Assessfy Industry Projects Lab Advanced 6 milestones 100 marks

Development of a Personalized Product Recommendation Engine for Indian E-commerce Platf...

Industry: Retail & E-commerce Industry: Retail & E-commerce Function: Data Analytics Type: Industry-vertical applied project Team: up to 4 Assessment: 6 milestones (100 marks)

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About this project

Development of a Personalized Product Recommendation Engine for Indian E-commerce Platforms

Objective: To design and implement a data-driven product recommendation and personalisation system to enhance user experience and sales for an Indian online retail store.

Indian online retailers face intense competition and high customer churn due to undifferentiated experiences and overwhelming product choices. Personalisation is a major challenge, especially given the diversity of consumer preferences and shopping behaviors across regions and demographics in India.

The project will involve analyzing user interaction data, transaction history, and product metadata from an e-commerce platform (such as Flipkart, Myntra, or a mid-sized Indian online retailer). The team will research suitable recommendation algorithms (collaborative filtering, content-based filtering, hybrid approaches) and implement a prototype engine using Python and open-source libraries.

Deliverables include a clean dataset, exploratory data analysis, model selection rationale, a working recommendation engine (API or dashboard), and a business impact analysis. The team will also provide metrics comparing their engine’s recommendations to existing baselines and report on scalability and integration feasibility.

The outcome will guide e-commerce managers in decision-making on personalisation strategy, expected uplift in sales/conversion, and prioritisation of technology investments to improve customer retention and average order value.

Milestones

1. Problem Definition & Business Case

10 marks 18d

Define the scope, articulate the business need for personalisation in the Indian e-commerce context, and present a project charter. Reviewed through a written proposal and oral discussion with faculty/industry mentor.

2. Domain Research & Data Gathering

12 marks 22d

Conduct secondary research on Indian e-commerce personalization practices, identify and acquire relevant datasets, and clean/preprocess data. Deliverable: research summary, data dictionary, and cleaned dataset; reviewed via documentation and Q&A.

3. Solution Design / Methodology

13 marks 16d

Select and justify recommendation algorithms (e.g., collaborative, content-based, hybrid), design system architecture, and outline evaluation metrics. Deliverable: design document and methodology presentation, peer and mentor reviewed.

4. Build / Analysis & Implementation

28 marks 36d

Develop, train, and tune the recommendation engine on Indian retail data. Include exploratory data analysis, feature engineering, and dashboard/API prototype. Deliverable: working code, technical documentation, and interim demo. Reviewed by code assessment and live demo.

5. Validation & Results

27 marks 32d

Evaluate model performance using relevant metrics (precision, recall, hit rate, NDCG), compare with baseline, and assess business impact (CTR, conversion uplift). Deliverable: results report and validation walkthrough; reviewed via critical evaluation session.

6. Final Report & Presentation

10 marks 16d

Compile comprehensive report including executive summary, methodology, results, business implications, and deployment recommendations. Present findings to faculty and industry panel. Deliverable: report and final presentation, assessed by panel for clarity and impact.

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Skills you'll learn

Retail & E-commerceData AnalyticsDomain knowledge of Indian e-commerce operations and customer behaviorData preprocessing and exploratory analysisMachine learning algorithms for recommendation systemsPython programming (pandasscikit-learnsurpriseTensorFlow/PyTorch)Data visualization and dashboarding (Power BI or Tableau)Statistical evaluation of model performanceEffective business communication and reporting

Tools used

Python (pandasscikit-learnsurpriseTensorFlow/PyTorch)Jupyter NotebookPower BI or TableauMySQL or PostgreSQL for data storage and queryingOpen-source datasets (e.g.Kaggle's E-Commerce DataUCI Online Retail Data)GitHub for code and version controlFlask or Streamlit for deployment prototype

Prerequisites

Basic statistics and probabilityIntroductory Python programmingFundamentals of machine learningUnderstanding of databases and SQL

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