Predicting a Startups Profit/Success Rate using Multiple Linear Regression in Python in given 50 Startups Dataset

Question

Task- Predicting a Startups Profit/Success Rate using Multiple Linear Regression in Python-Download Data Set click here.

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

Answer 1

# Multiple Linear Regression for 50 Startup

# Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

# Importing the dataset

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

X = dataset.iloc[:, :-1]

y = dataset.iloc[:, 4]

#Convert the column into categorical columns

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

# Drop the state coulmn

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

# concat 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

y_pred = regressor.predict(X_test)

from sklearn.metrics import r2_score

score=r2_score(y_test,y_pred)

Answer 2

GO_STP_6412

https://www.linkedin.com/posts/suyash-singh-9770581b6_assignmenttask8-activity-6806646909340667904-ymHY

Answer 3

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import sklearn

from sklearn.linear_model import LinearRegression

df = pd.read_csv('50_Startups.csv')

df.head()

df.info()

df.describe()

from sklearn.preprocessing import LabelEncoder

lab_enc = LabelEncoder()

df.State = lab_enc.fit_transform(df.State)

df.State.unique()

x = df.drop(['Profit'], axis=1)

print(x.head())

print(x.shape)

y = df['Profit']

print(y.head())

print(y.shape)

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 = 45)

model = LinearRegression()

model.fit(x_train, y_train)

y_pred = model.predict(x_test)

y_pred

from sklearn.metrics import r2_score

score = r2_score(y_test, y_pred )

score

0.9426922836763976

from sklearn.metrics import mean_squared_error

mse = mean_squared_error(y_test, y_pred)

mse

newdf = pd.DataFrame(y_pred, y_test)

newdf

plt.scatter(y_test, y_pred, marker = '^')

plt.show()

Answer 4

ID: GO_STP_6266

https://www.linkedin.com/posts/sahil-parmar-4099391bb_google-colaboratory-activity-6806899762210701312-Qjqz