Question

For Question 1-4, We will load 1000_Companies.csv dataset that contains data belongs to 1000 companies such as R&D, administration and marketing spendings and location. We will use this data to build a machine learning based decision suppport system model to predict companies' profit.

Question 1: 10 Points (Load Data)

• (A) Load the "1000_Companies.csv" dataset - 5 points
• (B) Display the first and last 5 rows of this dataset - 5 points

In [ ]:

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Question 2: 15 Points (Manipulate Data)

• (A) Extract the independent (Feature Matrix) and dependent (target vector) variables. - 5 points

• (B) Encode the categorical data following the following steps:

  1) Integer Encoding - 5 points

  2) One-Hot Encoding - 5 points

In [1]:

#(A)Extract the independent (Feature Matrix) and dependent (target vector) variables.

​

​

#(B)Encode the categorical data following the following steps

##1)Integer Encoding

​

##2) One-Hot Encoding

​

Question 3: 35 Points (Modeling)

• (A) Split the dataset into the training and test sets. Hint: Use train_test_split(test_size=0.3, shuffle = False) - 5 points
• (B) Use Linear Regression Modeling to train your model (Name your model as Model1_LRM) - 5 points
• (C) Use the trained model (Model1_LRM) and the test dataset for prediction - 5 points
• (D) Calculate the accuracy of your Model1_LRM model. Hint: Use r2_score from sklearn.metrics - 5 points
• (E) Use Random Forest Regressor Modeling to train your model (Name your model Model2_RFR) - 5 points
• (F) Use the trained model(Model2_RFR) and the test dataset for prediction - 5 points
• (G) Calculate the accuracy of your Model2_RFR model. Hint: Use r2_score from sklearn.metrics - 5 points

In [2]:

#(A) Split the dataset into the training and test sets. Hint: Use train_test_split(test_size=0.3, shuffle = False)

​

#(B) Use Linear Regression Modeling to train your model (Name your model as Model1_LRM)

​

#(C) Use the trained model (Model1_LRM) and the test dataset for prediction

​

#(D) Calculate the accuracy of your Model1_LRM model. Hint: Use r2_score from sklearn.metrics

​

In [28]:

#(E) Use Random Forest Regressor Modeling to train your model (Name your model Model2_RFR)

​

#(F) Use the trained model(Model2_RFR) and the test dataset for prediction

​

#(G) Calculate the accuracy of your Model2_RFR model. Hint: Use r2_score from sklearn.metrics

​

Out[28]:

0.724613670616963

Answer 1

Importing the libraries

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

importing the dataset

• Question 1: Load Data

(A) Load the "1000_Companies.csv" dataset

(B) Display the first and last 5 rows of this dataset

# (A)
dataset = pd.read_csv('/content/profit_estimation_of_companies/1000_Companies.csv')

# (B)

print("First five rows")
dataset.head(5)

print("Last five rows")
dataset.tail(5)

Question 2: Manipulate Data

(A) Extract the independent (Feature Matrix) and dependent (target vector) variables. -

(B) Encode the categorical data following the following steps:

1) Integer Encoding -

2) One-Hot Encoding -

X = dataset.iloc[:,:-1].values #features
y = dataset.iloc[:,4].values #target

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer

# State column
labelencoder = LabelEncoder()
X[:,3] = labelencoder.fit_transform(X[:,3])
ct = ColumnTransformer([("State", OneHotEncoder(), [3])], remainder = 'passthrough')
X = ct.fit_transform(X)

Question 3: Modeling

(A) Split the dataset into the training and test sets. Hint: Use train_test_split(test_size=0.3, shuffle = False)

(B) Use Linear Regression Modeling to train your model (Name your model as Model1_LRM)

(C) Use the trained model (Model1_LRM) and the test dataset for prediction

(D) Calculate the accuracy of your Model1_LRM model. Hint: Use r2_score from sklearn.metrics

(E) Use Random Forest Regressor Modeling to train your model (Name your model Model2_RFR)

(F) Use the trained model(Model2_RFR) and the test dataset for prediction

(G) Calculate the accuracy of your Model2_RFR model. Hint: Use r2_score from sklearn.metrics

Linear Regression:

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0, shuffle=False)

#Linear Regression Modelling

from sklearn.linear_model import LinearRegression

# train model
Model1_LRM = LinearRegression()
Model1_LRM.fit(X_train,y_train)

# predict results
y_pred = Model1_LRM.predict(X_test)

# accuracy of model
from sklearn.metrics import r2_score
r2_score(y_test,y_pred)

Random Forest Regressor:

#Random Forest Regressor

# train model
from sklearn.ensemble import RandomForestRegressor
Model2_RFR = RandomForestRegressor()
Model2_RFR.fit(X_train, y_train)

# predict results
y_pred = Model2_RFR.predict(X_test)

# accuracy of model
from sklearn.metrics import r2_score
r2_score(y_test,y_pred)