Welcome to my AI Projects repository! This collection includes various machine learning and artificial intelligence projects demonstrating different algorithms, techniques, and applications. Each project is implemented in Jupyter notebooks and showcases practical uses of AI in real-world scenarios.
This repository contains the following projects:
• Player's Name Prediction A project focused on predicting player names using ML techniques. This project may involve NLP techniques or other supervised learning methods.
• Adult (Full Project) An extensive analysis of the UCI Adult dataset to predict income levels based on various demographic factors. Techniques include data preprocessing, feature engineering, and model evaluation.
• Classification & Regression Projects A combination of supervised learning projects involving both classification and regression tasks. Examples include predicting house prices, categorizing images, etc.
• K-Nearest Neighbors (KNN) Project A dedicated project exploring the K-Nearest Neighbors algorithm and its applications. This project includes data preprocessing, model training, and parameter tuning.
• Linear Regression A classic approach to regression analysis focusing on predicting continuous values. This project demonstrates linear regression techniques, model evaluation metrics, and visualization of results.
• Regression & Classification (Multiple Projects) A compilation of additional regression and classification projects showcasing different datasets and approaches, each with a unique problem statement and solution.
• Support Vector Machine (SVM) Project A project based on the SVM algorithm, demonstrating its use in classification tasks with feature scaling, hyperparameter tuning, and model evaluation.
• COVID-19 Prediction using MLP-CNN An advanced project combining Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN) for predicting COVID-19 cases or outcomes using medical imaging or time-series data.
• Deep Learning - Iris and Titanic Datasets A project showcasing the use of deep learning for classification tasks on well-known datasets (Iris and Titanic). It includes techniques like data preprocessing, model building, and evaluation.
Python: Core programming language for all projects. Jupyter Notebook: Interactive notebooks for code and visualization. Machine Learning Libraries: Scikit-learn, TensorFlow, and Keras for model building and evaluation. Data Visualization: Matplotlib and Seaborn for plotting and visual analysis. Pandas & NumPy: Data manipulation and analysis.
Exploratory Data Analysis (EDA): Each project includes EDA to uncover insights and patterns in the data. Model Training & Evaluation: Projects involve training various models and evaluating their performance using metrics like accuracy, precision, recall, MAE, and MSE. Feature Engineering: Custom feature extraction techniques to improve model performance. Parameter Tuning: Hyperparameter optimization using techniques such as GridSearchCV and RandomizedSearchCV.
Add more advanced deep learning models. Enhance data preprocessing techniques for better performance. Explore additional datasets for broader applications.
Contributions are welcome! If you have improvements, bug fixes, or new project ideas, feel free to submit a pull request.