mehzabin-haque / machine_learning

GitHub repository showcasing Machine Learning code: KNN, KMeans, Random Forest, Decision Tree, Apriori, Conflict Serializable, Naive Bayes used for skin detection and UCI dataset evaluation to check accuracy. Extensively tested on reliable datasets like breast_cancer and iris, providing valuable insights for ML training and testing.

Machine Learning Algorithms

Introduction

This GitHub repository contains machine learning algorithms implemented in Python. The included algorithms cover a range of tasks, such as classification, clustering, association rule mining, and skin detection. The code is tested on reliable datasets like breast_cancer and iris, providing crucial insights and accuracy evaluation.

Algorithms Included

1. K-Nearest Neighbors (KNN): A supervised learning algorithm for classification and regression tasks based on data similarity.
2. K-Means: An unsupervised clustering algorithm for partitioning data into K clusters based on similarity.
3. Random Forest: An ensemble learning method that combines multiple decision trees for improved accuracy.
4. Decision Tree: A predictive model used for classification and regression tasks based on tree-like structures.
5. Apriori: A widely-used association rule mining algorithm for finding frequent itemsets in transaction databases.
6. Conflict Serializable: A concurrency control technique for detecting conflicts in concurrent database transactions.
7. Naive Bayes: A probabilistic algorithm used for classification based on Bayes' theorem.
8. Skin Detection: A custom implementation of a machine learning model for detecting human skin color regions.

Dataset Information and Testing

The code is trained and tested on reliable datasets like breast_cancer and iris. Users can evaluate the algorithms' performance and accuracy using these datasets. The repository provides clear instructions on running the code and interpreting the results.

Usage

Each algorithm is implemented as a separate Python script. Users can run the scripts individually or use Jupyter notebooks to explore the code, understand the implementation, and experiment with the datasets.

Contributions

Contributions to this repository are encouraged. If you would like to add more machine learning algorithms, improve existing implementations, or include additional datasets for testing, feel free to submit a pull request.