Data Mining Projects

This repository contains a collection of data mining projects completed as part of coursework. Each project focuses on a different aspect of data mining, including regression, classification, clustering, and association rule mining.

Projects

1. P1: Regression and Classification

• Techniques: Linear and Polynomial Regression, Decision Trees, Random Forest, SVM, and KNN.
• Focus: Predicting population levels using various regression and classification algorithms.

2. P2: Model Comparison

• Techniques: SVM, Bayesian Networks, Random Forest, KNN.
• Focus: Hyperparameter tuning using GridSearch and comparison of model performance metrics.

3. P3: Clustering and Association Rules

• Techniques: PCA, K-Means, DBSCAN, Association Rule Mining.
• Focus: Dimensionality reduction, clustering on multi-dimensional datasets, and discovering association rules in transaction data.

How to Use

1. Clone this repository:

   git clone https://github.com/A-Darvish/DataMining_Projects.git

2. Navigate to a project folder and open the corresponding Jupyter Notebook.
3. Run the notebooks to explore data mining techniques and results.

Dependencies

• Python 3.8+
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

Acknowledgements

Thanks to Dr.Mazlaghani for providing guidance and datasets for these projects.