100天搞定机器学习——Day2

Simple Linear RegressionStep 1: Data Preprocessing

importpandasaspdimportnumpyasnpimportmatplotlib.pyplotaspltdataset=pd.read_csv('studentscores.csv')X=dataset.iloc[ : , :1].valuesY=dataset.iloc[ : ,1].valuesfromsklearn.cross_validationimporttrain_test_splitX_train, X_test, Y_train, Y_test=train_test_split( X, Y,test_size=1/4,random_state=)

Step 2: Fitting Simple Linear Regression Model to the training set

fromsklearn.linear_modelimportLinearRegressionregressor=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')