Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# With Years of experience predict the salary\n",
    "# or \n",
    "# With salary predict approximate years of experience "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    YearsExperience   Salary\n",
      "0               1.1   3.9343\n",
      "1               1.3   4.6205\n",
      "2               1.5   3.7731\n",
      "3               2.0   4.3525\n",
      "4               2.2   3.9891\n",
      "5               2.9   5.6642\n",
      "6               3.0   6.0150\n",
      "7               3.2   5.4445\n",
      "8               3.2   6.4445\n",
      "9               3.7   5.7189\n",
      "10              3.9   6.3218\n",
      "11              4.0   5.5794\n",
      "12              4.0   5.6957\n",
      "13              4.1   5.7081\n",
      "14              4.5   6.1111\n",
      "15              4.9   6.7938\n",
      "16              5.1   6.6029\n",
      "17              5.3   8.3088\n",
      "18              5.9   8.1363\n",
      "19              6.0   9.3940\n",
      "20              6.8   9.1738\n",
      "21              7.1   9.8273\n",
      "22              7.9  10.1302\n",
      "23              8.2  11.3812\n",
      "24              8.7  10.9431\n",
      "25              9.0  10.5582\n",
      "26              9.5  11.6969\n",
      "27              9.6  11.2635\n",
      "28             10.3  12.2391\n",
      "29             10.5  12.1872\n"
     ]
    }
   ],
   "source": [
    "print(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression is a statistical approach for modelling relationship between \n",
    "### 1. Dependent variable (Salary)\n",
    "### 2. Independent variables (Experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x255a26c85c8>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5mXlMZm4EXgnszczX9PIcKw3aPeMTbNmxl2O3X8WWHXt7VhpycZekfjKQK3dXErRlzgts3TzC+duOZ2R4iABGhoc4f9vxTuxKqkVkZt1jWNLo6GiOjY0t63uW29WzZcdeJuYpBY0MD/H57Scte8ySVLeI2JeZo3OPD+zK3eWuou23eQFJKstAlnpWwglYSW1h8BecgJXUFgNb6lkud9eU1BYG/yzurimpDSz1SFLLGPyS1DIGvyS1jMEvSS1j8EtSyzRiy4aImAS+U/c4lmE9cF/dg6iZPwN/BuDPoO7//idn5i9sb9yI4G+aiBibb3+MNvFn4M8A/Bn063+/pR5JahmDX5JaxuAvx666B9AH/Bn4MwB/Bn3532+NX5Jaxit+SWoZg1+SWsbg76GIeGJEfCYibo+Ir0TEm+seUx0iYk1EjEfElXWPpQ4RMRwRl0fE14r/F55X95iqFhFvKX4HbouIyyLiUXWPqWwRcWFE3BsRt806dmREXBcRdxT/Pq7OMc4w+HvrQeDPM/MZwHOBP46I42oeUx3eDNxe9yBq9EHg6sx8OvBsWvaziIgR4GxgNDOfBawBXlnvqCpxEfDiOce2A9dn5lOB64vPa2fw91Bm3pOZNxePf0jnF75VG/xHxDHAqcAFdY+lDhFxBHAi8BGAzPxZZk7VO6parAWGImItcDhwd83jKV1m3gDcP+fwacDFxeOLga2VDmoBBn9JImIjsBm4qd6RVO4DwFuBn9c9kJo8BZgEPlqUuy6IiEfXPagqZeYE8B7gTuAe4IHMvLbeUdXmCZl5D3QuDIHH1zwewOAvRUQ8BrgC+NPM/N+6x1OViHgJcG9m7qt7LDVaCzwH+IfM3Az8mD75874qRR37NOBYYAPw6Ih4Tb2j0mwGf49FxDo6oX9pZu6uezwV2wK8NCK+DXwMOCkiLql3SJW7C7grM2f+0ruczhtBm7wQ+FZmTmbmAWA3cELNY6rL9yPiaIDi33trHg9g8PdURASd2u7tmfm+usdTtcw8NzOPycyNdCbz9mZmq670MvN7wHcjYlNx6GTgqzUOqQ53As+NiMOL34mTadkE9yyfAk4vHp8OfLLGsTzEm6331hbgD4FbI+KW4thfZOanaxyTqncWcGlEPAL4JnBGzeOpVGbeFBGXAzfT6XQbp0+3LuiliLgMeAGwPiLuAs4DdgAfj4g30HlDfHl9IzzELRskqWUs9UhSyxj8ktQyBr8ktYzBL0ktY/BLUssY/BpIEXEwIm6Z9VHq6tmIeGnZ55B6xXZODaSI+FFmPqaic63NzAerOJfUC17xqzUi4rERsX9mVW2xT/wfFY9/FBHvjYibI+L6iDiqOP4rEXF1ROyLiM9FxNOL4xdFxPsi4jPAuyPidRHxoeJrR0XEFRHx38XHluL424s92/8zIr4ZEWfPGttrI+LLEfGliPiXxV5HWrXM9MOPgfsADgK3zPp4RXH8RcAX6WwpcfWs5yfw6uLx3wAfKh5fDzy1ePybdLahgM7e61cCa4rPXzfre/4V+K3i8ZPobOEB8HbgC8AjgfXAD4B1wDOB/cD64nlHLvY6fvix2g+3bNCgms7MX597MDOvi4iXA39P5yYpM34O/Fvx+BJgd7HL6gnAJzpbzgCd0J7xicw8OM+5XwgcN+t7joiIXyoeX5WZPwV+GhH3Ak8ATgIuz8z7ijHev9jrZOdeD9KKGfxqlYg4DHgGMA0cSWc3zfkknVLo1HxvIIUfL3D8MOB5mTk959wAP5116CCd38EoztfV60irZY1fbfMWOjtFvgq4sNhGGzq/Cy8rHv8B8F/ZuZfCt4q/EIiOZ899wXlcC/zJzCcRsdAbx4zrgd+PiF8unn/kCl9H6orBr0E1NKedc0dEPA14I537In8OuAH4q+L5PwaeGRH76JRe3lEcfzXwhoj4EvAVOjcYWcrZwGgxWftV4E2LPTkzvwK8C/hscZ6ZLb2X9TpSt2znlKi2/VOqm1f8ktQyXvFLUst4xS9JLWPwS1LLGPyS1DIGvyS1jMEvSS3z/5CE2+qWbPSLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Experience\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.scatter(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>3.9343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>4.6205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3.7731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.3525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>3.9891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience  Salary\n",
