Mahak Agrawal Salary Prediction.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Name - Mahak Agrawal\n",
    "Enrollment No. - EN19CS301188\n",
    "\n",
    "Project - Salary Prediction Model\n",
    "\n",
    "Concept - Supervised ML using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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       "<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",
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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"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading data from csv file\n",
    "df = pd.read_csv(\"Salary_Data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>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",
       "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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      ],
      "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": 37,
     "metadata": {},
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <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",
       "    </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",
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      ],
      "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There is no null values in the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "df.plot(x='YearsExperience',y='Salary')\n",
    "plt.title('Years of Experience Vs Salary')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NgZxmtuW6m4RysAQZj+TvxqhRo3jyySeH/Jdr5Xkwo0aNKjsrZoNGUYOzBxJPQtWNcePG0dHR4eegsPGJlmbWGEUNzh5IHGC6MWLECD/B0cwKUeTg7IHCVWRmZiUoanD2QOISjJlZCYoanD2QOMCY2aDTLN1/i54LrGwOMGY2qAyF7r/Nwm0wZjaoDIXuv83CJRgzG1Satftvs1Tr9YZLMGY2qHTVzXcgd/8t6pHrZXOAMbNBpRm7/w7Waj1XkZnZoNKM3X+btVqvJw4wZjboNFv338E6qt9VZGZmJWvGar16uARjZlayZqzWq4cDjJnZANBs1Xr1cIAxM6syGMeklMEBxswsx1PNNI4b+c3McgbrmJQyOMCYmeUM1jEpZXCAMTPLacapZgYqBxgzs5zBOialDG7kNzPLGaxjUsrgAGNmVmUwjkkpgwOMmdkW8FiZnjnAmJn1ksfK1MeN/GZmveSxMvVxgDEz6yWPlamPA4yZWS95rEx9HGDMzHrJY2Xq40Z+M7Ne8liZ+jjAmJltAY+V6ZmryMzMrBAOMGZmVghXkZlZ0/Do+ebiAGNmTcGj55uPq8jMrCl49HzzKSzASDpf0uOS7s2l7SjpBkkPpr875NbNkrRc0jJJh+fS95N0T1r3LUlK6SMl/TSl3yZpfG6f6ekcD0qaXtQ1mln/8ej55lNkCWYecERV2kzgxojYA7gxvUfSXsAxwN5pn+9Jqoxi+j4wA9gjvSrHPBF4OiJ2B84BzkrH2hE4DXgrcABwWj6QmVlz8uj55lNYgImIXwFPVSVPAS5MyxcCU3Ppl0XE8xHxELAcOEDSLsB2EbEoIgK4qGqfyrHmA4ek0s3hwA0R8VREPA3cwOaBzsyajEfPN5/+buTfOSJWA0TEakmvSultwK257TpS2vq0XJ1e2WdlOtYGSc8AO+XTa+xjZk3Ko+ebz