Simple_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"D:\\MACHINE LEARNING\\Part 2 - Regression\\Section 4 - Simple Linear Regression\\Salary_Data.csv\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
       "        55794.,  83088., 101302.])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_regressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_regressor.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = linear_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40835.10590871, 123079.39940819,  65134.55626083,  63265.36777221,\n",
       "       115602.64545369, 108125.8914992 , 116537.23969801,  64199.96201652,\n",
       "        76349.68719258, 100649.1375447 ])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Vusualizing the training set results\n",
    "import matplotlib.pyplot  as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YeuFOjV01f37KWgrB5ZxzUtYykIMLOIMxs4GmF+7UWK2IdFfJK69sLVu1CoYO7fWqNIQzGDMbWHrhTo3VuPfe1AVUCC6XXpoCTrMEF3CAMbOBphfu1NiR9evhPe+BvfZKy9tsky6iPPbYXjl9n+IAY2YDSy/cqbE9t94KgwbBn/+clq+/HpYuhU02qfup+yT3wZjZwFPnOzWWe+MN2HlnWLgwLb/jHTB3bgo2zcwZjJlZD1xzDWy0UWtw+fOf4f77HVzAGYyZWbcsW5Ympyw47DD4n//p//OH1ZIDjJlZF73znXDffa3LDz4Ib3974+rTVznAmJlVacECGD++tMz3bGyf+2DMzKqw0UalweX22x1cOuMMxsysA7Nnw777lpY5sFTHAcbMrB3lHfYPPwy77NKYuvRHbiIzMysza1ZpcBk3LmUtDi5d4wzGzCyLSPOHFXv2WRgxojH16e+cwZiZARdcUBpcDj88BRwHl+5zBmNmTW3tWthww9Kyl16CzTdvTH0GEmcwZta0vvnN0uBy8skpa6kquPTh2zL3Fc5gzKzpvPJK2yCyZk3bTKZdffi2zH2JMxgzayof+UhpcDn//JS1VB1coE/elrkvcgZjZk3huefadtivX9/NySn70G2Z+zJnMGY24L3tbaXB5dprU9bS7ZmP+8htmfs6BxgzG7AeeywFkcceay2LgH/6px4euMG3Ze4vHGDMbECSUuZS8Je/1HAOsQbelrk/cR+MmQ0oM2fCpz5VWlaXySl7+bbM/ZEDjJkNGOV9Ko89Bjvv3Ji6mJvIzGwAOOOMtsElwsGl0ZzBmFm/VWlyyiefhB13bEx9rJQzGDPrlz7xibbBJcLBpS+pW4CR9HNJSyU9VFQ2XNJNkubn52FF606VtEDS45IOLSrfS9K8vO58KSXCkjaWdGUuny1pbNE+k/M55kuaXK/3aGa9729/S81hV13VWrZihe8y2RfVM4P5JTChrOzbwM0RMR64OS8jaRdgIrBr3ucCSYPyPhcCU4Dx+VE45vHAiogYB/wI+EE+1nDgdGAfYG/g9OJAZmb91847w6abti6/610psGy5ZePqZO2rW4CJiDuA5WXFRwLT8+vpwFFF5VdExOsRsRBYAOwtaQQwNCLuiogALi3bp3Csq4GDc3ZzKHBTRCyPiBXATbQNdGbWjyxblrKWJ55oLVuzBu65p3F1ss71dh/MdhGxBCA/b5vLRwLPFG23OJeNzK/Ly0v2iYi1wCpgqw6O1YakKZLmSJqzbNmyHrwtM6sXCbbdtnX585/vxuSU1hB9pZO/0oxA0UF5d/cpLYyYFhEtEdGyzTbbVFVRM6ugDvdGKUzzUmz9+nTBvPUPvR1gns/NXuTnpbl8MbBD0XajgGdz+agK5SX7SBoMbEFqkmvvWGZWD4V7ozz1VEotCvdG6UGQKZ/m5Zxzejg5pTVEbweYWUBhVNdk4Lqi8ol5ZNiOpM78e3Iz2suS9s39K8eV7VM41tHALbmf5vfAIZKG5c79Q3KZmdVDDe+NcuutlS+Y/NrXelA/a5i6XWgpaSZwILC1pMWkkV1nAldJOh54GvgYQEQ8LOkq4BFgLfCliFiXD3UCaUTapsCN+QFwCfArSQtImcvEfKzlkr4P/G/e7oyIKB9sYGa1UqN7o5QHlt/8Bo46qvK21j8oPHgcgJaWlpgzZ06jq2HW/4wdm5rFyo0ZA4sWdbr7L38Jn/lMaZm/lvoPSXMjoqXSur7SyW9m/VUP7o0ilQaXuXMdXAYSBxgz65lu3BvlO9+p3NfyznfWua7WqzzZpZn1XJX3Rlm/HgYNKi17+mnYYYfK21v/5gzGzHrFEUeUBpfNNktZi4PLwOUMxszqavXqFEyKvfQSbL55Y+pjvccZjFl/VIcr5+th5MjS4PL+96esxcGlOTiDMetvClfOFy5uLFw5D33mHvHPPQcjRpSWrV3btv/FBjZnMGb9TQ2vnK8HqTS4nHRSylocXJqPMxiz/qZGV87X2kMPwdvfXlrma1qamzMYs/5m9OiulfcCqTS4/OQnDi7mAGPW//Tgyvku62QwwaWXVr5g8otfrH1VrP9xgDHrb7px5Xy3dDINvwSTJ7du/otfOGuxUp7sMvNkl2Zl2pnE8ltDL+Ssl75QUuavkebV0WSX7uQ3s8oqDBoQAS+1Lt9xB7z3vb1YJ+tX3ERmZpUVDRp4P7ek4FIkwsHFOuYAY2aVTZ3K2k03RwS38f6/Fz957m/dJGZVcYAxs4o2/PQkNnztpZKyuGwGO57s20xaddwHY2YlVq6EYcNKy1atgqFDAfrGVDTWPzjAmNnflV/TsvnmaeZjs+5wE5mZMX9+2+Cydq2Di/WMA4xZk5Ngp51alydM8OSUVhtuIjNrUrfeCgcdVFrm0WFWS85gzJqQVBpc/t//c3Cx2nMGY9ZEpk2Df/7n0jIHFquXqgKMpEERsa7elTGz+invxJ85EyZObExdrDlU20S2QNJZknapa23MrOZOPLHylPoOLlZv1TaR7Q5MBC6WtAHwc+CKiPAgRrM+KiLdxqXY7Nmw996NqY81n6oymIh4OSIuioj9gW8BpwNLJE2XNK6uNTSzLtt337bBJcLBxXpX1X0wwIeAzwBjgXOAGcB7gRuAndrd2cx6zZo1sPHGpWXPPAOjRjWmPtbcqm0imw/cCpwVEX8pKr9a0gG1r5aZdVV5Pwt4hJg1VqdNZDl7+WVEHF8WXACIiJPqUjMzq8qLL7YNLq+84uBijddpgMnDk9/f2XZm1vsk2Hrr1uWRI1Ng2WyzxtXJrKDaJrK/SPoxcCXwaqEwIu6tS63MrENz50JL2V3Q161r27Fv1kjVBpj98/MZRWUBHFRhWzOrhxkz4LTT0FOLSoo/9jG46qrGVMmsI1UFmIhwE5lZI82YweWf/SOT1iwqKY7LZsAk3wTM+iZFlT2Bkj4E7ApsUiiLiDPa36N/aWlpiTlz5jS6GmYVlXfif4ELuZAvwpgxsGhRQ+pkBiBpbkS0VFpXVYutpJ8CnwC+DAj4GDCmBxU6WdLDkh6SNFPSJpKGS7pJ0vz8PKxo+1MlLZD0uKRDi8r3kjQvrztfSv8NJW0s6cpcPlvS2O7W1ayRvvnNCtO8oBRcAJ5+uvcrZValarsE94+I44AVEfGvwH7ADt05oaSRwElAS0TsBgwiTUPzbeDmiBgP3JyXyfOfTSRlTxOAC/LQaYALgSnA+PyYkMuPz3UdB/wI+EF36mrWSBKcfXbr8kV8jqAs2owe3buVMuuCagPMa/l5taTtgTeAHXtw3sHAppIGA0OAZ4Ejgel5/XTgqPz6SNK8Z69HxEJgAbC3pBHA0Ii4K1I736Vl+xSOdTVwcCG7Mevr3v3uClnLZTP43JCZpYVDhsDUqb1XMbMuqjbAXC9pS+As4F5gEXBFd04YEf8HnA08DSwBVkXEH4DtImJJ3mYJsG3eZSTwTNEhFueykfl1eXnJPhGxFlgFbFVeF0lTJM2RNGfZsmXdeTtmNRORAstfii5nvvPOfMHkpEnpZi5jxqSNxoxJy+7gtz6s2lFk388vr5F0PbBJRKzqzglz38qRpAxoJfBrScd0tEulKnVQ3tE+pQUR04BpkDr5O6iDWV1VNc3LpEkOKNavdBhgJH2kg3VExLXdOOc/AgsjYlk+zrWk62yelzQiIpbk5q+lefvFlPb3jCI1qS3Or8vLi/dZnJvhtgCWd6OuZnW1enXbq+6fespdKzYwdJbBHN7BugC6E2CeBvaVNITUt3MwMIc0Q8Bk4Mz8fF3efhZwuaRzge1Jnfn3RMQ6SS9L2heYDRwH/FfRPpOBu4CjgVui2vHYZr3Ek1PaQNdhgImIz9T6hBExW9LVpL6ctcB9pGaqNwFXSTqeFIQ+lrd/WNJVwCN5+y8V3b75BOCXwKbAjfkBcAnwK0kLSJmL791nfcYzz7TNUF59NfXZmw0kvtAy84WW1huctdhA0+cutDRrNn/6U9vgsn69g4sNbL1+oaVZs5Hgve9tXd5339YhyWYDWXcvtFxLzy60NBvwLr64wgWTAXfd1Zj6mPW2rl5o+UNgLrCQbl5oadYMJPj851uXv/51N4dZ8+nsOph3Ac8ULrSU9CZgHvAYaY4vMyvyxS/ChReWljmwWLPqLIP5GbAGQNIBpGtUfkaaemVafatm1r9IpcHlV79ycLHm1tmFloMionAF/CeAaRFxDWnKmPvrWzWz/mHQoDQirJgDi1nnGcygPNUKpCvubylaV+3tls0GpPXrU9ZSHFzuucfBxaygsyAxE7hd0gukkWR3AkgaR2omM2tKvmDSrHOdTRUzVdLNwAjgD0XzeW1AuujSrKmsXAnDhpWWeXJKs8o6beaKiLsrlD1Rn+qY9V3OWsy6ptrrYMya1qOPtg0uq1c7uJh1xh31Zh1w1mLWfc5gzCr47W89OaVZTzmDMStTHljGjIFFixpSFbN+zRmMWfbd71aenNLBxax7nMGY0TawfO5zcNFFjamL2UDhAGNN7X3vgzvuKC1zP4tZbbiJzJqWVBpcLrnEwcWslhxgrOlIlftaPvvZTnacMQPGjoUNNkjPM2bUqYZmA4MDjDWNtWvbBpa7764ya5kxA6ZMSfPCRKTnKVMcZMw64ABjTUGCDTcsLYuAffap8gCnnZYu3y+2enUqN7OKHGBsQHvhhbZZy5Il3ehrefrprpWbmQOMDVwSbLNNaVkE/MM/dONg7U2XPHq0+2bM2uEAYwPO/fe3zVpef72HI8SmToUhQ0rLhgyBD37QfTNm7XCAsQFFgj33LC2LgI026uGBJ02CadPSvDFSep42DW64wX0zZu1wgLHaalBz0cyZlYce1/S6lkmT0rwx69en50mT3Ddj1gFfyW+1UxjKW/hFX2gugvRlXCflgWX33eGBB+p2ulKjR6f3WancrMk5g7Ha6eWhvCefXDlr6bXgAu33zUyd2ouVMOubHGCsdnqxuUiC885rXf7qVxs0zUt7fTN1zNjM+gs3kVnt9EJzUZ+cnHLSJAcUswqcwVjt1Lm5qHxyymuv7QPBxcza5QzGaqfwK/6001Kz2OjRKbj08Nd9eT8LOLCY9QcOMFZbNWwuWrMGNt64tGzePNhtt5oc3szqzE1k1idJbYNLRA2Di6d3Mas7BxjrU55/vm2T2Isv1rhJzFPvm/WKhgQYSVtKulrSY5IelbSfpOGSbpI0Pz8PK9r+VEkLJD0u6dCi8r0kzcvrzpfSV5OkjSVdmctnSxrb++9yAKvTr3+p7USUETB8eE0O38pT75v1ikZlMP8J/C4i3gq8A3gU+DZwc0SMB27Oy0jaBZgI7ApMAC6QNCgf50JgCjA+Pybk8uOBFRExDvgR8IPeeFNNoQ6//ufObZu1vPFGHTvyPb2LWa/o9QAjaShwAHAJQESsiYiVwJHA9LzZdOCo/PpI4IqIeD0iFgILgL0ljQCGRsRdERHApWX7FI51NXBwIbuxHqrxr38JWlpalzfZJAWWwfUcftLR1PtmVjONyGDeDCwDfiHpPkkXS9oM2C4ilgDk523z9iOBZ4r2X5zLRubX5eUl+0TEWmAVsFV5RSRNkTRH0pxly5bV6v0NbDX69T9jRuVpXl57rZv16gpP72LWKxoRYAYD7wQujIg9gVfJzWHtqJR5RAflHe1TWhAxLSJaIqJlm/I7U1llNfj1L8Exx7QuH354L1/X4uldzHpFIwLMYmBxRMzOy1eTAs7zudmL/Ly0aPsdivYfBTyby0dVKC/ZR9JgYAtgec3fSTPqwa//9iannDWrhvWrVqWp982spno9wETEc8AzknbORQcDjwCzgMm5bDJwXX49C5iYR4btSOrMvyc3o70sad/cv3Jc2T6FYx0N3JL7aaynuvnrv3xyyjPP9NX4ZgNdo0aRfRmYIelBYA/g34EzgQ9Img98IC8TEQ8DV5GC0O+AL0XEunycE4CLSR3/fwVuzOWXAFtJWgB8jY6b4KyruvDrf889K2ctp5zSzg7VDoH2hZJmfV9E+BHBXnvtFdaByy6LGDMmQkrPl13W4ebr1xfuJ9n6uPHGKs4xZEjpTkOGtD1XtduZWd0Bc6Kd71WF2ykAaGlpiTlz5jS6Gn1T+Z0qIfW7tNM01u3JKceOrVgPgH0AAAw9SURBVDzd/5gxKVPq6nZmVneS5kZES6V1nirGOlfltS9vvNE2uDz+eBf6WqodAu0LJc36BQcY61wVX+jbbQcbbVS6OgJ22qkL56l2CLQvlDTrFxxgrHMdfKEvX56ylqVLW4tXr+7mCLFqh0D7QkmzfsEBxjrXzhe6nlrEVkXzI+y3H8RlM9j0bWO7N7qr2iHQvlDSrF9wJ3/mTv5OzJjx9ztVzh9xADs9e1vJ6nXrYIOZXRsMYGb9X0ed/A4wmQNMdco78b/0Jfjxj/OCR3eZNZ2OAoxvmWxVuesu2H//0rI2v008usvMirgPxjollQaXCy5opxPfo7vMrIgDjLXr17+uPM3LCSe0s4NHd5lZEQcYq0iCj3+8dfnOO6sYeuzRXWZWxH0wVuI//gO+853Ssi6NA5k0yQHFzAAHGMsi0qUrxebPh3HjGlMfM+v/3ERmHHts2+AS4eBiZj3jDKaJvf46bLJJadmLL8Lw4Y2pj5kNLM5gmtTb314aXN7xjpS1OLiYWa04g2kyy5dTMn8YpEymfCZkM7OecgbTRKTS4HLccSlrcXAxs3pwBtMEFiyA8eNLy9avr3znSTOzWnEGM8BJpcHlzDNT1uLgYmb15gxmgLrzTjjggNIyT5xtZr3JGcwAJJUGl6uvdnAxs97nAFMvM2ak+6N0586O3XTnnZUnp/zoR+t+ajOzNtxEVg8zyu7s+NRTaRnqNk9XeWC5+27YZ5+6nMrMrCrOYOrhtNNKbxsMafm002p+qquuKg0ue+yRshYHFzNrNGcw9dALd3asNDnl0qWwzTY1O4WZWY84g6mHOt/Z8ZxzSoPLxIkp4Di4mFlf4gymHqZOLe2DgZrc2XHNGth449KyV19texNJM7O+wBlMPdThzo4nnlgaXE4b+l+ENmDILmN7ZYSamVlXOYOplxrd2fGll2CLLUrL1m66OYNeeiUt9MIINTOz7nAG04cdemhpcPnZzyDGjGXQa6+UblinEWpmZj3hDKYPWrwYdtihtOzvk1N+of4j1MzMasEZTB+zww6lweWGG8omp6zzCDUzs1pxgOkj5s1LQWTx4tayCDjssLINp05tO2ysBiPUzMxqzQGmD5Bg991bl+fM6WByyjqMUDMzqwf3wTTQLbfAwQe3Lm++eRo11qkajVAzM6unhmUwkgZJuk/S9Xl5uKSbJM3Pz8OKtj1V0gJJj0s6tKh8L0nz8rrzpdRTIWljSVfm8tmSxvb2++uMVBpcnnyyyuBiZtZPNLKJ7CvAo0XL3wZujojxwM15GUm7ABOBXYEJwAWSBuV9LgSmAOPzY0IuPx5YERHjgB8BP6jvW6nejBmlk1Put19qDttxxxodvJdvEWBm1p6GBBhJo4APARcXFR8JTM+vpwNHFZVfERGvR8RCYAGwt6QRwNCIuCsiAri0bJ/Csa4GDi5kNzVX5Zd6YZjxMce0lr34IvzlLzWsx5Qp6cLLiNYLMB1kzKxBGpXBnAd8C1hfVLZdRCwByM/b5vKRwDNF2y3OZSPz6/Lykn0iYi2wCtiqvBKSpkiaI2nOsmXLuv4uqvxS//d/h0GDWpcnT06bDx/e9VO2qxdvEWBmVo1e7+SX9GFgaUTMlXRgNbtUKIsOyjvap7QgYhowDaClpaXrNxXu6Et90iRefx022aR09WuvtS2riV64RYCZWVc0IoN5N3CEpEXAFcBBki4Dns/NXuTnpXn7xUDxde2jgGdz+agK5SX7SBoMbAEsr/k76eBLfdas0kByxhkpa6lLcAFfgGlmfU6vB5iIODUiRkXEWFLn/S0RcQwwC5icN5sMXJdfzwIm5pFhO5I68+/JzWgvS9o3968cV7ZP4VhH53N0PUPpTIUv79fYhC1ZyZFHtpatWwf/8i81P3spX4BpZn1MX7rQ8kzgA5LmAx/Iy0TEw8BVwCPA74AvRcS6vM8JpIECC4C/Ajfm8kuArSQtAL5GHpFWc2Vf6r/g0wzhNVbFUADuu6/ynSfrwhdgmlkfo3r8sO+PWlpaYs6cOV3fccYMVp76A4Y98+Dfiz71KQ/eMrPmIGluRLRUWucr+Xto3cRJDDumNUtYsADe8pYGVsjMrI9wgOmhDTaAk09Ow5DPOqvRtTEz6zscYHpIgnPPbXQtzMz6nr7UyW9mZgOIA4yZmdWFA4yZmdWFA4yZmdWFA4yZmdWFA4yZmdWFA4yZmdWFA4yZmdWF5yLLJC0Dnmp0Pbpoa+CFRleiwZr9M2j29w/+DKCxn8GYiNim0goHmH5M0pz2JplrFs3+GTT7+wd/BtB3PwM3kZmZWV04wJiZWV04wPRv0xpdgT6g2T+DZn//4M8A+uhn4D4YMzOrC2cwZmZWFw4wZmZWFw4w/YykHSTdKulRSQ9L+kqj69QokgZJuk/S9Y2uSyNI2lLS1ZIey38P+zW6Tr1N0sn5/8FDkmZK2qTRdao3ST+XtFTSQ0VlwyXdJGl+fh7WyDoWOMD0P2uBr0fE24B9gS9J2qXBdWqUrwCPNroSDfSfwO8i4q3AO2iyz0LSSOAkoCUidgMGARMbW6te8UtgQlnZt4GbI2I8cHNebjgHmH4mIpZExL359cukL5WRja1V75M0CvgQcHGj69IIkoYCBwCXAETEmohY2dhaNcRgYFNJg4EhwLMNrk/dRcQdwPKy4iOB6fn1dOCoXq1UOxxg+jFJY4E9gdmNrUlDnAd8C1jf6Io0yJuBZcAvcjPhxZI2a3SlelNE/B9wNvA0sARYFRF/aGytGma7iFgC6UcosG2D6wM4wPRbkt4EXAN8NSJeanR9epOkDwNLI2Juo+vSQIOBdwIXRsSewKv0kWaR3pL7GY4EdgS2BzaTdExja2XFHGD6IUkbkoLLjIi4ttH1aYB3A0dIWgRcARwk6bLGVqnXLQYWR0Qhe72aFHCayT8CCyNiWUS8AVwL7N/gOjXK85JGAOTnpQ2uD+AA0+9IEqnd/dGIOLfR9WmEiDg1IkZFxFhSp+4tEdFUv1wj4jngGUk756KDgUcaWKVGeBrYV9KQ/P/iYJpsoEORWcDk/HoycF0D6/J3gxtdAeuydwPHAvMk3Z/LvhMRNzSwTtYYXwZmSNoIeBL4TIPr06siYrakq4F7SaMr76OPTplSS5JmAgcCW0taDJwOnAlcJel4UuD9WONq2MpTxZiZWV24iczMzOrCAcbMzOrCAcbMzOrCAcbMzOrCAcbMzOrCAcasCkr+JOmworKPS/pdg+rzVkn352li3lK2bpGkeXn9/ZLOz+WzJB1btN1Fkr6ZX98m6XFJD0j6c9H1NWbd5mHKZlWStBvwa9L8b4OA+4EJEfHXbhxrUESs60Fdvg1sGhGnV1i3iDTD8Atl5WOBW0n13wX4KbBXRLwh6TbgGxExR9IU4MMRcUR362cGzmDMqhYRDwH/DZxCurjtMuA0Sf+bM4kjIX2RS7pT0r35sX8uPzDfy+dy0oWym0n6n5w1PCTpE+XnlLSHpLslPSjpN5KGSfog8FXgc5Ju7UL9F5EuRPwhcAFwYp5ipdwdwLgufDRmFflKfrOu+VfSleNrgOtJ09R8VtKWwD2S/kiaB+oDEfE3SeOBmUBL3n9vYLeIWCjpo8CzEfEhAElbVDjfpcCXI+J2SWcAp0fEVyX9FHglIs5up563SipkSNMj4kf59dnAX4E787TvlRwOzKvq0zDrgAOMWRdExKuSrgReAT4OHC7pG3n1JsBo0j1JfixpD2AdsFPRIe6JiIX59TzgbEk/AK6PiDuLz5UDzpYRcXsumk5qoqvG+8ubyLLdAQFvlbRBRBTf7mCGpNeARaRpaMx6xE1kZl23Pj8EfDQi9siP0RHxKHAy8DzpLpMtwEZF+75aeBERTwB7kQLNf0j6bj0rLWkDUtPYscB84ISyTSbl93FURDxTz7pYc3CAMeu+3wNfzjP5ImnPXL4FsCRnB8eSBgS0IWl7YHVEXEZquiqZbj8iVgErJL03Fx0L3E73/TMwPyJuA74GfEvSNj04nlmH3ERm1n3fJ91Z88EcZBYBHyZlCddI+hhp1Nar7ez/duAsSeuBN2ibUUCaev2nkobQtRmTi/tgHgS+QRqcsC9ARDwr6T9JHf5tjpmD38UR8cEqz2fWhocpm5lZXbiJzMzM6sIBxszM6sIBxszM6sIBxszM6sIBxszM6sIBxszM6sIBxszM6uL/A459UwOy7YguAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train,Y_train,color=\"red\")\n",
    "plt.plot(X_train,linear_regressor.predict(X_train),color=\"blue\")\n",
    "plt.title(\"SALARY vs. EXPERIENCE (training set)\")\n",
    "plt.xlabel(\"Years of EXP.\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": [
    "plt.scatter(X_test,Y_test,color=\"red\")\n",
    "plt.plot(X_train,linear_regressor.predict(X_train),color=\"blue\")\n",
    "plt.title(\"SALARY vs. EXPERIENCE (test set)\")\n",
    "plt.xlabel(\"Years of EXP.\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}