Classification Model Evaluation

This repository contains Python code for evaluating classification models using various algorithms such as Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). The evaluation is based on precision, recall, and F1 score metrics using cross-validation and testing on unseen data.

Project Overview

The project involves the following key steps:

1. Loading Data: Loading the training and testing data from CSV files.
2. Model Evaluation and Tuning:
   • Tuning the C parameter for SVM.
   • Tuning the k parameter for kNN.
   • Evaluating different configurations for MLP.
3. Model Comparison: Comparing the performance of various classifiers.
4. Metrics Calculation: Computing precision, recall, and F1 score for each classifier.

Features

• Model Tuning: Optimize hyperparameters for SVM, kNN, and MLP.
• Cross-Validation: Employ k-fold cross-validation for model evaluation.
• Model Comparison: Compare the performance of multiple classifiers.
• Metrics Calculation: Compute precision, recall, and F1 score for each classifier.

Technologies Used

• Python
• Pandas
• Matplotlib
• scikit-learn

Getting Started

Prerequisites

Ensure you have Python and the required libraries installed:

pip install pandas matplotlib scikit-learn