Exploratory Data Analysis on Shopping Trends in Python

Data Analysis Project by Anushka Sharma, Team edSlash.

Customer Shopping Trends

Customer Shopping Trends¶

About Dataset¶

This dataset captures detailed information about customer transactions, providing a comprehensive view of purchasing behavior across various demographics, regions, and product categories. It includes features such as customer demographics (age, gender, location), product details (item purchased, category, size, color, and season), and transaction specifics (purchase amount, payment method, shipping type, and discounts applied).

Data Dictionary¶

Customer ID: Unique identifier assigned to each customer.
Age: The age of the customer (in years).
Gender: The gender of the customer (e.g., Male, Female).
Item Purchased: The specific product bought by the customer (e.g., Blouse, Sneakers, Coat).
Category: The category the purchased item belongs to (e.g., Clothing, Footwear, Accessories, Outerwear).
Purchase Amount (USD): The amount spent by the customer on the purchase (in USD).
Location: The customer's geographical location (e.g., State or region).
Size: The size of the purchased item (e.g., S, M, L, XL).
Color: The color of the purchased item (e.g., Gray, Maroon, White).
Season The season associated with the purchase (e.g., Winter, Spring, Summer, Fall).
Review Rating: The rating provided for the purchased item, typically on a scale (e.g., 1-5).
Subscription Status: Indicates if the customer has an active subscription (e.g., Yes, No).
Payment Method: The mode of payment used for the transaction (e.g., Credit Card, PayPal, Cash, Venmo).
Shipping Type: The shipping option selected by the customer (e.g., Free Shipping, Express, Next Day Air).
Discount Applied: Indicates whether a discount was applied to the purchase (e.g., Yes, No).
Promo Code Used: Indicates whether a promotional code was used during the transaction (e.g., Yes, No).
Previous Purchases: The number of prior purchases made by the customer.
Preferred Payment Method: The payment method most frequently used by the customer (e.g., Credit Card, Venmo, Cash).
Frequency of Purchases: The customer's purchase frequency (e.g., Weekly, Fortnightly, Monthly, Quarterly, Annually).

Installing Dependencies¶

👉Ignore it if already installed

!pip install numpy
!pip install pandas
!pip install matplotlib
!pip install seaborn

In [1]:

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

1. Data Understanding¶

Display the dataset's summary statistics.
Check for missing values and handle them if present.
Determine the data types of each column.

In [2]:

df = pd.read_csv(r"C:\Users\harsh\Downloads\shopping_trends.csv")
df.head()

Out[2]:

	Customer ID	Age	Gender	Item Purchased	Category	Purchase Amount (USD)	Location	Size	Color	Season	Review Rating	Subscription Status	Payment Method	Shipping Type	Discount Applied	Promo Code Used	Previous Purchases	Preferred Payment Method	Frequency of Purchases
0	1	55	Male	Blouse	Clothing	53	Kentucky	L	Gray	Winter	3.1	Yes	Credit Card	Express	Yes	Yes	14	Venmo	Fortnightly
1	2	19	Male	Sweater	Clothing	64	Maine	L	Maroon	Winter	3.1	Yes	Bank Transfer	Express	Yes	Yes	2	Cash	Fortnightly
2	3	50	Male	Jeans	Clothing	73	Massachusetts	S	Maroon	Spring	3.1	Yes	Cash	Free Shipping	Yes	Yes	23	Credit Card	Weekly
3	4	21	Male	Sandals	Footwear	90	Rhode Island	M	Maroon	Spring	3.5	Yes	PayPal	Next Day Air	Yes	Yes	49	PayPal	Weekly
4	5	45	Male	Blouse	Clothing	49	Oregon	M	Turquoise	Spring	2.7	Yes	Cash	Free Shipping	Yes	Yes	31	PayPal	Annually

In [3]:

df.describe()

Out[3]:

	Customer ID	Age	Purchase Amount (USD)	Review Rating	Previous Purchases
count	3900.000000	3900.000000	3900.000000	3900.000000	3900.000000
mean	1950.500000	44.068462	59.764359	3.749949	25.351538
std	1125.977353	15.207589	23.685392	0.716223	14.447125
min	1.000000	18.000000	20.000000	2.500000	1.000000
25%	975.750000	31.000000	39.000000	3.100000	13.000000
50%	1950.500000	44.000000	60.000000	3.700000	25.000000
75%	2925.250000	57.000000	81.000000	4.400000	38.000000
max	3900.000000	70.000000	100.000000	5.000000	50.000000

