Basket Analysis and Recommendation
Select the Recommendation Option:
Market Basket Analysis
Market Basket Analysis is a data mining technique used to discover purchasing patterns by identifying frequent combinations of items in customer transactions. This implementation uses the MLxtend library's Apriori algorithm and Transaction Encoder to analyze co-occurring product categories.
Key Components:
Itemset
A collection of items that appear together in transactions. For example: {home_and_kitchen, electronics}
Transaction
A set of items purchased together in a single order, representing one customer's basket.
Association Rule
A pattern of the form X → Y, indicating that customers who buy X are likely to buy Y.
Metrics
- Support: Frequency of items appearing together
- Confidence: Probability of buying Y when X is bought
- Lift: Strength of the association between X and Y
Using MLxtend's implementation, we analyze transaction patterns to identify strong associations between product categories, helping to understand customer purchasing behavior and make data-driven recommendations.
Frequently Co-occurring Categories
Category Network Graph
Network visualization of category relationships. Node size represents category frequency, and edge thickness represents co-occurrence strength.
Category Word Cloud
Word cloud visualization showing the relative frequency of different product categories. Larger text indicates higher frequency in the dataset.
The following technology stack was used in creating the Recommendation System
Category & Rating Based Recommendations
Algorithm Type: Content-Based Filtering
Key Features:
- Product Category Matching
- Average Rating Similarity
- Price Range Consideration
- Review Count Validation
Customer-Based Collaborative Filtering
Algorithm Type: User-Based Collaborative Filtering
Key Components:
- User-Item Matrix Construction
- Cosine Similarity Calculation
- Similar User Identification
- Product Rating Analysis
Data Processing
Data Cleaning:
- Null value handling
- Duplicate removal
- Data validation
- Type standardization
Data Analysis
- Customer Purchase History Analysis
- Product Category Distribution
- Rating Pattern Analysis
- Price Range Segmentation
Technologies & Libraries
Data Processing
Numerical Computing
Machine Learning
Database
Machine Learning
Graph Theory