Create Linear Regression_single feature.py

Author: Adp-797

Linear Regression_single feature.py

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+#Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+dataset=pd.read_csv("Enter the path of california dataset")
+
+print(dataset)
+x8=dataset['median_income']
+y=dataset['median_house_value']
+print(x8)
+print(y)#median_house_value
+print(x8.shape)
+print(y.shape)
+x8=np.reshape(x8.to_numpy(),(-1,1))
+y=np.reshape(y.to_numpy(),(-1,1))
+print(x8.shape)
+print(y.shape)
+
+plt.scatter(x8,y,color='green')
+plt.title("Median house value vs Median Income")
+plt.xlabel("Median Income->")
+plt.ylabel("Median house value->")
+plt.show()
+
+from sklearn.linear_model import LinearRegression
+
+regressor_1= LinearRegression()
+regressor_1.fit(x8,y)
+
+plt.scatter(x8,y,color='green')
+plt.plot(x8,regressor_1.predict(x8),color='blue')
+plt.title("Median house value vs Median Income")
+plt.xlabel("Median Income->")
+plt.ylabel("Median house value->")
+plt.show()
+
+from sklearn.model_selection import train_test_split
+
+x_train, x_test, y_train, y_test = train_test_split(x8, y, test_size = 1/3, random_state = 0)
+
+plt.scatter(x_train, y_train, color='green')
+plt.scatter(x_test, y_test, color='blue')
+plt.title("Median house value vs Median Income")
+plt.xlabel("Median Income->")
+plt.ylabel("Median house value->")
+plt.show()
+
+regressor_2 = LinearRegression()
+regressor_2.fit(x_train, y_train)
+y_pred=regressor_2.predict(x_test)
+
+plt.scatter(x_test, y_test, color='green')
+plt.scatter(x_test, y_pred, color='blue')
+plt.plot(x_test, y_pred, color='black')
+plt.title("Median house value vs Median Income")
+plt.xlabel("Median Income->")
+plt.ylabel("Median house value->")
+plt.show()
+
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error
+
+print('MSE',mean_squared_error(y,regressor_1.predict(x8)))
+print('MAE',mean_absolute_error(y,regressor_1.predict(x8)))
+print('Accuracy',regressor_2.score(x_test,y_test)*100)
+
+
+
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+