Delete app.py

app.py

@@ -1,39 +0,0 @@
-# Simple Linear Regression
-
-# Importing the libraries
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-
-# Importing the dataset
-dataset = pd.read_csv('Salary_Data.csv')
-X = dataset.iloc[:, :-1].values
-y = dataset.iloc[:, -1].values
-
-# Splitting the dataset into the Training set and Test set
-from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)
-
-# Training the Simple Linear Regression model on the Training set
-from sklearn.linear_model import LinearRegression
-regressor = LinearRegression()
-regressor.fit(X_train, y_train)
-
-# Predicting the Test set results
-y_pred = regressor.predict(X_test)
-
-# Visualising the Training set results
-plt.scatter(X_train, y_train, color = 'red')
-plt.plot(X_train, regressor.predict(X_train), color = 'blue')
-plt.title('Salary vs Experience (Training set)')
-plt.xlabel('Years of Experience')
-plt.ylabel('Salary')
-plt.show()
-
-# Visualising the Test set results
-plt.scatter(X_test, y_test, color = 'red')
-plt.plot(X_train, regressor.predict(X_train), color = 'blue')
-plt.title('Salary vs Experience (Test set)')
-plt.xlabel('Years of Experience')
-plt.ylabel('Salary')
-plt.show()