KMeansForEtsy

This is a unsupervised learning algorithm to group different types of Etsy accounts. This project is a Python-based tool designed for analyzing and clustering data using

KMeans Clustering. The tool supports data loading, preprocessing, clustering, and visualization, providing an end-to-end solution for cluster analysis.

---

Features

1. Configurable File Paths:

   • File paths are saved in a config.json file, enabling persistence across runs.
   • No need to input file paths repeatedly; they are loaded automatically if available.

2. Data Preprocessing:

   • Handles missing data, standardizes features, and encodes categorical data for clustering readiness.

3. KMeans Clustering:

   • Identifies groupings in data using the KMeans algorithm.
   • Evaluates clustering quality with inertia and silhouette scores.

4. Visualizations:

   • Price distribution by cluster.
   • Shop age vs. reviews.
   • Favorites vs. views.
   • Category distribution.
   • Clustered bar charts.
   • Histograms by cluster for numeric fields.

---

Installation

1. Clone the repository:

   git clone <repository_url>
   

Install Dependencies

pip install -r requirements.txt

KMeans Clustering: Overview and Implementation

What is KMeans Clustering? KMeans is an unsupervised machine learning algorithm used for partitioning a dataset into distinct groups (clusters). It is particularly useful for identifying patterns and structures in data without predefined labels.

How Does KMeans Work?

Initialization:

Choose k (number of clusters). Initialize k cluster centroids randomly. Assignment:

Assign each data point to the nearest centroid based on a distance metric (e.g., Euclidean distance). Update:

Recalculate the centroids by taking the mean of all points assigned to each cluster. Iterate:

Repeat steps 2 and 3 until centroids stabilize or a maximum number of iterations is reached. Evaluating Clusters Inertia:

Measures how tightly data points are clustered around centroids. Lower inertia indicates better clustering. Silhouette Score:

Evaluates how similar a data point is to its own cluster (cohesion) compared to other clusters (separation). Score ranges from -1 to 1, with higher values indicating better-defined clusters.

Project Workflow

Load Data:

The DataLoader module reads the input CSV file. Preprocess Data:

The DataPreprocessor module handles missing values, standardization, and encoding. Evaluate Clusters:

The ClusterEvaluator module calculates inertia and silhouette scores to determine the optimal number of clusters (k). Perform Clustering:

The ClusterData module applies KMeans clustering to group the data. Save Results:

Clustered data and original data with cluster labels are saved as Excel files. Visualize Results:

The DataVisualizer module generates insightful plots for analysis.

Configuration File: config.json

The config.json file stores file paths for persistence. Example structure:

{ "input_file_path": "path/to/your/input.csv", "output_clustered_file_path": "path/to/save/clustered_data.xlsx", "output_original_with_clusters_path": "path/to/save/original_data_with_clusters.xlsx" `}

Example Visualizations

Price Distribution by Cluster:

Shows how prices are distributed across clusters. Shop Age vs. Reviews:

Highlights relationships between shop age and customer reviews. Favorites vs. Views:

Explores popularity metrics within clusters.

Dependencies

numpy pandas matplotlib scikit-learn seaborn Install these using:

pip install -r requirements.txt

Future Enhancements

Add support for hierarchical clustering. Enable dynamic determination of k using elbow or silhouette methods. Enhance visualization capabilities. Contributing Contributions are welcome! Feel free to open issues or submit pull requests.

License

This project is licensed under the MIT License. See LICENSE for more details.