This repository contains code for modeling data using various machine learning algorithms, including Support Vector Machine, Logistic Regression, K Neighbors, Decision Tree Classifier, Random Forest Algorithm, and Naive Bayes Classifier. The primary objective of this project is to compare the accuracy metrics of each algorithm to gain insights into their strengths and weaknesses.
- Modeling Data:
- Implemented machine learning models using popular algorithms.
- Utilized Python libraries such as scikit-learn for model implementation and evaluation.
- Algorithm Comparison:
- Analyzed accuracy metrics for each algorithm to understand their performance on the dataset.
- Identified strengths and weaknesses of different algorithms based on their performance metrics.