https://github.com/sayande01/unsupervised_learning_ml

This project merges unsupervised learning with Association Rule Learning to analyze retail market basket data. By applying K-Means, DBSCAN, Apriori, Eclat, and FP-Growth algorithms, it uncovers purchasing patterns and segments customers into clusters, aiming to optimize product placement, promotions, and product development.
https://github.com/sayande01/unsupervised_learning_ml

apriori-algorithm dbscan fp-growth-algorithm k-means-clustering

Last synced: 9 months ago
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This project merges unsupervised learning with Association Rule Learning to analyze retail market basket data. By applying K-Means, DBSCAN, Apriori, Eclat, and FP-Growth algorithms, it uncovers purchasing patterns and segments customers into clusters, aiming to optimize product placement, promotions, and product development.

• Host: GitHub
• URL: https://github.com/sayande01/unsupervised_learning_ml
• Owner: sayande01
• Created: 2024-08-08T03:31:25.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-08T03:48:36.000Z (over 1 year ago)
• Last Synced: 2025-02-13T02:38:43.432Z (11 months ago)
• Topics: apriori-algorithm, dbscan, fp-growth-algorithm, k-means-clustering
• Language: Jupyter Notebook
• Homepage:
• Size: 2.24 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
### Title:

**Comprehensive Retail Market Analysis: Clustering and Association Rule Learning**

### Description:

This project combines unsupervised learning techniques with Association Rule Learning to analyze retail market basket data. By applying clustering algorithms (K-Means and DBSCAN) and association rule mining algorithms (Apriori, Eclat, and FP-Growth), the project aims to uncover customer purchasing patterns and segment customers into meaningful clusters. The goal is to provide actionable insights for optimizing product placement, promotional strategies, and product development.

### Objective:

1. **Data Loading and Preprocessing:**

   - **Import Data:** Load transaction data from a CSV file (`groceries.csv`) containing customer purchase records.

   - **Data Transformation:** Convert transaction data into a binary matrix using the `TransactionEncoder` from the `mlxtend` library. This transformation prepares the data for Association Rule Learning.

2. **Frequent Itemset Mining with Association Rule Learning:**

   - **Apriori Algorithm:** Apply the Apriori algorithm to identify frequent itemsets with a minimum support threshold of 0.05. Use Breadth-First Search to iteratively find itemsets and generate association rules.

   - **Eclat Algorithm:** Utilize the Eclat algorithm to find frequent itemsets through Depth-First Search, offering a comparison in terms of performance with Apriori.

   - **FP-Growth Algorithm:** Implement the FP-Growth algorithm to discover frequent itemsets using a Frequent Pattern Tree (FP-tree), avoiding candidate generation and improving efficiency.

3. **Clustering Analysis:**

   - **K-Means Clustering:** Apply K-Means clustering to segment customers based on their purchasing behavior. Determine the optimal number of clusters and analyze the clusters to understand customer segments.

   - **DBSCAN Clustering:** Use DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to identify clusters of varying shapes and densities. Evaluate how this algorithm complements K-Means by identifying noise and outliers.

4. **Association Rule Generation:**

   - **Generate Rules:** Extract association rules from frequent itemsets identified by the Apriori, Eclat, and FP-Growth algorithms. Assess the rules using metrics such as support, confidence, and lift to identify actionable patterns.

5. **Data Visualization and Interpretation:**

   - **Visualize Clusters:** Create visualizations to represent the clusters formed by K-Means and DBSCAN. Use scatter plots and cluster heatmaps to illustrate the customer segments and their purchasing patterns.

   - **Visualize Association Rules:** Generate visualizations to display frequent itemsets and association rules. Use bar charts and heatmaps to represent the strength of item associations and support values.

6. **Practical Applications and Recommendations:**

   - **Retail Strategies:** Recommend strategies for optimizing store layouts and product placement based on cluster analysis and association rules. For example, group related items and place them together to enhance cross-selling opportunities.

   - **Promotional Campaigns:** Suggest targeted promotional campaigns based on customer segments and frequent itemsets. Design offers or discounts that cater to specific customer clusters and their purchasing habits.

   - **Product Innovation:** Explore opportunities for creating new product bundles or combinations based on frequent itemsets and cluster profiles.

### Tools and Libraries:

- **Pandas:** For data manipulation and preprocessing.

- **Matplotlib and Seaborn:** For data visualization.

- **mlxtend:** For implementing Association Rule Learning algorithms.

- **Scikit-Learn:** For K-Means and DBSCAN clustering algorithms.

This comprehensive project description now includes both clustering and association rule learning, providing a clear overview of the techniques used and their applications in retail market analysis.