Data Mining Project

A comprehensive Jupyter notebook for data mining tasks, fully compatible with Google Colab.

Getting Started

Option 1: Open in Google Colab (Recommended)

Click the badge below to open the notebook directly in Google Colab:

Option 2: Download and Upload to Colab

1. Download the data_mining_project.ipynb file
2. Go to Google Colab
3. Click on "File" → "Upload notebook"
4. Select the downloaded .ipynb file

Option 3: Local Jupyter Notebook

If you have Jupyter installed locally:

# Install required packages
pip install pandas numpy matplotlib seaborn scikit-learn jupyter

# Start Jupyter
jupyter notebook

Then open data_mining_project.ipynb in your browser.

Features

The notebook includes:

• Setup and Installation: Install and import necessary libraries
• Data Loading: Multiple methods to load data (Google Drive, local upload, URL, built-in datasets)
• Exploratory Data Analysis: Visualizations, statistics, and data understanding
• Data Preprocessing: Handle missing values, encode categorical features, feature engineering
• Model Training: Train multiple machine learning models (Logistic Regression, Decision Trees, Random Forest, SVM)
• Model Evaluation: Compare models and detailed performance metrics
• Predictions: Make predictions with trained models
• Model Saving: Export models for future use

Usage

1. Open the notebook in Google Colab
2. Run cells sequentially (Shift + Enter)
3. Customize the data loading section with your own dataset
4. Adjust the target column name to match your data
5. Modify features and models as needed for your specific task

Requirements

The notebook uses common data science libraries:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

All packages are pre-installed in Google Colab.

Customization

• Data Source: Change the data loading method in Section 2
• Target Variable: Update target_col variable to match your dataset
• Models: Add or remove models in Section 5
• Features: Add custom feature engineering in Section 4
• Visualizations: Customize plots and charts throughout

Contributing

Feel free to fork this repository and submit pull requests with improvements.

License

This project is open source and available for educational purposes.