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analyticsindiamagazine/LinearRegression_Practice.ipynb

Created February 24, 2020 04:20
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LinearRegression_Practice.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "6BZ-jLOunczC"
},
"outputs": [],
"source": [
"#1 Importing essential libraries\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "WKtERLr9n09B"
},
"outputs": [],
"source": [
"#2 Importing the dataset\n",
"dataset = pd.read_csv('Salary_Data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Displaying the dataset\n",
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1112bbbe0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Plotting the relation between salary and experience\n",
"dataset.plot(x='YearsExperience', y='Salary')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "G5WuUWRFn4j8"
},
"outputs": [],
"source": [
"#3 classify dependent and independent variables\n",
"X = dataset.iloc[:,:-1].values #independent variable YearsofExperience\n",
"y = dataset.iloc[:,-1].values #dependent variable salary"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "-bZ82kVbn7Ga"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Idependent Variable (Experience):\n",
"\n",
" [[ 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]]\n",
"\n",
"Dependent Variable (Salary):\n",
"\n",
" [ 39343. 46205. 37731. 43525. 39891. 56642. 60150. 54445. 64445.\n",
" 57189. 63218. 55794. 56957. 57081. 61111. 67938. 66029. 83088.\n",
" 81363. 93940. 91738. 98273. 101302. 113812. 109431. 105582. 116969.\n",
" 112635. 122391. 121872.]\n"
]
}
],
"source": [
"print(\"\\nIdependent Variable (Experience):\\n\\n\", X)\n",
"print(\"\\nDependent Variable (Salary):\\n\\n\", y)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "BFMIwI5Zn9MX"
},
"outputs": [],
"source": [
"#4 Creating training set and testing 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": 8,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "YcY06YGDn_dz"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Training Set :\n",
"----------------\n",
"\n",
"X = \n",
" [[ 2.9]\n",
" [ 5.1]\n",
" [ 3.2]\n",
" [ 4.5]\n",
" [ 8.2]\n",
" [ 6.8]\n",
" [ 1.3]\n",
" [10.5]\n",
" [ 3. ]\n",
" [ 2.2]\n",
" [ 5.9]\n",
" [ 6. ]\n",
" [ 3.7]\n",
" [ 3.2]\n",
" [ 9. ]\n",
" [ 2. ]\n",
" [ 1.1]\n",
" [ 7.1]\n",
" [ 4.9]\n",
" [ 4. ]]\n",
"y = \n",
" [ 56642. 66029. 64445. 61111. 113812. 91738. 46205. 121872. 60150.\n",
" 39891. 81363. 93940. 57189. 54445. 105582. 43525. 39343. 98273.\n",
" 67938. 56957.]\n",
"\n",
"\n",
"Test Set :\n",
"----------------\n",
"\n",
"X = \n",
" [[ 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",
"y = \n",
" [ 37731. 122391. 57081. 63218. 116969. 109431. 112635. 55794. 83088.\n",
" 101302.]\n"
]
}
],
"source": [
"print(\"\\n\\nTraining Set :\\n----------------\\n\")\n",
"print(\"X = \\n\", X_train)\n",
"print(\"y = \\n\", y_train)\n",
"\n",
"print(\"\\n\\nTest Set :\\n----------------\\n\")\n",
"print(\"X = \\n\",X_test)\n",
"print(\"y = \\n\", y_test)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "8XbYFyk8oCrH"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Predictions = [ 40835.10590871 123079.39940819 65134.55626083 63265.36777221\n",
" 115602.64545369 108125.8914992 116537.23969801 64199.96201652\n",
" 76349.68719258 100649.1375447 ]\n"
]
}
],
"source": [
"\"\"\"# II. Simple Linear Regressor \"\"\"\n",
"\n",
"#5 import SLR library\n",
"from sklearn.linear_model import LinearRegression\n",
"\n",
"#6 Train the Regressor with training set\n",
"regressor = LinearRegression()\n",
"regressor.fit(X_train, y_train)\n",
"\n",
"#7 predict the outcome of test sets\n",
"y_Pred = regressor.predict(X_test)\n",
"print(\"\\n\\nPredictions = \", y_Pred)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ZAwiJVWuoHEX"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction Accuracy = 0.9749154407708353\n",
"\n",
"Actual vs Predicted Salaries \n",
"-------------------------\n",
"\n",
"Actual :\n",
" [ 37731. 122391. 57081. 63218. 116969. 109431. 112635. 55794. 83088.\n",
" 101302.]\n",
"Predicted :\n",
" [ 40835.10590871 123079.39940819 65134.55626083 63265.36777221\n",
" 115602.64545369 108125.8914992 116537.23969801 64199.96201652\n",
" 76349.68719258 100649.1375447 ]\n"
]
}
],
"source": [
"#8 Claculating the Accuracy of the predictions\n",
"from sklearn import metrics\n",
"print(\"Prediction Accuracy = \", metrics.r2_score(y_test, y_Pred))\n",
"\n",
"#9 Comparing Actual and Predicted Salaries for he test set\n",
"print(\"\\nActual vs Predicted Salaries \\n-------------------------\\n\")\n",
"print(\"Actual :\\n \", y_test)\n",
"print(\"Predicted :\\n \", y_Pred)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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 Actual observation vs Predictions\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline \n",
"plt.scatter(X_test,y_test, s = 70, label='Actual')\n",
"plt.scatter(X_test,y_Pred, s = 90, marker = '^', label='Predicted')\n",
"plt.xlabel('Years of Experience')\n",
"plt.ylabel('Salary')\n",
"plt.legend()\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "LinearRegression.ipynb",
"provenance": []
},
"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": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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