       "0              1.1  3.9343\n",
       "1              1.3  4.6205\n",
       "2              1.5  3.7731\n",
       "3              2.0  4.3525\n",
       "4              2.2  3.9891"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.5582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>11.6969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>11.2635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>12.2391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>12.1872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience   Salary\n",
       "25              9.0  10.5582\n",
       "26              9.5  11.6969\n",
       "27              9.6  11.2635\n",
       "28             10.3  12.2391\n",
       "29             10.5  12.1872"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set - To Prevent Overfitting\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": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppose there is your math exam and you learnt all the problems with their answers\n",
    "# 10 question were in book and you know the answer to each of them by heart\n",
    "# In exam 10 questions are there but the values are changed or jumbled \n",
    "# you wont be able to answer any of the problem and will score bad even if you learnt for hours / days\n",
    "\n",
    "# Thats why we see some examples in maths book and then try to solve unsolved question \n",
    "# to check the grasp on subject \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"overfit.png\" width=\"750\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"math.png\" width=\"750\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"curver.png\" width=\"750\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"fruit_overfit.png\" width=\"750\" align=\"center\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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",
      " [ 9.6]\n",
      " [ 4. ]\n",
      " [ 5.3]\n",
      " [ 7.9]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.7731 12.2391  5.7081  6.3218 11.6969 10.9431 11.2635  5.5794  8.3088\n",
      " 10.1302]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 120,
     "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": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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": [
    "# Visualising the Training set results\n",
    "plt.scatter(X_train, y_train, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "\n",
    "plt.title('Salary vs Experience (Training set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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": [
    "# Visualising the Test set results\n",
    "plt.scatter(X_test, y_test, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "\n",
    "plt.title('Salary vs Experience (Test set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your name: 123\n",
      "Enter the years of experience you have: 123\n",
      "\n",
      "\n",
      "\n",
      " Hi, 123 As per your experience of 123.0 years your salary is 117.64 Lakhs\n"
     ]
    }
   ],
   "source": [
    "name = input(\"Enter your name: \") \n",
    "X = float(input(\"Enter the years of experience you have: \"))\n",
    "\n",
    "X = np.array([X]).reshape(-1, 1)\n",
    "y = regressor.predict(X)\n",
    "\n",
    "print(f\"\\n\\n Hi, {name} As per your experience of {X[0][0]} years, your salary is {y[0]:.2f} Lakhs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# END"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}