Project Description

Sports clothing is a fast-growing industry. In this project, we will use our exploratory data analysis skills to study product data for an online sports retail company.

The sports clothing and athleisure market is very large, valued at around $193 billion in 2021, with strong growth expected in the coming years. (Source: Statista Sports Apparel Market)

We will take on the role of product analysts working for an online sports clothing company. The company wants to understand how it can increase its revenue. To do this, we will explore product data including prices, reviews, descriptions, ratings, revenue, and website traffic. Our goal is to provide useful recommendations to the marketing and sales teams.

We will work with different types of data to answer important questions that help the company improve its revenue.

The data:

We have four datasets to explore:

brands.csv

Columns	Description
product_id	Unique product identifier
brand	Brand name of the product

finance.csv

Columns	Description
product_id	Unique product identifier
listing_price	Original price of the product
sale_price	Discounted price of the product
discount	Discount off the listing price (as a decimal)
revenue	Revenue generated by the product

info.csv

Columns	Description
product_name	Name of the product
product_id	Unique product identifier
description	Description of the product

reviews.csv

Columns	Description
product_id	Unique product identifier
rating	Average rating of the product
reviews	Number of reviews for the product

Using these datasets, we will analyze the relationships between product features, pricing, customer feedback, and sales performance. Our analysis will help the company find ways to increase sales and improve customer satisfaction.

# Importing libraries
import pandas as pd

# Loading the data
brands = pd.read_csv("brands.csv") 
finance = pd.read_csv("finance.csv")
info = pd.read_csv("info.csv")
reviews = pd.read_csv("reviews.csv")

# Merge the data and drop null values
merged_df = info.merge(finance, on="product_id")
merged_df = merged_df.merge(reviews, on="product_id")
merged_df = merged_df.merge(brands, on="product_id")
merged_df.dropna(inplace=True)

# final merged data
merged_df.head()

	product_name	product_id	description	listing_price	sale_price	discount	revenue	rating	reviews	brand
1	Women's adidas Originals Sleek Shoes	G27341	A modern take on adidas sport heritage, tailor...	75.99	37.99	0.5	1641.17	3.3	24.0	Adidas
2	Women's adidas Swim Puka Slippers	CM0081	These adidas Puka slippers for women's come wi...	9.99	5.99	0.4	398.93	2.6	37.0	Adidas
3	Women's adidas Sport Inspired Questar Ride Shoes	B44832	Inspired by modern tech runners, these women's...	69.99	34.99	0.5	2204.37	4.1	35.0	Adidas
4	Women's adidas Originals Taekwondo Shoes	D98205	This design is inspired by vintage Taekwondo s...	79.99	39.99	0.5	5182.70	3.5	72.0	Adidas
5	Women's adidas Sport Inspired Duramo Lite 2.0 ...	B75586	Refine your interval training in these women's...	47.99	19.20	0.6	1555.20	1.0	45.0	Adidas

What is the volume of products and average revenue for Adidas and Nike products based on listing price quartiles?

# Add price labels based on listing_price quartiles
twenty_fifth = merged_df["listing_price"].quantile(0.25)
median = merged_df["listing_price"].quantile(0.5)
seventy_fifth = merged_df["listing_price"].quantile(0.75) 
maximum = merged_df["listing_price"].max()
merged_df["price_label"] = pd.cut(merged_df["listing_price"], bins=[0, twenty_fifth, median, seventy_fifth, maximum], labels=["Budget", "Average", "Expensive", "Elite"], include_lowest=True)

# Group by brand and price_label to get volume and mean revenue
adidas_vs_nike = merged_df.groupby(["brand", "price_label"], as_index=False).agg(
    num_products=("price_label", "count"), 
    mean_revenue=("revenue", "mean")
).round(2)

adidas_vs_nike

C:\Users\newbe\AppData\Local\Temp\ipykernel_13972\2545225764.py:9: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  adidas_vs_nike = merged_df.groupby(["brand", "price_label"], as_index=False).agg(

	brand	price_label	num_products	mean_revenue
0	Adidas	Budget	574	2015.68
1	Adidas	Average	655	3035.30
2	Adidas	Expensive	759	4621.56
3	Adidas	Elite	587	8302.78
4	Nike	Budget	357	1596.33
5	Nike	Average	8	675.59
6	Nike	Expensive	47	500.56
7	Nike	Elite	130	1367.45

Do any differences exist between the word count of a product's description and its mean rating?

# Store the length of each description
merged_df["description_length"] = merged_df["description"].str.len()

# Upper description length limits
lengthes = [0, 100, 200, 300, 400, 500, 600, 700]

# Description length labels
labels = ["100", "200", "300", "400", "500", "600", "700"]

# Cut into bins
merged_df["description_length"] = pd.cut(merged_df["description_length"], bins=lengthes, labels=labels)



# Group by the bins
description_lengths = merged_df.groupby("description_length", as_index=False).agg(
    mean_rating=("rating", "mean"), 
    total_reviews=("reviews", "sum")
).round(2)

description_lengths

C:\Users\newbe\AppData\Local\Temp\ipykernel_13972\2042162190.py:16: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  description_lengths = merged_df.groupby("description_length", as_index=False).agg(

	description_length	mean_rating	total_reviews
0	100	2.26	36.0
1	200	3.19	17719.0
2	300	3.28	76115.0
3	400	3.29	28994.0
4	500	3.35	4984.0
5	600	3.12	852.0
6	700	3.65	818.0

We can answer many more questions from the dataset. Thank you!