Employee_Salary_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import requests\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the dataset\n",
    "dataset=pd.read_csv('Salary_Data.csv')"
   ]
  },
  {
   "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",
       "  </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the X and Y variables\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Simple Linear Regression to the Training set\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#confusion matrix\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f43fb6d1208>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#PLOT\n",
    "plt.scatter(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9749154407708353"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check accuracy Score of model\n",
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving model to disk\n",
    "pickle.dump(regressor, open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading model to compare the results\n",
    "model = pickle.load( open('model.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[45508.07713028]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict([[2.0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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