Fashion Recommendation System using Python

Recommendation Systems are one of the popular applications of Data Science for every e-commerce platform. When it comes to fashion, an e-commerce platform wants to recommend the most trending fashion to its users for higher sales and engagement. Myntra is one of the popular e-commerce platforms known for its fashion recommendations. So, if you want to know how to build a recommendation system to recommend trending fashion, this article is for you. This article will take you through how to build a fashion recommendation system using Python.

Fashion Recommendation System

A fashion recommendation system is an application that recommends the most trending fashion based on the user’s search query. For example, if a user is looking for a Kurti, the recommendation system will recommend the most trending or highly rated Kurtis on their platform.

To build a fashion recommendation system, we need a dataset based on fashion products. We collected data about Kurtis from Myntra that we can use to create a fashion recommendation system using Python.

You can download the dataset from here.

In the section below, I will take you through building a fashion recommendation system using Python.

Fashion Recommendation System using Python

Let’s start this task by importing the necessary Python libraries and the dataset:

import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from nltk.corpus import stopwords
import string

data = pd.read_csv("Myntra kurtis.csv")
print(data.head())
       Brand Name                                        Product URL  \
0  Rain & Rainbow  https://www.myntra.com/kurtis/rain--rainbow/ra...   
1        HERE&NOW  https://www.myntra.com/kurtis/herenow/herenow-...   
2           Anouk  https://www.myntra.com/kurtis/anouk/anouk-wome...   
3       Anubhutee  https://www.myntra.com/kurtis/anubhutee/anubhu...   
4           GERUA  https://www.myntra.com/kurtis/gerua/gerua-wome...   

                                               Image  Product Ratings  \
0  https://assets.myntassets.com/dpr_2,q_60,w_210...              4.2   
1  https://assets.myntassets.com/dpr_2,q_60,w_210...              4.2   
2  https://assets.myntassets.com/dpr_2,q_60,w_210...              4.2   
3  https://assets.myntassets.com/dpr_2,q_60,w_210...              4.3   
4  https://assets.myntassets.com/dpr_2,q_60,w_210...              4.2   

   Number of ratings                               Product Info  \
0                 28                  Printed Pure Cotton Kurti   
1                805       Embroidered Pure Cotton A-Line Kurti   
2               2800  Printed Pure Cotton Indigo Anarkali Kurta   
3               1100                Ethnic Motifs Printed Kurti   
4                157                Ethnic Motifs Printed Kurti   

   Selling Price   Price   Discount  
0          837.0  1395.0  (40% OFF)  
1          719.0  1799.0  (60% OFF)  
2          594.0  1699.0  (65% OFF)  
3          521.0  1739.0  (70% OFF)  
4          449.0  1499.0  (70% OFF)  

The data contains information about:

  1. brand name of the product
  2. URL of the product
  3. Image URL of the product
  4. Ratings of the product on Myntra
  5. Total number of ratings
  6. Information about the product
  7. Selling price and original price of the product
  8. and discount on the product

Let’s have a look if the dataset contains any null values or not:

print(data.isnull().sum())
Brand Name             0
Product URL            0
Image                467
Product Ratings      198
Number of ratings      0
Product Info           0
Selling Price         74
Price                 74
Discount              74
dtype: int64

The dataset has some null values, but there are 467 null values in the Image column, where the total number of rows in the data is 600. So, I will delete the Image column and move further:

data = data.drop("Image",axis=1)

Now let’s drop the null values present in the other columns in the dataset:

data = data.dropna()

Now let’s have a look at the shape of the dataset:

print(data.shape)
(364, 8)

So there are 364 rows present in the dataset after removing the null values. Now let’s move forward by looking at the brands that sell Kurtis more on Myntra:

text = " ".join(i for i in data["Brand Name"])
stopwords = set(STOPWORDS)
wordcloud = WordCloud(stopwords=stopwords, 
                      background_color="white").generate(text)
plt.figure( figsize=(15,10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
myntra Kurtis for fashion recommendation