In [4]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3900 entries, 0 to 3899
Data columns (total 19 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Customer ID               3900 non-null   int64  
 1   Age                       3900 non-null   int64  
 2   Gender                    3900 non-null   object 
 3   Item Purchased            3900 non-null   object 
 4   Category                  3900 non-null   object 
 5   Purchase Amount (USD)     3900 non-null   int64  
 6   Location                  3900 non-null   object 
 7   Size                      3900 non-null   object 
 8   Color                     3900 non-null   object 
 9   Season                    3900 non-null   object 
 10  Review Rating             3900 non-null   float64
 11  Subscription Status       3900 non-null   object 
 12  Payment Method            3900 non-null   object 
 13  Shipping Type             3900 non-null   object 
 14  Discount Applied          3900 non-null   object 
 15  Promo Code Used           3900 non-null   object 
 16  Previous Purchases        3900 non-null   int64  
 17  Preferred Payment Method  3900 non-null   object 
 18  Frequency of Purchases    3900 non-null   object 
dtypes: float64(1), int64(4), object(14)
memory usage: 579.0+ KB

we have non null values

In [5]:

df.dtypes

Out[5]:

Customer ID                   int64
Age                           int64
Gender                       object
Item Purchased               object
Category                     object
Purchase Amount (USD)         int64
Location                     object
Size                         object
Color                        object
Season                       object
Review Rating               float64
Subscription Status          object
Payment Method               object
Shipping Type                object
Discount Applied             object
Promo Code Used              object
Previous Purchases            int64
Preferred Payment Method     object
Frequency of Purchases       object
dtype: object

2. Demographic Analysis¶

Age distribution: Visualize the age distribution of customers.
Gender distribution: Analyze the proportion of male vs. female customers.
Location analysis: Identify the locations with the highest and lowest purchase volumes.

In [6]:

sns.displot(x = df.Age, kde=True , bins= 28)

D:\Installed Softwares\Anaconda\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

Out[6]:

<seaborn.axisgrid.FacetGrid at 0x16eeeb06b50>

In [7]:

def agegrp(data):
    if data < 20 :
        return "below 20 "
    elif (data >= 20) & (data < 30):
        return "20-30"
    elif (data >= 30) & (data < 40):
        return "30-40"
    elif (data >= 40) & (data < 50):
        return "40-50"
    elif (data >= 50) & (data < 60):
        return "50-60"
    elif (data >= 60) & (data <= 70):
        return "60-70"
    else:
        return np.nan

In [8]:

d1=df.copy()

In [9]:

d1["age group"]= d1.Age.apply(agegrp)
d1

Out[9]:

	Customer ID	Age	Gender	Item Purchased	Category	Purchase Amount (USD)	Location	Size	Color	Season	Review Rating	Subscription Status	Payment Method	Shipping Type	Discount Applied	Promo Code Used	Previous Purchases	Preferred Payment Method	Frequency of Purchases	age group
0	1	55	Male	Blouse	Clothing	53	Kentucky	L	Gray	Winter	3.1	Yes	Credit Card	Express	Yes	Yes	14	Venmo	Fortnightly	50-60
1	2	19	Male	Sweater	Clothing	64	Maine	L	Maroon	Winter	3.1	Yes	Bank Transfer	Express	Yes	Yes	2	Cash	Fortnightly	below 20
2	3	50	Male	Jeans	Clothing	73	Massachusetts	S	Maroon	Spring	3.1	Yes	Cash	Free Shipping	Yes	Yes	23	Credit Card	Weekly	50-60
3	4	21	Male	Sandals	Footwear	90	Rhode Island	M	Maroon	Spring	3.5	Yes	PayPal	Next Day Air	Yes	Yes	49	PayPal	Weekly	20-30
4	5	45	Male	Blouse	Clothing	49	Oregon	M	Turquoise	Spring	2.7	Yes	Cash	Free Shipping	Yes	Yes	31	PayPal	Annually	40-50
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3895	3896	40	Female	Hoodie	Clothing	28	Virginia	L	Turquoise	Summer	4.2	No	Cash	2-Day Shipping	No	No	32	Venmo	Weekly	40-50
3896	3897	52	Female	Backpack	Accessories	49	Iowa	L	White	Spring	4.5	No	PayPal	Store Pickup	No	No	41	Bank Transfer	Bi-Weekly	50-60
3897	3898	46	Female	Belt	Accessories	33	New Jersey	L	Green	Spring	2.9	No	Credit Card	Standard	No	No	24	Venmo	Quarterly	40-50
3898	3899	44	Female	Shoes	Footwear	77	Minnesota	S	Brown	Summer	3.8	No	PayPal	Express	No	No	24	Venmo	Weekly	40-50
3899	3900	52	Female	Handbag	Accessories	81	California	M	Beige	Spring	3.1	No	Bank Transfer	Store Pickup	No	No	33	Venmo	Quarterly	50-60

