Welcome to my Machine Learning repository! Here, I share various Machine Learning projects I've worked on. You can find detailed information about each project, including datasets, code, and results.
Machine Learning is a subset of artificial intelligence that involves the development of algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. It has applications in a wide range of fields, from image recognition and natural language processing to recommendation systems and healthcare.
| Project Name | Description | Kaggle | GitHub |
|---|---|---|---|
| KNN Logistic Regression for Grape Variety Classification | This project explores the implementation of KNN logistic regression for classifying grape varieties. It covers the fundamentals of the algorithm and its applications in grape variety prediction. | Kaggle | GitHub |
| K Nearest Neighbors (KNN) for Classification Fundamentals and Applications | In this project, we delve into the fundamentals of KNN for classification and explore its practical applications. The code includes examples and explanations for better understanding. | Kaggle | GitHub |
| Concrete Slump Test Regression | This project focuses on regression analysis using the concrete slump test dataset. It explores various regression techniques to predict concrete slump values based on different features. | Kaggle | GitHub |
| Exploring Linear Regression | The project provides an in-depth exploration of linear regression, covering the theory, implementation, and practical applications. It includes code examples and datasets for hands-on learning. | Kaggle | GitHub |
| Mall Shopper Segmentation Clustering Analysis | This project involves clustering analysis to segment mall shoppers based on their behavior. The code includes clustering algorithms and visualizations to understand and interpret shopper segments. | Kaggle | GitHub |
| Melbourne House Price Regression Exploration | Explore regression techniques applied to predict house prices in Melbourne. The project includes data preprocessing, model building, and evaluation, providing insights into predicting house prices. | Kaggle | GitHub |
| Multiple Linear Regression and Regression Error Metrics | Dive into multiple linear regression and understand various regression error metrics. The project includes examples and explanations to enhance your understanding of regression evaluation. | Kaggle | GitHub |
| Simple Linear Regression Supervised Model | Learn about the basics of simple linear regression through this supervised modeling project. The code includes dataset loading, model training, and evaluation steps for a hands-on experience. | Kaggle | GitHub |
| The Ultimate Guide to Multiclass Classification for Predicting Race | This comprehensive guide explores multiclass classification techniques for predicting race. The project covers dataset preparation, model selection, and evaluation strategies for accurate predictions. | Kaggle | GitHub |
| Auto Analytics: Advanced Estimation & Deployment | This project focuses on estimating car prices using various regression algorithms. Implemented models include Linear Regression, Lasso Regression, Ridge Regression, Decision Tree, Random Forest, and XGBoost (using GridSearchCV). The final models chosen were Random Forest and XGBoost, deployed live via Streamlit on AWS EC2 and the Streamlit website for interactive predictions. | Kaggle | GitHub Streamlit |
You can also check out my Kaggle profile for more Machine Learning projects and competitions: My Kaggle Profile
To explore a specific project, simply click on the project folder and you'll find the Jupyter notebooks, datasets, and documentation.
If you have any questions or would like to collaborate on a project, feel free to contact me.
Happy coding!