Mini Project
Data Science
Personal Finance Analytics
Statement parsing, auto-categorisation and spend forecasting
- Python
- Pandas
- Prophet
- Streamlit
- +2
₹2,299₹3,999
4.5/198 reviews
Data Science · Mini Project
Customer Segmentation with K-Means
RFM scoring and clustering to group shoppers by behaviour
1-2 weeks build
Overview
Takes the UCI Online Retail dataset of real e-commerce transactions and computes recency, frequency and monetary value for every customer. After log-scaling, k-means groups customers into segments chosen with the elbow method and silhouette score. Each segment gets a plain-language profile, such as loyal high spenders or lapsed one-timers, and a suggested marketing action.
What makes it stand up
- 01Transaction cleaning for returns, cancellations and missing IDs
- 02RFM feature engineering with quintile scores
- 03Elbow and silhouette analysis to choose the cluster count
- 04Segment profiles with suggested marketing actions
- 053D cluster plot and segment-size dashboard
Module breakdown
- 01
Cleaning
Removes cancellations and rows without a customer ID.
- 02
RFM features
Computes recency, frequency and monetary value.
- 03
Scaling
Log-transforms and standardises skewed features.
- 04
Clustering
Runs k-means and validates the cluster count.
- 05
Profiling
Names each segment and summarises its behaviour.
After this, you will be able to
- Explain what R, F and M capture about a customer
- Defend your choice of k using the elbow and silhouette plots
- Present the segments and a campaign for each
Technology stack
- Python
- Pandas
- scikit-learn
- Plotly
- Streamlit
- Level
- Mini Project
- Domain
- Data Science
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support