Predicting Player Goals using Multiple Linear Regression Using Python

MultipleLinearRegression.py

import numpy as np
import matplotlib.pyplot as plt 
import pandas as pd



dataset = pd.read_csv('/Users/tharunpeddisetty/Desktop/PlayerStatsBasketball.csv') #Please provided your file path


X = dataset.iloc[:,:-1].values
Y = dataset.iloc[:,-1].values


#Splitting data into training and testing set
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test =  train_test_split(X,Y,test_size=0.2,random_state=0) 


#Training the model
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)
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(y_pred),1)),1))#.reshape is to display the vector vertical instead of default horizontal. axis =0 = vertical cat, axis =1 = horizontal cat :

PlayerStatsBasketball.csv

This python file covers the implementation of Multiple Linear Regression. It is used to predict the Profits of 50 StartUps based on their RnD spent, Administration cost, Marketing and State.

Data File Reference: The official NBA basketball Encyclopedia, Villard Books