0DpRaYaadFN+pbus+lJpRlk1W+8+tWv7jmXZlYqj55vLv3di+yxVO1F+vt4Su8Ads1tNw54JKWPq5G+yT6ShgPbk1XJdXWszUTE3Ihoj4j2MWPG9OGyzIa2BUtXMWnOQibMvIZJcxayYOmqsrNkA0B/B5irgEqvrunAlbn0Y1LPsAlkjfm3p+q0ZyUdmNpXjqvap3Ks9wMLUzvNdcBhknZIjfuHpTQzK0BlfMqqzrUEG8enOMhYkd2ULwUWAXtK6pB0IjAHOFTSg8Ch6T0RcR9wOXA/8Evg5IiodHj/JHAuWcP/H4FrU/p5wE6SlgOfJ/VIi4ingK8Bd6TXGSnNzArg8SnWFWU/+q29vT0WL15cdjbMms6EmdfUbOQU8NCcI/s7O9bPJC2JiPZa6zyS38z6xONTrCsOMGbWJx6fYl0ZKN2UzaxJeXyKdcUBxsz6zONTrBZXkZmZWSEcYMzMrBCuIjNrQn6yozUDBxizJuMnO1qzcBWZWZPxyHlrFg4wZk3GT3a0ZuEAY9ZkPHLemoUDjFmT6c+R856G3/rCjfxmTaa/Rs67M4H1lQOMWRPqj5Hz3XUmcICxeriKzMxqcmcC6ysHGDOryZ0JrK8cYMysJk/Db33lNhgzq8nT8FtfOcCYWZc8Db/1havIzMysEA4wZmZWCAcYMzMrhAOMmZkVwgHGzMwK4QBjZmaFcIAxM7NCOMCYmVkhHGDMzKwQdQUYScN63srMzGyjekswyyWdLWmvQnNjZmaDRr0B5o3AH4BzJd0qaYak7QrMl5mZNbm6JruMiGeBHwE/kvT3wKXAOZLmA1+LiOUF5tGsaS1YusqzEduQVVeASW0wRwInAOOB/wAuAd4B/AJ4XUH5M2tafqa9DXX1Ttf/IHATcHZE/DaXPj+VaMysip9pb0NdjwEmlV7mRcQZtdZHxKcbniuzQcDPtLehrsdG/oh4EfiHfsiL2aDS6GfaL1i6iklzFjJh5jVMmrOQBUtX9SV7ZoWrtxfZbyV9R9I7JL2l8io0Z2ZNrpHPtK+056zqXEuwsT3HQcYGsnrbYN6e/uaryQI4uLHZMRs8GvlMe7fnWDOqt5tyQ6vIJH0O+ChZkLqHrHfa1sBPyXqprQA+EBFPp+1nAScCLwKfjojrUvp+wDyghaw322ciIiSNBC4C9gOeBKZFxIpGXoNZPRr1THu351gzqnsuMklHSvqSpH+pvLbkhJLagE8D7RGxDzAMOAaYCdwYEXsAN6b3pNkDjgH2Bo4Avpebuub7wAxgj/Q6IqWfCDwdEbsD5wBnbUlezQaKRrfnmPWHeuci+wEwDTgFEHA0sFsfzjscaJE0nKzk8ggwBbgwrb8QmJqWpwCXRcTzEfEQsBw4QNIuwHYRsSgigqzEkt+ncqz5wCGS1If8mpWqke05Zv2l3hLM2yPiOLJSwf8D3gbsuiUnjIhVwNeBh4HVwDMRcT2wc0SsTtusBl6VdmkDVuYO0ZHS2tJydfom+0TEBuAZYKfqvKQpbxZLWrxmzZotuRyzfjF13zZmHzWRttYWBLS1tjD7qIluf7EBrd5G/kpF798kjSVr15iwJSeUtANZCWMC0An8l6QPdbdLjbToJr27fTZNiJgLzAVob2/fbL3ZQNKo9hyz/lJvCeZqSa3A2cCdZI3wl23hOd8JPBQRayJiPXAFWS+1x1K1F+nv42n7DjYtLY0jq1LrSMvV6Zvsk6rhtgee2sL8mpnZFqgrwETE1yKiMyJ+Rtb28vqI+L9beM6HgQMlbZ3aRQ4BHgCuAqanbaYDV6blq4BjJI2UNIGsMf/2VI32rKQD03GOq9qncqz3AwtTO42ZmfWTbqvIJB3VzToi4orenjAibkuzMN8JbACWklVTbQtcLulEsiB0dNr+PkmXA/en7U9OswsAfJKN3ZSvTS+A84CLJS0nK7kc09t8mplZ36i7H/aSLuhm34iIjzQ+S+Vob2+PxYsXl50NM7OmImlJRLTXWtdtCSYiTigmS2ZmNtjV24sMSUeSDXYcVUnraoZlMzOzsgZampnZINfvAy3NzGxoqDfAVA+03MAWDrQ0M7Ohod42mMpAy38HlqS0cwvJkZmZDQo9jYPZH1gZEV9L77clm17/92SzFJuZmdXUUxXZD4EXACT9PTAnpT1DmsPLzMyslp6qyIZFRGUOr2nA3DRdzM8k3VVozszMrKn1VIIZliaLhGzOsIW5dXWPoTEzs6GnpyBxKXCzpCfIepL9GkDS7mTVZGZmZjX1NFXMmZJuBHYBrs/NSLwV2aBLMzOzmnqs5oqIW2uk/aGY7JiZ2WBR70BLMzOzXnGAMTOzQjjAmJlZIRxgzMysEA4wZmZWCAcYMzMrhEfjm9VpwdJVnH3dMh7pXMvY1hZOPXxPpu7bVna2zAYsBxizOixYuopZV9zD2vUvArCqcy2zrrgHwEHGrAuuIjOrw9nXLXs5uFSsXf8iZ1+3rKQcmQ18DjBmdXikc22v0s3MAcasLmNbW3qVbmYOMGZ1OfXwPWkZMWyTtJYRwzj18D1ZsHQVk+YsZMLMa5g0ZyELlq4qKZdmA4sb+c3qUGnIr+5FBrjx36wLDjBmdZq6b9tmQWPSnIVdNv47wNhQ5yoysz5w479Z11yCsYYaaoMRx7a2sKpGMHHjv5lLMNZAlcGIqzrXEmxsjxjMjd7dNf6bDXUuwVjDdDcYsa+lmIFaMuqq8X8g5M2sbA4w1jBFtUcM9GlaajX+m5mryKyBihqM6GlazJqTA4w1TFHtEe6pZdacHGCsYabu28bsoybS1tqCgLbWFmYfNbHP1UeepsWsObkNxhqqiPaIUw/fc5M2GOh7yWigdhowG0wcYGzAa3RPrYHeacBssHCAsabQyJJRkd2pzWyjUtpgJLVKmi/p95IekPQ2STtKukHSg+nvDrntZ0laLmmZpMNz6ftJuiet+5YkpfSRkn6a0m+TNL6Eyxy0mn32YHcaMOsfZTXyfxP4ZUS8HngT8AAwE7gxIvYAbkzvkbQXcAywN3AE8D1Jla5K3wdmAHuk1xEp/UTg6YjYHTgHOKs/LmooGAyj9d1pwKx/9HuAkbQd8PfAeQAR8UJEdAJTgAvTZhcCU9PyFOCyiHg+Ih4ClgMHSNoF2C4iFkVEABdV7VM51nzgkErpxvpmMIxJ8fQuZv2jjBLMa4A1wAWSlko6V9I2wM4RsRog/X1V2r4NWJnbvyOltaXl6vRN9omIDcAzwE7VGZE0Q9JiSYvXrFnTqOsb1AZD9VJR3anNbFNlNPIPB94CnBIRt0n6Jqk6rAu1Sh7RTXp3+2yaEDEXmAvQ3t6+2Xrb3GCZPdjTu5gVr4wSTAfQERG3pffzyQLOY6nai/T38dz2u+b2Hwc8ktLH1UjfZB9Jw4HtgacafiVDkKuXzKxe/R5gIuJRYKWkyjfSIcD9wFXA9JQ2HbgyLV8FHJN6hk0ga8y/PVWjPSvpwNS+clzVPpVjvR9YmNpprI+Krl5q9h5qZrZRWeNgTgEukfQK4E/ACWTB7nJJJwIPA0cDRMR9ki4nC0IbgJMjotLK/ElgHtACXJtekHUguFjScrKSyzH9cVFDRVHVS70ZAJkfib99ywgk6Pzbeo/KNxtA5B/2mfb29li8eHHZ2Riw+mNqlUlzFtZs32lrbeGWmQdvkpfqqWPyWkYMc6O9WT+RtCQi2mut82SX1qP+GvtSbw+1Wl2l85qt27TZYOUAYz3qr7Ev9Q6ArKdLdDN1mzYbrBxgrEf9Nfal3h5qrVuP6PFYzdZt2mwwcoCxHvXX1Cr19lDrqdnQ3abNBgbPpmw96s3zWPraGaCeHmrPrF3f5bo29yIzGzAcYKxH9T6Ppb+es9LVbALVvc3MrFwOMFaXekoW/fWclSKecGlmjecAYw3TX50BGv2ESzMrhgOMNUx/ToTpySrNBj73IrOG8USYZpbnEow1jKuuzCzPAcYaylVXZlbhKjIzMyuEA4yZmRXCAcbMzArhAGNmZoVwgDEzs0I4wJiZWSEcYMzMrBAOMGZmVggHGDMzK4QDjJmZFcIBxszMCuG5yArS10cHm5k1OweYAvTXo4PNzAYyV5EVoLtHB5uZDRUOMAXor0cHm5kNZA4wBejqEcFFPDrYzGygcoApgB8dbGbmRv5CFP3oYPdQM7Nm4ABTkKIeHeweambWLFxF1mTcQ83MmoUDTJNxDzUzaxYOME3GPdTMrFk4wDQZ91Azs2bhRv4mU3QPNTOzRnGAaUJF9VAzM2uk0qrIJA2TtFTS1en9jpJukPRg+rtDbttZkpZLWibp8Fz6fpLuSeu+JUkpfaSkn6b02ySN7/cLNDMb4spsg/kM8EDu/UzgxojYA7gxvUfSXsAxwN7AEcD3JFUaIb4PzAD2SK8jUvqJwNMRsTtwDnBWsZcyMCxYuopJcxYyYeY1TJqzkAVLV5WdJTMbwkoJMJLGAUcC5+aSpwAXpuULgam59Msi4vmIeAhYDhwgaRdgu4hYFBEBXFS1T+VY84FDKqWbRhsoX+qVAZirOtcSbByA6SBjZmUpqwTzDeBLwEu5tJ0jYjVA+vuqlN4GrMxt15HS2tJydfom+0TEBuAZYKeGXgED60vdAzDNbKDp9wAj6T3A4xGxpN5daqRFN+nd7VOdlxmSFktavGbNmjqzs9FA+lL3AEwzG2jKKMFMAv5R0grgMuBgST8GHkvVXqS/j6ftO4Bdc/uPAx5J6eNqpG+yj6ThwPbAU9UZiYi5EdEeEe1jxozp9YUMpC91D8A0s4Gm3wNMRMyKiHERMZ6s8X5hRHwIuAqYnjabDlyZlq8Cjkk9wyaQNebfnqrRnpV0YGpfOa5qn8qx3p/OsVkJpq+29Eu9iHYbD8A0s4FmII3knwMcKulB4ND0noi4D7gcuB