Create LinearRegression_Parvezi123.py

LinearRegression_Parvezi123.py

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+#Import the libraries required
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+#Importing the excel data 
+dataset = pd.read_excel('D:\MachineLearing\Machine Learning A-Z Template Folder\Part 2 - Regression\Section 4 - Simple Linear Regression\CricketChirpsVs.Temperature.xls')
+
+x = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, 1].values
+
+#Split the data into train and test dataset
+from sklearn.cross_validation import train_test_split
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=1/3,random_state=42)
+
+#Fitting Simple Linear regression data model to train data set
+from sklearn.linear_model import LinearRegression
+regressorObject=LinearRegression()
+regressorObject.fit(x_train,y_train)
+
+#predict the test set
+y_pred_test_data=regressorObject.predict(x_test)
+
+
+# Visualising the Training set results in a scatter plot
+plt.scatter(x_train, y_train, color = 'red')
+plt.plot(x_train, regressorObject.predict(x_train), color = 'blue')
+plt.title('Cricket Chirps vs Temperature (Training set)')
+plt.xlabel('Cricket Chirps (chirps/sec for the striped ground cricket) ')
+plt.ylabel('Temperature (in degrees Fahrenheit)')
+plt.show()
+
+# Visualising the test set results in a scatter plot
+plt.scatter(x_test, y_test, color = 'red')
+plt.plot(x_train, regressorObject.predict(x_train), color = 'blue')
+plt.title('Cricket Chirps vs Temperature (Test set)')
+plt.xlabel('Cricket Chirps (chirps/sec for the striped ground cricket) ')
+plt.ylabel('Temperature (in degrees Fahrenheit)')
+plt.show()