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Capstone Project 1 1

The document provides guidelines for three capstone projects involving analyzing retail sales data, customer purchase patterns for an online retailer, and customer reviews. For each project, the report should include a problem statement, objectives, data description, preprocessing steps, algorithm choice and motivation, assumptions, model evaluation, inferences, and future possibilities. Models must be saved and submitted. The first project involves forecasting retail store sales using store and economic data. The second analyzes an online retailer's customer purchase history to find insights and customer segments. The third classifies products based on analyzing customer reviews.

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SavitaDarekar
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2K views4 pages

Capstone Project 1 1

The document provides guidelines for three capstone projects involving analyzing retail sales data, customer purchase patterns for an online retailer, and customer reviews. For each project, the report should include a problem statement, objectives, data description, preprocessing steps, algorithm choice and motivation, assumptions, model evaluation, inferences, and future possibilities. Models must be saved and submitted. The first project involves forecasting retail store sales using store and economic data. The second analyzes an online retailer's customer purchase history to find insights and customer segments. The third classifies products based on analyzing customer reviews.

Uploaded by

SavitaDarekar
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Capstone Project

support@intellipaat.com

+91-7022374614

US: 1-800-216-8930 (Toll-Free)


Submission Guidelines:
For the following problem statements, follow the guidelines mentioned below:
1. Create a project report in a pdf format that will consist of the following:
a. Problem Statement
b. Project Objective
c. Data Description
d. Data Pre-processing Steps and Inspiration
e. Choosing the Algorithm for the Project
f. Motivation and Reasons For Choosing the Algorithm
g. Assumptions
h. Model Evaluation and Techniques
i. Inferences from the Same
j. Future Possibilities of the Project
2. Save your model for each project and provide a copy of the same during
submission.

Problem Statement 1:
A retail store that has multiple outlets across the country are facing issues in managing the
inventory - to match the demand with respect to supply. You are a data scientist, who has to
come up with useful insights using the data and make prediction models to forecast the sales for
X number of months/years.

Dataset Information:
The walmart.csv contains 6435 rows and 8 columns.

Feature Name Description

Store Store number

Date Week of Sales

Weekly_Sales Sales for the given store in that week

Holiday_Flag If it is a holiday week

Temperature Temperature on the day of the sale

Fuel_Price Cost of the fuel in the region

CPI Consumer Price Index

Unemployment Unemployment Rate


1. Using the above data, come up with useful insights that can be used by each of
the stores to improve in various areas.
2. Forecast the sales for each store for the next 12 weeks.

Problem Statement 2:
An online retail store is trying to understand the various customer purchase patterns for their
firm, you are required to give enough evidence based insights to provide the same.

Dataset Information:
The online_retail.csv contains 387961 rows and 8 columns.

Feature Name Description

Invoice Invoice number

StockCode Product ID

Description Product Description

Quantity Quantity of the product

InvoiceDate Date of the invoice

Price Price of the product per unit

CustomerID Customer ID

Country Region of Purchase

1. Using the above data, find useful insights about the customer purchasing history
that can be an added advantage for the online retailer.
2. Segment the customers based on their purchasing behavior.

Problem Statement 3:
You are working in an e-commerce company, and your company has put forward a task to
analyze the customer reviews for various products. You are supposed to create a report that
classifies the products based on the customer reviews.

Dataset Information:
The Reviews.csv dataset contains 60145 rows and 10 columns.
Feature Name Description

Id Record ID

ProductId Product ID

UserId User ID who posted the review

ProfileName Profile name of the User

HelpfullnessNumerator Numerator of the helpfulness of the review

HelpfullnessDenominator Denominator of the helpfulness of the review

Score Product Rating

Time Review time in timestamp

Summary Summary of the review

Text Actual text of the review

1. Find various trends and patterns in the reviews data, create useful insights that
best describe the product quality.

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