3900 rows × 20 columns

In [10]:

sns.barplot(x= d1["age group"], y=d1.index)

Out[10]:

<Axes: xlabel='age group'>

no significant change in shopping pattern of each age group. thus people of all age shopped regularly .

In [11]:

sns.countplot(x=d1.Gender)

Out[11]:

<Axes: xlabel='Gender', ylabel='count'>

we see that males have purchased more than females.

In [12]:

plt.figure(figsize=(10,6))
sns.countplot(x=d1.Location)
plt.xticks(rotation=90)
plt.show()

We can see highest purchasing from "MONTANA" and lowest purchasing from Rhode Island

3. Purchase Behavior¶

Average purchase amount by category: Compare average spend across product categories (e.g., Clothing, Footwear).
Analyze the frequency of purchases and identify customers with the most frequent purchases.
Payment method trends: Identify the most and least preferred payment methods.

In [13]:

sns.countplot(x=d1.Category, hue=d1.Gender)

Out[13]:

<Axes: xlabel='Category', ylabel='count'>

highest shopped category is "Clothing"

In [14]:

meanprice=d1.groupby("Category")["Purchase Amount (USD)"].mean()
meanprice

Out[14]:

Category
Accessories    59.838710
Clothing       60.025331
Footwear       60.255426
Outerwear      57.172840
Name: Purchase Amount (USD), dtype: float64

In [15]:

totalpurchase=d1.groupby("Category")["Purchase Amount (USD)"].sum()
totalpurchase

Out[15]:

Category
Accessories     74200
Clothing       104264
Footwear        36093
Outerwear       18524
Name: Purchase Amount (USD), dtype: int64

from above, we can conclude that highest shopped category is "Clothing" with total sales of 104264 USD. but the mean sales of "footware" category is high which means more expensive items from footware category were sold with mean being 60 USD.

In [16]:

sns.countplot(x=d1["Frequency of Purchases"])
plt.xticks(rotation=35)
plt.show()

we can say that the frequency of purchase is also uniformally distributed.

In [17]:

sns.countplot(x=d1["Payment Method"])
plt.xticks(rotation=35)
plt.show()

Although the data is uniformally distributed for each payment method but we can see a slight increase in credit card purchases.

4. Product Analysis¶

Analyze the popularity of items purchased (e.g., which products are purchased the most and least).
Determine which product categories contribute the most to revenue.
Explore the impact of product size, color, and season on purchases.

In [18]:

productcount=d1["Item Purchased"].value_counts()
productcount

Out[18]:

Item Purchased
Blouse        171
Jewelry       171
Pants         171
Shirt         169
Dress         166
Sweater       164
Jacket        163
Belt          161
Sunglasses    161
Coat          161
Sandals       160
Socks         159
Skirt         158
Shorts        157
Scarf         157
Hat           154
Handbag       153
Hoodie        151
Shoes         150
T-shirt       147
Sneakers      145
Boots         144
Backpack      143
Gloves        140
Jeans         124
Name: count, dtype: int64

most selling: blouse and jewelry
least selling : jeans

In [19]:

sns.countplot(x=d1.Size)

Out[19]:

<Axes: xlabel='Size', ylabel='count'>

most selling size is M

In [20]:

sns.countplot(x=d1.Color)
plt.xticks(rotation=90)
plt.show()

In [21]:

sns.countplot(x=d1.Season)

Out[21]:

<Axes: xlabel='Season', ylabel='count'>

5. Customer Segmentation¶

Segment customers based on purchase frequency (e.g., Weekly, Fortnightly, Quarterly).
Group customers by subscription status and analyze their behavior (e.g., spending patterns, preferred products).