So, brands like Anubhutee, Malhaar, Now, Tissu, and Pistaa are popular for Kurtis on Myntra. Now let’s have a look at the highest-rated Kurtis on Myntra:

highest_rated = data.sort_values(by=["Product Ratings"], 
                                 ascending=False)
highest_rated = highest_rated.head(10)
print(highest_rated[['Product Info', "Product Ratings", "Brand Name"]])
                        Product Info  Product Ratings        Brand Name
435            Mandarin Collar Kurti              5.0            INDYES
249      Floral Printed Kaftan Kurta              5.0           Sangria
448          Solid Pure Cotton Kurti              5.0           MALHAAR
308             Floral Printed Kurti              5.0           MALHAAR
538                Pure Cotton Kurti              5.0           MALHAAR
277    Women Solid Embellished Kurti              5.0          Fabindia
515     Chikankari Embroidered Kurti              5.0  PARAMOUNT CHIKAN
62       Ethnic Motifs Printed Kurti              4.9              Biba
80   Ethnic Motifs Embroidered Kurti              4.8           Sangria
450      Self Striped Straight Kurti              4.8            Saanjh

So, brands like Indyes, Sangria, Malhaar, Fabindia, Paramount Chikan, Biba, and Saanjh are selling the highest-rated Kurtis on Myntra.

Recommending Fashion Products

To recommend the trending fashion, we cannot use the content-based filtering strategy. The content-based filtering strategy is good to use when a user is looking at a fashion product, and your application wants to recommend something similar.

To recommend the trending fashion, we can find the weighted average of all the ratings and recommend the products based on the weighted average ratings. To count the weighted score of all the ratings of Kurtis, we need:

  1. mean rating (mr): the mean rating of all the products
  2. minimum ratings (m): minimum number of ratings
  3. number of ratings (n): total number of ratings of the product
  4. average ratings (a): average rating of the product

Now below is the formula to calculate the weighted score of the product ratings:

  • score = (n/(n+m) * a) + (m/(m+n) * mr)

Now let’s calculate the weighted score and recommend the most trending Kurtis on Myntra:

mr = data['Product Ratings'].mean()
m = data['Number of ratings'].quantile(0.9)
n = data['Number of ratings']
a = data['Product Ratings']
data["Score"]  = (n/(n+m) * a) + (m/(m+n) * mr)

recommendations = data.sort_values('Score', ascending=False)
print(recommendations[['Brand Name', 'Product Info',
                       'Product Ratings', 'Score', 
                       'Selling Price', 'Discount']].head(10))
         Brand Name                                       Product Info  \
48            Tissu                    Women Floral Print A-Line Kurti   
11        Anubhutee                        Ethnic Motifs Printed Kurti   
155       Anubhutee                                Women Printed Kurti   
66     YASH GALLERY                               Printed A-Line Kurti   
27        Anubhutee                       Women Printed Straight Kurti   
102          AKIMIA                      Embroidered Pure Cotton Kurti   
88            Tissu                Women Floral Printed Straight Kurti   
3         Anubhutee                        Ethnic Motifs Printed Kurti   
42   Rain & Rainbow  Women Printed Pure Cotton Pure Cotton A-Line K...   
18            GERUA                        Ethnic Motifs Printed Kurti   

     Product Ratings     Score  Selling Price   Discount  
48               4.4  4.338320          549.0  (45% OFF)  
11               4.4  4.300868          521.0  (70% OFF)  
155              4.4  4.296895          486.0  (72% OFF)  
66               4.5  4.295568          629.0  (55% OFF)  
27               4.3  4.274815          521.0  (70% OFF)  
102              4.5  4.273667          767.0  (52% OFF)  
88               4.3  4.267992          548.0  (39% OFF)  
3                4.3  4.267992          521.0  (70% OFF)  
42               4.4  4.264685          797.0  (50% OFF)  
18               4.6  4.262359          449.0  (70% OFF)  

So this is how you can create a fashion recommendation system using Python.

Summary

A fashion recommendation system is an application that recommends the most trending fashion based on the user’s search query. Myntra is one of the popular e-commerce platforms known for its fashion recommendations. I hope you liked this article on building a fashion recommendation system using Python. You can learn more about recommendation systems here. Feel free to ask valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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