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Simple Linear Regression Step 1: Data Preprocessing
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dataset = pd.read_csv("studentscores.csv")
X = dataset.iloc[ : , : 1 ].values
Y = dataset.iloc[ : , 1 ].values
from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0)
Step 2: Fitting Simple Linear Regression Model to the training set
from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor = regressor.fit(X_train, Y_train)
# Step 3: Predecting the Result
Y_pred = regressor.predict(X_test)
# Step 4: Visualization
## Visualising the Training results
plt.scatter(X_train , Y_train, color = "red") plt.plot(X_train , regressor.predict(X_train), color ="blue")
## Visualizing the test results
plt.scatter(X_test , Y_test, color = "red") plt.plot(X_test , regressor.predict(X_test), color ="blue")
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