In [22]:

d1["Frequency of Purchases"].unique().tolist()

Out[22]:

['Fortnightly',
 'Weekly',
 'Annually',
 'Quarterly',
 'Bi-Weekly',
 'Monthly',
 'Every 3 Months']

In [23]:

fortnight=d1[d1["Frequency of Purchases"]=="Fortnightly"]
weekly=d1[d1["Frequency of Purchases"]=="Weekly"]
annually=d1[d1["Frequency of Purchases"]=="Annually"]
quarterly=d1[d1["Frequency of Purchases"]=="Quarterly"]
week2=d1[d1["Frequency of Purchases"]=="Bi-Weekly"]
monthly=d1[d1["Frequency of Purchases"]=="Monthly"]
month3=d1[d1["Frequency of Purchases"]=="Every 3 Months"]

In [24]:

def revrating(data):
    if data <= 2 :
        return "below 2"
    elif (data > 2) & (data <=3 ):
        return "2-3"
    elif (data > 3) & (data <=4):
        return "3-4"
    elif (data > 4) & (data <=5):
        return "4-5"
    else:
        return np.nan

In [25]:

d1["rating group"]= d1["Review Rating"].apply(revrating)

In [26]:

subscribed=d1[d1["Subscription Status"]=="Yes"]

In [27]:

sns.countplot(x=subscribed.Gender)

Out[27]:

<Axes: xlabel='Gender', ylabel='count'>

fom the above observation we can see that all the subscriber are MALES.

In [28]:

sns.countplot(x=subscribed["Item Purchased"])
plt.xticks(rotation=90)
plt.show()

most purchased item is sweater.

In [29]:

sns.countplot(x=subscribed["Payment Method"])
plt.xticks(rotation=45)
plt.show()

In [30]:

sns.countplot(x=subscribed["Discount Applied"])

Out[30]:

<Axes: xlabel='Discount Applied', ylabel='count'>

Discount is applied for all the subscribers.

In [31]:

sns.countplot(x=subscribed["Promo Code Used"])

Out[31]:

<Axes: xlabel='Promo Code Used', ylabel='count'>

Promo code is used for all the subscribers.

In [32]:

sns.countplot(x=subscribed["age group"])

Out[32]:

<Axes: xlabel='age group', ylabel='count'>

maximum purchases are from 50-60 years.

In [33]:

sns.countplot(x=subscribed["rating group"])

Out[33]:

<Axes: xlabel='rating group', ylabel='count'>

most of the subscriber gave 3-4 rating.

6. Review Ratings Analysis¶

Analyze the distribution of review ratings.
Explore relationships between review ratings and product categories, seasons, or colors.
Identify categories with the highest and lowest average ratings.

In [34]:

sns.displot(x=d1["Review Rating"])

D:\Installed Softwares\Anaconda\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

Out[34]:

<seaborn.axisgrid.FacetGrid at 0x16ef377c9d0>

In [35]:

revcount = d1.groupby("rating group")["Category"].value_counts().reset_index(name = "Count")
revcount

Out[35]:

	rating group	Category	Count
0	2-3	Clothing	400
1	2-3	Accessories	255
2	2-3	Footwear	123
3	2-3	Outerwear	69
4	3-4	Clothing	715
5	3-4	Accessories	507
6	3-4	Footwear	239
7	3-4	Outerwear	129
8	4-5	Clothing	622
9	4-5	Accessories	478
10	4-5	Footwear	237
11	4-5	Outerwear	126

In [36]:

revcount = d1.groupby("Category")["Review Rating"].mean()
revcount.sort_values()

Out[36]:

Category
Clothing       3.723143
Outerwear      3.746914
Accessories    3.768629
Footwear       3.790651
Name: Review Rating, dtype: float64

Analyze the impact of discounts and promo codes on purchase amounts.
Determine the proportion of customers using promo codes and discounts.
Explore correlations between discounts and purchase frequency.

