ziadahmed10 / e-commerce-customer-churn-analysis

Figuring out customer's behaviour in order to identify the key indicators of customer's churn status, Moreover, Build a classification model that predicts whether the customer would churn or otherwise.

E-Commerce-Churn-Analysis

Figuring out customer's behaviour in order to identify the key indicators of customer's churn status, Moreover, Build a classification model that predicts whether the customer would churn or otherwise.

Tech Stacks:

• Python
• Pandas (Library)
• Numpy (Library)
• Matplotlib.pyplot (Library)
• Seaborn (Library)
• Sklearn (Library)
• Excel (Dashboard and Analysis)
• PowerBI (Dashboard and Analysis)

Machine Learning Models:

• Decision Tree
• Random Forest Classifier

Contributors:

• Moustafa Ahmed (Email, GitHub)
• Anas Khaled (Email, GitHub)
• Ziad Ahmed (Email, GitHub)

Dataset:

You can download the dataset from the repo files or by clicking Here

Column	Description
CustomerID	Unique customer ID
Churn	Churn Flag
Tenure	Tenure of customer in organization
PreferredLoginDevice	Preferred login device of customer
CityTier	City tier
WarehouseToHome	Distance in between warehouse to home of customer
PreferredPaymentMode	Preferred payment method of customer
Gender	Gender of customer
HourSpendOnApp	Number of hours spend on mobile application or website
NumberOfDeviceRegistered	Total number of deceives is registered on particular customer
PreferedOrderCat	Preferred order category of customer in last month
SatisfactionScore	Satisfactory score of customer on service
MaritalStatus	Marital status of customer
NumberOfAddress	Total number of added added on particular customer
Complain	Any complaint has been raised in last month
OrderAmountHikeFromlastYear	OrderAmountHikeFromlastYear
CouponUsed	Total number of coupon has been used in last month
OrderCount	Total number of orders has been places in last month
DaySinceLastOrder	Day Since last order by customer
CashbackAmount	Average cashback in last month