Linear Regression Prediction of future values in Python using Scatter Plot - Machine Learning

We are Importing Necessary Libraries for this project

 import pandas as pandas 

import numpy as numpy 

import matplotlib.pyplot as pyplot 

from sklearn.linear_model import LinearRegression

Importing Data using Pandas library and creating table dataframe

data = pandas.read_csv('python_linear_regression_prediction_python_data.csv') 

print(data)

Assigning Input / Independent columns to X variable and Assigning Output / Dependent Column to Y Variable

x = data[['Experience_Years','Calls_Made','Meetings_Conducted']] 

y = data['Sales_Amount']

Asking Linear Regression Model to learn data from X and Y variables

model = LinearRegression()

model.fit(x,y)

Printing Coefficient and Intercept to check their values

print("Coefficient:",model.coef_) 

print("Intercept",model.intercept_)

Adding New Inputs / Independent Values to predict Output / Dependent values

new_data = pandas.DataFrame({ 'Experience_Years': [8, 12, 15], 'Calls_Made': [105, 150, 180], 'Meetings_Conducted': [10, 14, 18]})

Predicting the Output for new Inputs using Model

Predicted_y = model.predict(new_data) 

print(Predicted_y)

Showing both the Original Input and Output as well as New Input and predicted output, in a Scatter plot graph

pyplot.figure(figsize=(15,8)) 

pyplot.scatter(data['Experience_Years'],data['Sales_Amount'], color='blue', label='Actual Sales') 

pyplot.scatter(new_data['Experience_Years'],Predicted_y,color='red',label='Predicted Sales') 

pyplot.legend() 

pyplot.xlabel('Experience Years') 

pyplot.ylabel('Sales Amount') 

pyplot.show()