ghghg

 

linearregh1.py import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as seabornInstance from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics "%matplotlib inline" dataset = pd.read_csv('/Users/sbogadhi/Downloads/Weather.csv') dataset.shape dataset.describe() dataset.plot(x='MinTemp', y='MaxTemp', style='o') plt.title('MinTemp vs MaxTemp') plt.xlabel('MinTemp') plt.ylabel('MaxTemp') plt.show() plt.figure(figsize=(15,10)) plt.tight_layout() seabornInstance.distplot(dataset['MaxTemp']) X = dataset['MinTemp'].values.reshape(-1,1) y = dataset['MaxTemp'].values.reshape(-1,1) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) regressor = LinearRegression() regressor.fit(X_train, y_train) #training the algorithm #To retrieve the intercept: print(regressor.intercept_) #For retrieving the slope: print(regressor.coef_) y_pred = regressor.predict(X_test) df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()}) df df1 = df.head(25) df1.plot(kind='bar',figsize=(16,10)) plt.grid(which='major', linestyle='-', linewidth='0.5', color='green') plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') plt.show() plt.scatter(X_test, y_test,  color='gray') plt.plot(X_test, y_pred, color='red', linewidth=2) plt.show() print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred)) print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))