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Day2_Simple_Linear_Regression

tangr206 / 1011人閱讀

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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