https://github.com/camilajaviera91/clustering-first-approach
This code will help to understand, in a simple way, how the clustering model works. To achieve this, we will input certain variables (strings and integers), then plot them to visualize and analyze how the values are positioned.
https://github.com/camilajaviera91/clustering-first-approach
array cluster clustering dictionaries matplotlib numpy pandas python
Last synced: 3 months ago
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This code will help to understand, in a simple way, how the clustering model works. To achieve this, we will input certain variables (strings and integers), then plot them to visualize and analyze how the values are positioned.
- Host: GitHub
- URL: https://github.com/camilajaviera91/clustering-first-approach
- Owner: CamilaJaviera91
- Created: 2024-12-03T16:47:27.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-03T14:22:58.000Z (over 1 year ago)
- Last Synced: 2025-03-28T08:45:16.391Z (over 1 year ago)
- Topics: array, cluster, clustering, dictionaries, matplotlib, numpy, pandas, python
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# K-Means Clustering Project
This project demonstrates the implementation of a K-Means clustering algorithm to group data based on user-provided inputs. The clustering is visualized using a scatter plot with centroids highlighted. Below is a breakdown of the project files and instructions for use.
## Files in the Project
### 1. `cluster.py`
This script performs the K-Means clustering process, including:
- Collecting data from the user.
- Applying the K-Means algorithm to cluster the data.
- Visualizing the clustering results.
#### Key Functions and Features:
- **`get_user_input`**: Prompts the user to select columns for clustering.
- **K-Means Clustering**: Assigns data points to clusters and calculates centroids.
- **Visualization**: Plots data points colored by cluster and displays centroids.
### 2. `dataset.py`
This script provides the `collect_data` function to gather input data for clustering.
#### Data Collection:
- **Labels (`key1`)**: Names or labels for the data points (e.g., city names).
- **Numeric Variables (`key2` and `key3`)**: Numeric values for clustering (e.g., sales figures).
- Ensures all input lists have the same length before returning a structured dataset.
## How to Run the Project
1. **Prepare the Environment**:
- Install the required libraries:
```bash
pip install pandas numpy matplotlib scikit-learn
```
2. **Run the Project**:
- Execute the `cluster.py` script to start the clustering process.
```bash
python cluster.py
```
3. **Provide Inputs**:
- Enter labels (e.g., city names) and numeric data for clustering when prompted.
- Select columns for clustering from the dataset.
4. **View Results**:
- A scatter plot will be displayed, showing clustered data points and centroids.
- Labels are annotated for clarity.
## Example Usage
### Input Example
```plaintext
Collecting data for cities, k2, and key3...
--------------------------------
Enter the name of the variable that you want to cluster: city
Enter a city name (or type 'e' to finish): CityA
Enter a city name (or type 'e' to finish): CityB
Enter a city name (or type 'e' to finish): e
Enter the name of the first variable that you want analyze: tomatoes
Enter a tomatoes value (or type 'e' to finish): 120
Enter a tomatoes value (or type 'e' to finish): 90
Enter a tomatoes value (or type 'e' to finish): e
Enter the name of the second variable that you want analyze: bread
Enter a bread value (or type 'e' to finish): 60
Enter a bread value (or type 'e' to finish): 70
Enter a bread value (or type 'e' to finish): e
```
### Output
- A scatter plot showing clusters of data points based on the provided numeric variables.
- Centroids of the clusters highlighted with red markers.
## Dependencies
- Python 3.x
- Libraries:
- `pandas`
- `numpy`
- `matplotlib`
- `scikit-learn`
## Project Structure
```
.
├── cluster.py # Main script for clustering and visualization
├── dataset.py # Data collection script
├── README.md # Project documentation (this file)
```
## Notes
- Ensure the data entered is consistent and numeric where required.
- K-Means clustering assumes that data is numeric and uses Euclidean distance for clustering.
- Modify the number of clusters (`n_clusters`) in the K-Means algorithm as needed.