Repository containing data science projects completed by me for academic, self learning, and hobby purposes.

• Sentiment Analysis: Feedback Analyzer:This repository contains a Python-based feedback analyzer that uses machine learning techniques to process and classify feedback into various sentiment categories, such as positive, negative, and neutral. The project is still in-progress, with ongoing exploration of different machine learning and deep learning models to enhance the accuracy and capabilities of the feedback analyzer.
• Classification: Detecting Diabetic : Model Developing: Develop ML model to detect diabetic using different features. Two data-sets (Well-known Pima Indians Diabetes Database and new data set that combined both Behaviour-based features and demographic features such as age, weight, height and blood pressure.
• Regression: Predicting Housing Price: A model to predict the house rental price using various features..Advanced regression techniques (random forest and gradient boosting) are deployed to predict the housing price.

• This repository (https://github.com/myothida/DataAnalytics_Projects.git) contains 7 different data analytics projects focused on analyzing various aspects of student performance and engagement in online courses, as well as evaluating the effectiveness of different training programs. The projects utilize machine learning techniques to derive insights from the data.

• Python Programming: This repo includes the assignments and lectures conducted in the Python Programming Course. This Course teaches you programming in general as well as Python fundamentals for data science. This course provides you knowledge and skills to create basic programs to work with real data and solve real-world problems in Python. You will gain a strong foundation for more advanced learning that requires the Python Programming knowledge.
• Supervised Machine Learing: This repo includes all the example codes and data set used in my book 'Introduction to Supervised Machine Learning'. The purpose of this book is to document my teachings at Chiang Mai University in a physical form and make it accessible for students with limited resources to learn from. The book aims to provide a comprehensive and easy-to-follow introduction to the fundamental concepts of machine learning methods. It is divided into four parts. The first chapter provides an overview of the basic questions of machine learning and introduces the Python development environment. The second chapter covers various regression methods and the third chapter discusses different classification methods. The last chapter provides recommendations for continuing the journey of learning machine learning. I believe that hands-on learning is crucial for understanding; thus, the book's explanations are accompanied by detailed ’Python code’ snippets throughout the text. The readers can follow the instructions and run the code in this Repo on their own computer or an online platform such as Google Colab.
• Introduction to Deep Learning: This repo includes all the example codes and datasets used in my book 'Introduction to Deep Learning', written in both Burmese and English. The purpose of this book is to document my teachings in a physical form and make it accessible for students with limited resources to learn from. The book aims to provide a comprehensive and easy-to-follow introduction to the fundamental concepts of deep learning methods. This book covers essential topics from the basics to the latest trends in the deep learning field. Join us on this journey to unlock the secrets of neural networks, ANN, CNN, RNN, and Generative AI.