Welcome to Avinash’s Multiple Linear Regression Project

Problem Statement

Multiple linear regression to predict Profit based on Administration spend, Marketing spends, R&D spend and State. Explain the trend and correlation between dependent and independent variables.

Click here to download the raw data and the python code to follow along.

The solution guide is available too.

Solution:

Importing the libraries

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

Importing the dataset

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

Exploratory Data Analysis

Understanding the data

print(dataset.head())  # top 5 rows of the data set'
print(dataset.info()) # general information about the dataset. Fill missing values.
print(dataset.describe()) # summary statistics

Grouping profit by state

Profitperstate = dataset.groupby('State')['Profit'].mean().sort_values(ascending=False)
print(Profitperstate.head())

Assigning x & y for regression

X = dataset.iloc[:, :-1] #all values from all rows  and all columns excluding the last one
y = dataset.iloc[:, 4] #all values from rows in the last column
#Convert the column into categorical columns

Heatmap Plot to understand correlations

plt.figure(figsize=( 8,8))
sns.heatmap(X.corr(),annot=True, cmap="coolwarm")
states=pd.get_dummies(X['State'],drop_first=True)

Drop the state column

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

concatenating the dummy variables

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

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 = 0.2, random_state = 0)

Fitting Multiple Linear Regression to the Training set

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

Predicting the Test set results i.e. Predicting Profit based on Administration spend, Marketing spend, R&D spend and State.

y_pred = regressor.predict(X_test)

from sklearn.metrics import r2_score
score=r2_score(y_test,y_pred) 
print(score)

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