Intelligent Data Classifier

Overview

The Intelligent Data Classifier is a robust machine learning model using K-Nearest Neighbors (KNN) and Decision Trees to perform multi-label classification. This project demonstrates the application of fundamental algorithms developed from scratch, aimed at achieving high accuracy in complex label prediction tasks through meticulous hyperparameter tuning.

Features

• K-Nearest Neighbors (KNN): Custom implementation of the KNN algorithm, allowing for adjustable parameters such as the number of neighbors and distance metrics.
• Decision Trees: Utilizes both Powerset and MultiOutput formulations to address complex classification scenarios.
• Hyperparameter Tuning: Detailed optimization process to enhance model performance.
• Data Analysis: Extensive exploratory data analysis with visualizations to understand data distributions and relationships.

Technologies Used

• Python
• Jupyter Notebook
• NumPy
• Matplotlib
• Bash Scripting

Installation

Clone this repository:

git clone https://github.com/yourusername/intelligent-data-classifier.git

Navigate to the project directory:

cd intelligent-data-classifier

Install the required dependencies:

pip install -r requirements.txt

Usage

Run the Jupyter Notebooks to explore the dataset and model implementation:

jupyter notebook

Execute the bash script to test the model with new data:

bash eval.sh