Academic Projects

This repository contains a collection of academic projects focused on various topics within data science, machine learning, and artificial intelligence.

Project Overview

The projects cover a wide range of tasks and techniques, including:

Reinforcement Learning

• Actor_critics_cliff_walking: Implementation of Actor-Critic methods applied to the Cliff Walking environment.
• reinforcement_black_jack: Reinforcement learning algorithms applied to the game of Blackjack.

Natural Language Processing (NLP)

• IMDB-Movie-Review-Dataset: Analysis and processing of the IMDB Movie Review dataset, for sentiment analysis or text classification.
• news_sentiment_analysis: Sentiment analysis of news articles.

Classification

• Red-wine-quality-classifier-NN: A neural network classifier for predicting the quality of red wine.
• breast_cancer_mall_customers_dataset: Potentially combines breast cancer classification with customer segmentation analysis using the mall customers dataset.
• credit_card_fraud_detection: Methods for detecting fraudulent credit card transactions.
• german_credit_dataset: Classification tasks using the German Credit Dataset (e.g., credit risk assessment).
• iris_dataset: Classification using the classic Iris dataset.
• male_female_eye_classification: Classification of images to determine gender based on eye features.
• multiclass_classification: General multiclass classification problems.
• red_wine_quality: Classification or regression tasks related to red wine quality.

Regression

• melbourne_housing_dataset: Regression analysis to predict housing prices in Melbourne.

Computer Vision

• object-detection-hugging-face-example: Example of object detection using Hugging Face libraries.
• opencv_and_algo_pract: Practice with OpenCV and computer vision algorithms.
• opencv_pract: General practice with the OpenCV library.

Other

• alexa_skill_kit_pract: Practice with the Alexa Skills Kit for developing Alexa skills.
• wholesale_customer_dataset: Analysis of wholesale customer data, possibly for clustering or segmentation.

Organization

Each project is contained within its own directory. Inside each directory, you should find the relevant code, data, and any necessary documentation.

Usage

To use these projects, navigate to the specific project directory and follow any instructions provided within the project's README or code. You will typically need to have the necessary programming languages (e.g., Python) and libraries (e.g., scikit-learn, TensorFlow, PyTorch, OpenCV) installed.

Contributions

These projects represent academic work. Contributions are not currently being accepted.