45 lines
1.2 KiB
Markdown
45 lines
1.2 KiB
Markdown
# Simple Linear Regression
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<p align="center">
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<img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%202.jpg">
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</p>
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# Step 1: Data Preprocessing
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```python
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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dataset = pd.read_csv('studentscores.csv')
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X = dataset.iloc[ : , : 1 ].values
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Y = dataset.iloc[ : , 1 ].values
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from sklearn.cross_validation import train_test_split
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X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0)
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```
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# Step 2: Fitting Simple Linear Regression Model to the training set
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```python
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from sklearn.linear_model import LinearRegression
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regressor = LinearRegression()
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regressor = regressor.fit(X_train, Y_train)
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```
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# Step 3: Predecting the Result
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```python
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Y_pred = regressor.predict(X_test)
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```
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# Step 4: Visualization
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## Visualising the Training results
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```python
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plt.scatter(X_train , Y_train, color = 'red')
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plt.plot(X_train , regressor.predict(X_train), color ='blue')
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```
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## Visualizing the test results
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```python
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plt.scatter(X_test , Y_test, color = 'red')
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plt.plot(X_test , regressor.predict(X_test), color ='blue')
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```
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