz3jx0Q0qu8RaqrynMUZtaCok/8XYGDwL/M7CflrRJgzJUzXt46Jw849xTwFEBiYuIpz4vUJOccbybvZOaitRzJyePXl/bgfy7qTkNvTyJY8x0iNaU6k9mXApudc1kAZrYAOA/YbWYdnHM7vcNKe7zlM4DOJdbvRNGhqgzvcenxkutkeIe3mgP7q1GzSEjtOpTDXQtTeG/dHs7qHMeD4wbSq31Tv8sSqZbqzFFsA4aZWaw3b3AJsA5YBEzylpkEvOE9XgRMMLNGZtaVoknrld7hqSNmNszbzsRS6xRv61pgmTePIRJWnHO8snIb33v4Qz5O38tdV/Rhwc3nKSSkTqjyHoVz7jMzmwd8AeQDqyk6/NMEmGtmN1IUJuO95dea2Vwg1Vv+Fudc8QnlN/Pt6bGLvS+AZ4GXzCydoj2JCVWtVyRUtu47xvT5KazYtI/h3Voxa9wAzmjV2O+yRILG6toH9MTERJeUlOR3GVIPFBQ6nvt4M39amkZUgwbccUUfJpzTWVdWS61kZqucc4mBntOV2SJVkLbrCFPnfcVXGYe4tE9b/jBmAO2bR/tdlkhIKChETsOJ/EL+9p90Hv8gnabRUfz5h4O4amAH7UVInaagEKmkL7cfZOq8r9iw+yhjzu7I/13Vj5aNG/pdlkjIKShEKpB9ooA/vZvGc59spl2zaJ77aSIX927nd1kiNUZBIVKOT9P3Mn1BCtv2H+fH53Zh2vd70yw6yu+yRGqUgkIkgEPZedz/9jpe/Xw7Ca1ieXXyMIZ1O6V7jEi9oKAQKWVp6m7uWphC1pFcbrqgG7++tCcxDSMqXlGkjlJQiHj2Hs1l5qK1vJW8k97tm/L0xEQGdorzuywR3ykopN5zzvHGlzv4/ZtrOZqbz+3f68kvLzzzmyZ+IvWdgkLqtR0Hs7lr4RqWrd/D2Z3jePDagfRsp/5MIiUpKKReKix0/HPlNmYtXk9BoeN3V/blp+clENFAF86JlKagkHpn895jTJufzMrN+xnRvRX3XzOQLq1i/S5LJGwpKKTeyC8o5JmPN/PI0g00jGzAg+MGMj6xk9pviFRAQSH1QuqOw0ybn0xK5iG+17cdfxjTn3bN1MRPpDIUFFKn5eYX8Ndl6TzxwdfExUbxtx8N5vIB7bUXIXIaFBRSZ63aeoBp85NJ33OUsYPj+d0VfWmhJn4ip01BIXXO8RP5zF6SxgufbqFDs2ie/9k5fLdXW7/LEqm1FBRSp3y8cS/TFySTcSCbicPPYOqo3jRpdPJ/84WrM5m9JI0dB7PpGBfDlJG9GDMo3qeKRcKfgkLqhEPH87jv7VTmJmXQrXVj5t40nKFdW56y3MLVmcxYkEJ2XtHt2jMPZjNjQQqAwkKkDAoKqfWWrN3FXQvXsP/YCW6+6Exuu6QH0VGBm/jNXpL2TUgUy84rYPaSNAWFSBkUFFJrZR0pauL375Sd9O3QjOd/eg7945uXu86Og9mnNS4iCgqphZxzLPgik3veSiX7RAFTRvZi8gXdiIqouIlfx7gYMgOEQse4GM1diJRBQSG1SubBbO5YkMKHG7IYckYLHhg3kO5tm1R6/Skje500RwEQExXBd3u30dyFSBkUFFIrFBY6/vHZVh5YvB4HzLyqLxOHJ9DgNJv4Fb/pl95z0NyFSNkUFBJQOB2G+TrrKNPnJ/P5lgOc36M1f7xmAJ1bVr2J35hB8af8LL957cuAy2ruQkRBIQGEyymkeQWFPP3RJh59byMxURE8NP4sxg2OD0n7jfLmLkTqO93CS05R3mGYmrJ2xyHG/O0THnwnjUt6t2Xp7Rdw7ZDQdXqdMrIXMaVOqY2JimDKyF4heT2R2kR7FHKKUJ5CWtEhrZy8Av6ybCNPfriJFrENeeLHg/n+gA7Vft2KlDV3ofkJEQWFBBCqwzAVHdJK2rKfqfOT2ZR1jGuHdOKuK/oQF1tzTfwCzV2IiIJCAijrFNLqHoYp65DWA++s58vtB5mzYgsdm8fw4g1DuaBnm2q9logEj4JCThGqwzBlHbraeSiHOSu2MGl4AlNG9qJxI/23FAkn+ouUgEJxGKasQ1qRDYxXJw8jMeHUJn4i4j+d9SQ1JtCZRZENjPvHDqhySCxcncmIWcvoOv3fjJi1jIWrM4NRqoiUoKCQGnNe91b0bPdtu402TRrx0PizGJ/YuUrbK54czzyYjePbyXGFhUhwVevQk5nFAc8A/QEH3ACkAa8BCcAW4AfOuQPe8jOAG4EC4Fbn3BJvfAjwAhADvA3c5pxzZtYIeBEYAuwDrnPObalOzfKtmrr62jnHvFUZ3PtWKjn5hUwb1ZtfnN+VyEo08SuP2m6I1Izq7lE8BrzjnOsNnAWsA6YD7zvnegDve99jZn2BCUA/YBTwuJkVH4d4ApgM9PC+RnnjNwIHnHPdgUeAB6pZr3hq6tP49v3HmfjcSqbMS6ZX+6Ysvu18br7ozGqHBKhluEhNqfJfq5k1Ay4AngVwzp1wzh0ERgNzvMXmAGO8x6OBV51zuc65zUA6MNTMOgDNnHMrnHOOoj2IkusUb2secImF6tLceibUV18XFjpe+GQzIx9dzhdbD3DP6H68Nnk4Z7apfKfXipR1XYfabogEV3U+1nUDsoDnzWy1mT1jZo2Bds65nQDev8V3tY8HtpdYP8Mbi/celx4/aR3nXD5wCGhVuhAzm2xmSWaWlJWVVY0fqf4I5afx9D1HGP/3Fcx8M5VzElqy5DcXVKnTa0XUdkOkZlRnjiISGAz8yjn3mZk9hneYqQyB3iVcOePlrXPygHNPAU8BJCYmnvK8nCoUV1/nFRTy1PJNPPbeRmIbRfDwD87imkGhaeIHarshUlOqExQZQIZz7jPv+3kUBcVuM+vgnNvpHVbaU2L5kqe3dAJ2eOOdAoyXXCfDzCKB5sD+atQsnmBffb0m8xBT5iWzbudhrhjYgZlX9aNN00bBKrdMarshEnpVDgrn3C4z225mvZxzacAlQKr3NQmY5f37hrfKIuCfZvYw0JGiSeuVzrkCMztiZsOAz4CJwF9KrDMJWAFcCyzz5jGkmoL1aTwnr4BH39vI0x9tomXjhvz9+iGM7Ne+SmdUhdM9METkW9W9MvtXwMtm1hDYBPyMonmPuWZ2I7ANGA/gnFtrZnMpCpJ84BbnXPHH2Zv59vTYxd4XFE2Uv2Rm6RTtSUyoZr31XjDfjFdu3s/0+cls2nuM6xI7c8flfWgeG3Va97MorifzYDbGt8cVdStSkfBhde0DemJioktKSvK7jLBU+g0cig433T92wGm9GR/JyePBd9J46b9b6dQihlljB/KdHq2/eX7ErGUB5z/i42L4ZPrF5dZT0ToiEhpmtso5lxjoOfV6qkeCcYHaf9L2cOeCFHYezuGGEV357ciexDY8+b9RZc+oClRPReuISM1TUNQj1Tkldv+xE9z7Viqvr86kR9smzL/5PAZ3aRFw2cqeURVomYrWEZGap15P9UhVLlBzzvFW8g6+9/CHvPnVDm69uDtv3fqdMkMCKn99Q0QFp83qmgiR8KA9inrkdE6JXbg6k1mL17PrcA4AnVvE8I+fn0ufDs0qfJ3KnlFVUM78WLzOehIJGwqKeqSyb+Cvf5HB1PnJ5BV8+0aedSSXtF1HKhUUxa9V0Zt8fBmHqDSBLRJeFBT1TEVv4Nv2HWf6gpSTQgIgJ78w6F1ZQ3XLVREJLgWFAFBQ6Hjh0y08tCSN3PzCgMsE+wwkteAQqR0UFMLG3UeYOj+Z1dsOcnHvtqTuOPzN3ERJoTgDSS04RMKfgqIeO5FfyJMffs1flm2kSaNIHptwNlef1ZE3vtyhQ0Ii8g0FRT311faDTJufzPpdR7jqrI7MvKovrZoUNfHTISERKUlBUc9knyjgkfc28MxHm2jTtBFPT0zke33bnbKcDgmJSDEFRT2y4ut9zFiQzJZ9x/nh0M7MuLwPzaKj/C5LRMKcgqIeOJyTx6zF6/nnZ9s4o1Us//zFuZx3ZuuKVxQRQUFR5y1bv5s7Fqxhz5EcfnF+V27/Xi9iGkZUvKKIiEdBUUftO5rLPW+l8saXO+jVrilPXj+EszvH+V2WiNRCCoo6xjnHm8k7mbloLUdy8vj1pT34n4u60zBS/R9FpGoUFHXIrkM53LUwhffW7eGsznE8OG4gvdo39bssEanlFBR1gHOOVz/fzh//vY68wkLuuqIPPxvRlYgG5bfxFhGpDAVFLbdl7zFmLEhhxaZ9DO/WilnjBnBGq8Z+lyUidYiCopYqKHQ89/Fm/rQ0jagGDZg1dgDXndMZq+BmQCIip0tBUQul7TrC1Hlf8VXGIS7t05Y/jBlA++bRfpclInWUgqICC1dnhk3PoxP5hfztP+k8/kE6zaKj+MsPB3HlwA7aixCRkFJQlGPh6syTuqhmHsxmxoIUgBoPiy+3H2TqvK/YsPsoY87uyP9d1Y+WjRvWaA0iUj8pKMoxe0naSa22AbLzCoJ+p7fyZJ8o4E/vpvHcJ5tp1yya536ayMW9T23iJyISKgqKcpR1R7dg3+mtLJ9+vZfp81PYtv84PxnWhWmjetNUTfxEpIYpKMrRMS6GzAChEIo7vZV0KDuPWYvX8crK7SS0iuXVycMY1q1VSF9TRKQsCopyTBnZK6R3egs0Ud64USR3LUwh60guN13Yjd9c2pPoKDXxExH/KCjKEco7vQWaKP/fuV9R4By92zfl6YmJDOwUV+3XERGpLgVFBUJ1p7dAE+UFztE0OpJF/+87auInImFD70Y+KWtC/GhOvkJCRMKK3pF8UFjoaBYT+OylUE+Ui4icLgVFDdu89xgTnv4vh7LzKN3cNZgT5SIiwaI5ihqSX1DIMx9v5pGlG2gY2YAHxw0kKsJ46N0NYdEeRESkLAqKGpC64zDT5ieTknmIkf3ace/o/rRtVtTE75rBnXyuTkSkfNUOCjOLAJKATOfclWbWEngNSAC2AD9wzh3wlp0B3AgUALc655Z440OAF4AY4G3gNuecM7NGwIvAEGAfcJ1zbkt1a64pufkF/HVZOk988DVxsVE8/uPBfL9/+wqb+IVTI0IRkWDsUdwGrAOaed9PB953zs0ys+ne99PMrC8wAegHdATeM7OezrkC4AlgMvBfioJiFLCYolA54JzrbmYTgAeA64JQ8ymC/ea8ausBps1PJn3PUcYOjud3V/SlRSWa+IVTI0IREajmZLaZdQKuAJ4pMTwamOM9ngOMKTH+qnMu1zm3GUgHhppZB6CZc26Fc85RtAcxJsC25gGXWAh6ahe/OWcezMbx7ZvzwtWZp72tY7n5/P7NtVz75KdknyjghZ+dw8M/OLtSIQHlNyIUEfFDdfcoHgWmAk1LjLVzzu0EcM7tNLO23ng8RXsMxTK8sTzvcenx4nW2e9vKN7NDQCtgb8kizGwyRXskdOnS5bR/iGB1if1oYxYzFqSQcSCbicPPYOqo3jRpdHq/Yr8bEYqIlFblPQozuxLY45xbVdlVAoy5csbLW+fkAeeecs4lOucS27RpU8lyvlXdN+dDx/OYOu8rrn92JQ0jGjD3puHcM7r/aYcElH0dha6vEBG/VGePYgRwtZldDkQDzczsH8BuM+vg7U10APZ4y2cAnUus3wnY4Y13CjBecp0MM4sEmgP7q1FzQFXtErtwdSb3vJnK/uMnALi0T1v++qPB1WriF+pGhCIip6vKexTOuRnOuU7OuQSKJqmXOed+AiwCJnmLTQLe8B4vAiaYWSMz6wr0AFZ6h6mOmNkwb/5hYql1ird1rfcap+xRVNeUkb2IKfXmXtGb84ufbuH2uV9+ExIAn6Tv4501u6pVy5hB8dw/dgDxcTEYEB8Xw/1jB2giW0R8E4rrKGYBc83sRmAbMB7AObfWzOYCqUA+cIt3xhPAzXx7euxi7wvgWeAlM0unaE9iQgjqPa0usc45FnyRyd1vrqV0ZAXr7nehakQoIlIVFoIP6L5KTEx0SUlJIdl2xoHj3PH6GpZvyCpzGQM2z7oiJK8vIhIqZrbKOZcY6Dn1eqqEwkLHiyu2MPKR5SRt2c/vr+5Hx+bRAZfVpLOI1DVq4VGBr7OOMm1eMklbD3B+j9b88ZoBdG4ZS/OYKE06i0i9oKAoQ15BIU8t38Rj728kJiqCh8afxbjB8d+03wjl3e9ERMKJgiKANZmHmDY/mbU7DnP5gPbMvLofbZueeqhJk84iUh8oKErIySvgz+9v5O/LN9EitiFP/mQwo/p38LssERFfKSg82/cfZ9LzK9mUdYzxQzpx1xV9aR4b+C50IiL1iYLC065ZNAmtGjPzqn5c0PP024CIiNRVCgpPw8gGPPfTc/wuQ0Qk7Og6ChERKZeCQkREyqWgEBGRcikoRESkXAoKEREpl4JCRETKpaAQEZFyKShERKRcde7GRWaWBWz1u47T1BrY63cRPqvvv4P6/vODfgfg7+/gDOdcwLYUdS4oaiMzSyrrzlL1RX3/HdT3nx/0O4Dw/R3o0JOIiJRLQSEiIuVSUISHp/wuIAzU999Bff/5Qb8DCNPfgeYoRESkXNqjEBGRcikoRESkXAoKn5hZZzP7j5mtM7O1Znab3zX5xcwizGy1mb3ldy1+MLM4M5tnZuu9/w/D/a6pppnZb7y/gzVm9oqZRftdU6iZ2XNmtsfM1pQYa2lmS81so/dvCz9rLKag8E8+8L/OuT7AMOAWM+vrc01+uQ1Y53cRPnoMeMc51xs4i3r2uzCzeOBWINE51x+IACb4W1WNeAEYVWpsOvC+c64H8L73ve8UFD5xzu10zn3hPT5C0ZtDvL9V1Twz6wRcATzjdy1+MLNmwAXAswDOuRPOuYO+FuWPSCDGzCKBWGCHz/WEnHNuObC/1PBoYI73eA4wpiZrKouCIgyYWQIwCPjM51L88CgwFSj0uQ6/dAOygOe9w2/PmFljv4uqSc65TOAhYBuwEzjknHvX36p80845txOKPkwCbX2uB1BQ+M7MmgDzgV875w77XU9NMrMrgT3OuVV+1+KjSGAw8IRzbhBwjDA53FBTvOPwo4GuQEegsZn9xN+qpCQFhY/MLIqikHjZObfA73p8MAK42sy2AK8CF5vZP/wtqcZlABnOueK9yXkUBUd9cimw2TmX5ZzLAxYA5/lck192m1kHAO/fPT7XAygofGNmRtFx6XXOuYf9rscPzrkZzrlOzrkEiiYvlznn6tUnSefcLmC7mfXyhi4BUn0syQ/bgGFmFuv9XVxCPZvQL2ERMMl7PAl4w8davhHpdwH12AjgeiDFzL70xu5wzr3tX0nik18BL5tZQ2AT8DOf66lRzrnPzGwe8AVFZwOuJkxbWQSTmb0CXAS0NrMM4G5gFjDXzG6kKEDH+1fht9TCQ0REyqVDTyIiUi4FhYiIlEtBISIi5VJQiIhIuRQUIiJSLgWFiIiUS0EhIiLl+v9FPUte6fvAigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plottiong