/4JXByRFTqpT5J1lFgOfBH4NqUfh6wk6TlwOdJPdIabUu+1Itqt5m6bxuzj5pIW2sLAtpaW5h91ESPlzGz0qiAH/ZNqb29PRYvXtzr/Xr7bJZJcxayqkYVWltrC7fMPLjX5zczK5OkJRHRXmudR/L3UW9H1Q+kdhszsyINpCqyIcGN8WY2VDjA9DM3xpvZUOEqsn7m2ZDNbKhwgCmBZ0M2s6HAVWRmZlYIBxgzMyuEA4yZmRXCAcbMzArhAGNmZoXwVDGJpDXAn8vORy+NBp4oOxMlG+r3YKhfP/geQLn3YLeIqDkdvQNME5O0uKs5gIaKoX4Phvr1g+8BDNx74CoyMzMrhAOMmZkVwgGmuc0tOwMDwFC/B0P9+sH3AAboPXAbjJmZFcIlGDMzK4QDjJmZFcIBpslI2lXSTZIekHSfpM+UnaeySBomaamkq8vOSxkktUqaL+n36d/D28rOU3+T9Ln0/+BeSZdKGlV2noom6XxJj0u6N5e2o6QbJD2Y/u5QZh4rHGCazwbgCxHxBuBA4GRJe5Wcp7J8Bnig7EyU6JvALyPi9cCbGGL3QlIb8GmgPSL2AYYBx5Sbq34xDziiKm0mcGNE7AHcmN6XzgGmyUTE6oi4My0/S/alMuQeLiNpHHAkcG7ZeSmDpO2AvwfOA4iIFyKis9RMlWM40CJpOLA18EjJ+SlcRPwKeKoqeQpwYVq+EJjan3nqigNME5M0HtgXuK3krJThG8CXgJdKzkdZXgOsAS5I1YTnStqm7Ez1p4hYBXwdeBhYDTwTEdeXm6vS7BwRqyH7EQq8quT8AA4wTUvStsDPgM9GxF/Kzk9/kvQe4PGIWFJ2Xko0HHgL8P2I2Bf4KwOkWqS/pHaGKcAEYCywjaQPlZsry3OAaUKSRpAFl0si4oqy81OCScA/SloBXAYcLOnH5Wap33UAHRFRKb3OJws4Q8k7gYciYk1ErAeuAN5ecp7K8pikXQDS38dLzg/gANN0JIms3v2BiPjPsvNThoiYFRHjImI8WaPuwogYUr9cI+JRYKWkPVPSIcD9JWapDA8DB0raOv2/OIQh1tEh5ypgelqeDlxZYl5eNrzsDFivTQI+DNwj6a6U9pWI+EV5WbKSnAJcIukVwJ+AE0rOT7+KiNskzQfuJOtduZQBOmVKI0m6FJgMjJbUAZwGzAEul3QiWeA9urwcbuSpYszMrBCuIjMzs0I4wJiZWSEcYMzMrBAOMGZmVggHGDMzK4QDjA16yvxG0rtyaR+Q9MuS8vN6SXelKV5eW7VuhaR70vq7JH2r4Ly0F30OG7rcTdmGBEn7AP9FNnfbMOAu4IiI+OMWHGtYRLzYh7zMBFoi4rQa61aQzQ78xJYevxf5GB4RG4o+jw1dLsHYkBAR9wL/DXyZbGDaj4F/lnRHKklMgWwCUUm/lnRner09pU9Oz+H5Cdkg120kXSPpd+lZJNOqzynpzZJulXS3pJ9L2kHSu4HPAh+VdFM9eZc0POVzcno/W9KZaXmFpLMk3Z5eu6f0MZJ+lva7Q9KklH66pLmSrgcuStd1dVq3TXrWSPU9OV7SFZJ+mZ438u+5vB2R7tPvJN3Y3XFsCIoIv/waEi9gG2AZcA8wG/hQSm8F/pDWbw2MSul7AIvT8mSyCSUnpPfvA36UO/b2Nc53N3BQWj4D+EZaPh34Yhd5XJHyd1d6fS6l7002DcqhZCPWX5Hb/p/T8nHA1Wn5J8DfpeVXk00tVDn3ErISVOW6Kvv8Wxf35HiymQK2B0YBfwZ2BcYAK3P3ZMfujlP25+9X/788VYwNGRHxV0k/BZ4DPgC8V9IX0+pRZF/EjwDfkfRm4EXgdblD3B4RD6Xle4CvSzqL7Av61/lzSdoeaI2Im1PShWRVdPX4h6iqIouI+yRdTFYKe1tEvJBbfWnu7zlp+Z3AXtkUXQBsJ+mVafmqiFhb47yHkU0iWn1PIHuY1TPp2u4HdgN2AH5VuScR8VQPxxmq84QNWQ4wNtS8lF4C3hcRy/IrJZ0OPEb2hMitgHW51X+tLETEHyTtB7wbmC3p+og4o+C8TwQ6gZ2r0qPG8lZkgWiTQJICzl+prat78lbg+VzSi2TfHao6d7fHsaHHbTA2VF0HnJJm4UXSvil9e2B1RLxENqnosFo7SxoL/C0ifkz20KtNpspPv/aflvSOlPRh4Ga2kKSjgJ3InmL5LUmtudXTcn8XpeXrgU/l9n9zHafp6p50ZRFwkKQJafsdt/A4Nki5BGND1dfInop5d/oiXAG8B/ge8DNJRwM30fWv/YnA2ZJeAtYDn6yxzXTgB5K2pnezHd8kqdJL7W7g82Sz5R4SESslfQf4JhunZx8p6TayH4zHprRPA9+VdDfZ//NfAZ/o4bxd3ZOaImKNpBnAFZK2InsGyaG9PY4NXu6mbNbE+rNbs1lvuYrMzMwK4RKMmZkVwiUYMzMrhAOMmZkVwgHGzMwK4QBjZmaFcIAxM7NC/H+NkYpwCWlquwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='YearsExperience',y='Salary',style='o')\n",