In [37]:

discount_impact=d1.groupby("Discount Applied")["Purchase Amount (USD)"].mean()
discount_impact

Out[37]:

Discount Applied
No     60.130454
Yes    59.279070
Name: Purchase Amount (USD), dtype: float64

when discount is applied the average purchase amount is =59 usd and without discount it is 60 usd.

In [38]:

promo_impact=d1.groupby("Promo Code Used")["Purchase Amount (USD)"].mean()
promo_impact

Out[38]:

Promo Code Used
No     60.130454
Yes    59.279070
Name: Purchase Amount (USD), dtype: float64

In [39]:

def discountencode(data):
    if data=="Yes": return 1
    else :          return 0
    
d1["discount"]=d1["Discount Applied"].apply(discountencode)

In [40]:

mapping={
    "Weekly": 4,
    "Fortnightly": 2,
    "Monthly": 1,
    "Quarterly": 0.25,
    "Annually": 0.083
}
d1["Frequency"] = d1["Frequency of Purchases"].map(mapping)
d1

Out[40]:

	Customer ID	Age	Gender	Item Purchased	Category	Purchase Amount (USD)	Location	Size	Color	Season	...	Shipping Type	Discount Applied	Promo Code Used	Previous Purchases	Preferred Payment Method	Frequency of Purchases	age group	rating group	discount	Frequency
0	1	55	Male	Blouse	Clothing	53	Kentucky	L	Gray	Winter	...	Express	Yes	Yes	14	Venmo	Fortnightly	50-60	3-4	1	2.000
1	2	19	Male	Sweater	Clothing	64	Maine	L	Maroon	Winter	...	Express	Yes	Yes	2	Cash	Fortnightly	below 20	3-4	1	2.000
2	3	50	Male	Jeans	Clothing	73	Massachusetts	S	Maroon	Spring	...	Free Shipping	Yes	Yes	23	Credit Card	Weekly	50-60	3-4	1	4.000
3	4	21	Male	Sandals	Footwear	90	Rhode Island	M	Maroon	Spring	...	Next Day Air	Yes	Yes	49	PayPal	Weekly	20-30	3-4	1	4.000
4	5	45	Male	Blouse	Clothing	49	Oregon	M	Turquoise	Spring	...	Free Shipping	Yes	Yes	31	PayPal	Annually	40-50	2-3	1	0.083
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3895	3896	40	Female	Hoodie	Clothing	28	Virginia	L	Turquoise	Summer	...	2-Day Shipping	No	No	32	Venmo	Weekly	40-50	4-5	0	4.000
3896	3897	52	Female	Backpack	Accessories	49	Iowa	L	White	Spring	...	Store Pickup	No	No	41	Bank Transfer	Bi-Weekly	50-60	4-5	0	NaN
3897	3898	46	Female	Belt	Accessories	33	New Jersey	L	Green	Spring	...	Standard	No	No	24	Venmo	Quarterly	40-50	2-3	0	0.250
3898	3899	44	Female	Shoes	Footwear	77	Minnesota	S	Brown	Summer	...	Express	No	No	24	Venmo	Weekly	40-50	3-4	0	4.000
3899	3900	52	Female	Handbag	Accessories	81	California	M	Beige	Spring	...	Store Pickup	No	No	33	Venmo	Quarterly	50-60	3-4	0	0.250

3900 rows × 23 columns

In [41]:

correlation=d1[["discount", "Frequency"]].corr()
correlation

Out[41]:

	discount	Frequency
discount	1.000000	0.012103
Frequency	0.012103	1.000000

In [42]:

sns.heatmap(correlation, annot=True)

Out[42]:

<Axes: >

8. Shipping Analysis¶

Analyze the proportion of different shipping types.
Investigate which shipping methods are most commonly used for each product category.
Study the relationship between shipping types and review ratings.

