This project covers data analysis skills such as Data Scientist, Data Analyst, Data Engineer and also use software like python, EDA, SQL, MS-Excel, Power-BI, Tableau & Machine Learning algorithm such as Recommendation System, Natural Language processing, sentiment analysis, KNN algorithm, cluster classification, Navie Bayes algorithm, smote analysis.

1. PRESENTED BY
Mr. Gaurav Balaso Bhoite MITU22MSDS0008
Mr. Saurabh Vasant Bhosale MITU22MSDS0032
SCHOOL OF ENGINEERING & SCIENCE
DEPARTMENT OF APPLIED SCIENCE & HUMANITIES
S.Y. M.Sc. (Applied Statistics-Data Science)
Under the Guidence
Dr. Pratibha Jadhav
22MSDS321: Capstone Mini Project
On
A Comprehensive Study On Myntra Fashion
Products

2. Content:
 Introduction
 Data Description
 Objective
 Data Preprocessing
- Imputation Of Missing Values
 Exploratory Data Analysis
 Analysis Using Software
 Conclusion

3. Introduction:
 Myntra founded in 2007 by Mukesh Bansal, Ashutosh Lawania, and Vineet
Saxena as online retailer of personalized gifts.
 Myntra is India's leading online fashion retailer, offering a wide range of
products for men,women,and kids.
 Myntra was India's first e-commerce B2B fashion store and is now India's
largest fashion e-commerce platform.
 Myntra offers free shipping, COD, and easy returns and exchanges.
 Myntra promises 24-48 hour delivery of products to customers across India.

4. Data Description:
 Source: Kaggle.com
 Link: https://www.kaggle.com/datasets/manishmathias/myntra-fashion-dataset
 Myntra Fashion Clothing: December-2021 to January -2023

5.  To identify the trends and patterns in Myntra Fashion Products.
 To develop a natural language processing (NLP) models.
 To build a recommendation system that recommends complementary
fashion product to customers.
 To develop image classification model using machine learning algorithm.
 To check the performance of image classification model.
Objective:

6. Data Preprocessing:
1.Data Cleaning:
This Involves Identify and Correcting the Error or Inconsistent present in
the Data.
- Dealing With Missing Values:

7.  Imputation Of Missing Values:
Discount price(in Rs )=
𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑃𝑟𝑖𝑐𝑒 ∗(100−𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑖𝑛 % )
100
Discount (in %) = 0 then Discount price( in Rs) = 0

8. Data Visualization:
 Sales Of Brands Based on Ratings

9.  Plot Of Category Under The BrandName(Roadster)
Interpretation :- From the above Pie chart we can easily understand that Top
brand(Roadster) under Category is Western (55.46%)

10. Sales of Category Based On Count
Interpretation : From the above bar graph we can visualize that Western is the
category which is more preferable than other category.

11.  Plot of Individual Category Under The Category(Western)
Interpretation :- From the above graph we can easily understand that
Category(Western) under the Individual Category is tops (26.88%)

12.  Distribution Plot On Product Ratings:
Interpretation :- The distribution of ratings in the dataset is skewed to the right, with a
mode of 4 and a mean of 3.7. This indicates that there are more ratings to 4 than any
other rating value.

14. Analysis Using Software:
 Natural Language Processing (NLP) models:
 Natural language processing (NLP) is a field of computer science that
deals with the interaction between computers and human language.
 The goal of NLP is to understand the human language to computers,
generate, and manipulate human language in a way similar to how
humans.
 Applications: Machine translations
Text Summarization
Cluster Classification
Search engines
Customer service
Data analysis

15. 1.Extract keywords from product descriptions by using NLP
technique

16. 2.Cluster product descriptions into similar groups by using NLP
technique(K-Means).

17. Recommendation System
 A recommendation system is a type of information filtering system
that predicts what a user might be wants based on their past
behavior, interests, and preferences.
 Recommendation systems are used in a wide variety of applications,
including e-commerce, social media, and streaming services.
 There are three main types of recommendation systems:
1. Collaborative filtering
2. Content-based filtering
3. Hybrid filtering

18. 1.Build a product recommendation system based on Price, Ratings, Reviews
# Consider One Example for product recommendation :
recommended products = recommend products(Ratings=3.9,Reviews= 999, Price=1499)

19. Conclusion :
 By using exploratory data analysis we can identify the trends and patterns
present in the dataset based on sales of category and individual category,
ratings, reviews, price.
 We can conclude that NLP is technique used to extract keywords from text by
identifying the most important and relevant words and phrases based
on Tf Idf scores.
 we can conclude that K-means algorithm can be used to cluster product
descriptions into similar groups and it is used to improve the performance of
product recommendation systems
 we build a recommendation system based on rating, review, and price and the
recommendation system is accurate and relevant to the needs of customers.