    "plt.title('Years of Experience Vs Salary')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It is observed from the graph that there some linear relationship between Salary and Years of Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data preprocessing\n",
    "x = df.iloc[:,:-1].values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Regression Line on graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "# y = mx + c\n",
    "line = regressor.coef_*x+regressor.intercept_\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,line,color = 'red')                           \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the Values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40748.96184072, 122699.62295594,  64961.65717022,  63099.14214487,\n",
       "       115249.56285456, 107799.50275317])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison between the Actual salary and Predicted Salary\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.DataFrame({\"Actual Salary\" : y_test, \"predicted Salary\" : y_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It is observed that Predicted Salary varies a little from the Actual Salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing our data by giving some values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Years of experience of employee is: [[2.1]]\n",
      "Regressor model predicted Salary of employee is: [46336.50691676]\n"
     ]
    }
   ],
   "source": [
    "yearsExperience = [[2.1]]\n",
    "my_prediction = regressor.predict(yearsExperience)\n",
    "print(\"Years of experience of employee is:\",yearsExperience)\n",
    "print(\"Regressor model predicted Salary of employee is:\",my_prediction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Years of experience of employee is: [[10.0]]\n",
      "Regressor model predicted Salary of employee is: [119905.85041792]\n"
     ]
    }
   ],
   "source": [
    "yearsExperience = [[10.0]]\n",
    "my_prediction = regressor.predict(yearsExperience)\n",
    "print(\"Years of experience of employee is:\",yearsExperience)\n",
    "print(\"Regressor model predicted Salary of employee is:\",my_prediction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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