the regression line\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7.9]\n",
      " [4.9]\n",
      " [9.5]\n",
      " [1.3]\n",
      " [9. ]\n",
      " [3.2]]\n"
     ]
    }
   ],
   "source": [
    "# Making Predictions\n",
    "print(X_test) # Testing data - In year experienced\n",
    "y_pred = regressor.predict(X_test) # Predicting the salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>101302.0</td>\n",
       "      <td>100978.090001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>67938.0</td>\n",
       "      <td>71622.400909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>116969.0</td>\n",
       "      <td>116634.457516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46205.0</td>\n",
       "      <td>36395.573999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>105582.0</td>\n",
       "      <td>111741.842667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64445.0</td>\n",
       "      <td>54987.510424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual      Predicted\n",
       "22  101302.0  100978.090001\n",
       "15   67938.0   71622.400909\n",
       "26  116969.0  116634.457516\n",
       "1    46205.0   36395.573999\n",
       "25  105582.0  111741.842667\n",
       "8    64445.0   54987.510424"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.958086359922431\n",
      "Test Score: 0.9394460269118791\n"
     ]
    }
   ],
   "source": [
    "# Estimating training training and test score\n",
    "print(\"Training Score:\", regressor.score(X_train, y_train))\n",
    "print(\"Test Score:\", regressor.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103913.658909727"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting score for 8.2 years of expreience\n",
    "years =[[8.2]]\n",
    "prediction = regressor.predict(years)\n",
    "prediction[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The predicted salary if a employee had worked for 8.2 years is 103913.65890972699"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}