eCommerce-Transactions-Dataset-Analysis

Overview:

Provided with an eCommerce Transactions dataset consisting of three files: Customers.csv, Products.csv, and Transactions.csv.Perform exploratory data analysis (EDA), build predictive models, and derive actionable insights. This assignment will test your data analysis, machine learning, and business insight generation skills.

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:

1. 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.
2. Products.csv ○ ProductID: Unique identifier for each product. ○ ProductName: Name of the product. ○ Category: Product category. ○ Price: Product price in USD.
3. Transactions.csv ○ TransactionID: Unique identifier for each transaction. ○ CustomerID: ID of the customer who made the transaction. ○ 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.

Assignment Tasks:

Exploratory Data Analysis (EDA) and Business Insights

1. Perform EDA on the provided dataset.
2. Derive at least 5 business insights from the EDA. ○ Write these insights in short point-wise sentences (maximum 100 words per insight). Deliverables: ● A Jupyter Notebook/Python script containing your EDA code. ● A PDF report with business insights (maximum 500 words).

Lookalike Model Build a Lookalike Model that takes a user's information as input and recommends 3 similar customers based on their profile and transaction history. The model should: ● Use both customer and product information. ● Assign a similarity score to each recommended customer. Deliverables: ● Give the top 3 lookalikes with there similarity scores for the first 20 customers (CustomerID: C0001 - C0020) in Customers.csv. Form an “Lookalike.csv” which has just one map: Map<cust_id, List<cust_id, score>> ● A Jupyter Notebook/Python script explaining your model development. Evaluation Criteria: ● Model accuracy and logic. ● Quality of recommendations and similarity scores.

Customer Segmentation / Clustering Perform customer segmentation using clustering techniques. Use both profile information (from Customers.csv) and transaction information (from Transactions.csv). ● You have the flexibility to choose any clustering algorithm and any number of clusters in between(2 and 10) ● Calculate clustering metrics, including the DB Index(Evaluation will be done on this). ● Visualise your clusters using relevant plots.

Deliverables: ● A report on your clustering results, including: ○ The number of clusters formed. ○ DB Index value. ○ Other relevant clustering metrics. ● A Jupyter Notebook/Python script containing your clustering code.

Evaluation Criteria: ● Clustering logic and metrics. ● Visual representation of clusters.