Predicting a Startups Profit/Success Rate using Multiple Linear Regression in Python, ASSIGNMENT - 8, GO_STP_8113

Here 50 startups dataset containing 5 columns  like “R&D Spend”, “Administration”, “Marketing Spend”, “State”, “Profit”.

In this dataset first 3 columns provides you spending on Research , Administration and Marketing respectively. State indicates startup based on that state. Profit indicates how much profits earned by a startup.

Clearly, we can understand that it is a multiple linear regression problem, as the independent variables are more than one.

Prepare a prediction model for profit of 50_Startups data in Python

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

dataset = pd.read_csv('50_Startups.csv')

X = dataset.iloc[:, :-1]

y = dataset.iloc[:, 4]

states=pd.get_dummies(X['State'],drop_first=True)

X=X.drop('State',axis=1)

X=pd.concat([X,states],axis=1)

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)

from sklearn.linear_model import LinearRegression

regressor = LinearRegression()

regressor.fit(X_train, y_train)

y_pred = regressor.predict(X_test)

from sklearn.metrics import r2_score

score=r2_score(y_test,y_pred)