In [43]:

sns.countplot(x=d1["Shipping Type"])
plt.xticks(rotation=45)
plt.show()

In [44]:

shipping_cat=d1.groupby(["Category", "Shipping Type"]).size()
shipping_cat.sort_values()

Out[44]:

Category     Shipping Type 
Outerwear    Standard           49
             2-Day Shipping     50
             Next Day Air       51
             Store Pickup       53
             Express            57
             Free Shipping      64
Footwear     2-Day Shipping     90
             Next Day Air       93
             Express            96
             Store Pickup       98
             Standard          100
             Free Shipping     122
Accessories  Free Shipping     195
             Express           203
             2-Day Shipping    206
             Standard          208
             Next Day Air      211
             Store Pickup      217
Clothing     2-Day Shipping    281
             Store Pickup      282
             Express           290
             Next Day Air      293
             Free Shipping     294
             Standard          297
dtype: int64

In [45]:

ship_rate=d1.groupby("Shipping Type")["Review Rating"].mean()
ship_rate.sort_values()

Out[45]:

Shipping Type
Store Pickup      3.706462
Free Shipping     3.717926
Next Day Air      3.720833
2-Day Shipping    3.761085
Express           3.776935
Standard          3.817737
Name: Review Rating, dtype: float64

9. Time-Based Analysis¶

Analyze purchase behavior across seasons (Winter, Spring, Summer, Fall).
Identify seasonal trends in product categories.

In [46]:

seasonal_behavior = d1.groupby("Season")["Purchase Amount (USD)"].mean()
seasonal_behavior

Out[46]:

Season
Fall      61.556923
Spring    58.737738
Summer    58.405236
Winter    60.357364
Name: Purchase Amount (USD), dtype: float64

In [47]:

season_cat= d1.groupby(["Season","Category"]).size()
season_cat.sort_values()

Out[47]:

Season  Category   
Summer  Outerwear       75
Winter  Outerwear       80
Spring  Outerwear       81
Fall    Outerwear       88
        Footwear       136
Winter  Footwear       140
Summer  Footwear       160
Spring  Footwear       163
        Accessories    301
Winter  Accessories    303
Summer  Accessories    312
Fall    Accessories    324
Summer  Clothing       408
Fall    Clothing       427
Winter  Clothing       448
Spring  Clothing       454
dtype: int64

In spring, customer purchased more clothing .

10. Correlation Analysis¶

Investigate correlations between numerical variables like age, purchase amount, review ratings, and previous purchases.

In [48]:

numerical_data = d1[["Age", "Purchase Amount (USD)", "Review Rating", "Previous Purchases"]]
correlation_matrix = numerical_data.corr()
correlation_matrix

Out[48]:

	Age	Purchase Amount (USD)	Review Rating	Previous Purchases
Age	1.000000	-0.010424	-0.021949	0.040445
Purchase Amount (USD)	-0.010424	1.000000	0.030776	0.008063
Review Rating	-0.021949	0.030776	1.000000	0.004229
Previous Purchases	0.040445	0.008063	0.004229	1.000000

In [49]:

sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.xticks(rotation=15)
plt.show()

Conclusion from the above analysis is that :

most of the shopping was done by males.
all the subsciption was given to males.
all the subscribed customers enjoyed discounts and promo codes.
highest shopping was from "Montana".
most shopped category was "clothing". but average purchase price of footware was more which means that the price of items in footware category is more.

Exploratory Data Analysis on Shopping Trends in Python

Customer Shopping Trends¶

About Dataset¶

Data Dictionary¶

Installing Dependencies¶

1. Data Understanding¶

2. Demographic Analysis¶

3. Purchase Behavior¶

4. Product Analysis¶

5. Customer Segmentation¶

6. Review Ratings Analysis¶

8. Shipping Analysis¶

9. Time-Based Analysis¶

10. Correlation Analysis¶

Talk to our Industry Experts for Career Counselling

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Exploratory Data Analysis on Shopping Trends in Python

Customer Shopping Trends¶

About Dataset¶

Data Dictionary¶

Installing Dependencies¶

1. Data Understanding¶

2. Demographic Analysis¶

3. Purchase Behavior¶

4. Product Analysis¶

5. Customer Segmentation¶

6. Review Ratings Analysis¶

7. Discount and Promo Code Usage¶

8. Shipping Analysis¶

9. Time-Based Analysis¶

10. Correlation Analysis¶

Talk to our Industry Experts for Career Counselling

Company

Platform

Resources

Get in touch