E-Commerce-Transactions-Dataset 📊

Objective 🎯 The goal is to explore the dataset through EDA, create predictive models, and uncover valuable insights to drive business decisions. 🚀

Dataset Overview 📂

Customers.csv:

https://drive.google.com/file/d/1bu_--mo79VdUG9oin4ybfFGRUSXAe-WE/view?usp=sharing

Products.csv :

https://drive.google.com/file/d/1IKuDizVapw-hyktwfpoAoaGtHtTNHfd0/view?usp=sharing

Transactions.csv :

https://drive.google.com/file/d/1saEqdbBB-vuk2hxoAf4TzDEsykdKlzbF/view?usp=sharing

Files Description:

Customers.csv

CustomerID: Unique identifier for each customer. CustomerName: Name of the customer. Region: Continent where the customer resides 🌍. SignupDate: Date when the customer signed up 🗓.

Products.csv

ProductID: Unique identifier for each product. ProductName: Name of the product 🛍. Category: Product category 🏷. Price: Product price in USD 💵.

Transactions.csv

TransactionID: Unique identifier for each transaction. CustomerID: ID of the customer who made the purchase 👤. ProductID: ID of the product sold 🏷. TransactionDate: Date of the transaction 🗓. Quantity: Quantity of the product purchased 🛒. TotalValue: Total value of the transaction 💰. Price: Price of the product sold 💵.

Tasks 📝

Task 1: Exploratory Data Analysis (EDA) 🔍 Conduct a thorough analysis of the dataset. Identify and present five actionable business insights 💡.

Task 2: Lookalike Model 🧑‍🤝‍🧑 Build a recommendation model to find three similar customers for each of the first 20 customers based on their profiles and transaction histories 💭.

Task 3: Customer Segmentation (Clustering) 📊 Use clustering techniques to segment customers based on profile and transaction data 🔐. Evaluate the clusters with the Davies-Bouldin Index and present visualizations 📈.