100天机器学习算法-Day2: 线性回归

 上一篇: 100天机器学习算法-Day1

Day2: 线性回归

# modified of code from 100-Days-of-ML-Code
# day 2: linear regression

# Step 1: Data Processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

dataset = pd.read_csv('studentscores.csv')
X = dataset.iloc[:, :1].values
Y = dataset.iloc[:, 1].values

from sklearn.model_selection 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)
print('Y_pred:\n', Y_pred)

# Step 4: Visualization
# Visualising the training result
plt.scatter(X_train, Y_train, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')

# visualising the test result
plt.scatter(X_test, Y_test, color='yellow')
plt.plot(X_test, Y_pred, color='green')
plt.show()