Website sources for Applied Machine Learning for Tabular Data

Collision Avoidance Strategies with Jetracer Pro AI Kit.

This repository includes report about implementation of ML classification models .

Some projects that I've worked on to harness the power of machine learning and data science 🙌🏻

An Interactive Web Application to classify song genres based on audio features.

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

This project predicts customer churn using machine learning. It involves data cleaning, EDA, feature engineering, and model evaluation. AdaBoostClassifier with SMOTE was optimized using GridSearchCV and validated with ROC analysis.

Based on the Udemy Course "Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]"

Explored Heart Failure Prediction Dataset and performed Classification and Clustering on the data using R.

How to do a simple end-to-end machine learning classification project using the telco churn dataset

Hybrid neural network model is protected against adversarial attacks using either adversarial training or randomization defense techniques

Comparison of different machine learning algorithms to solve a classical problem of number recognition

This aims to explore different data mining techniques, such as classification, regression, clustering, and association rule mining, using datasets available in Weka.

Detailed exploration of random forest classifiers, including data cleaning, model building, and performance evaluation on various datasets.

👏Comprehensive exploration of decision tree classifiers, including data cleaning, model building🏩, and performance evaluation on various datasets.

This project focuses on the analysis of cyberbullying tweets categorized by various cyberbullying types. Using traditional Machine Learning Models, it aims to predict cyberbullying types in new tweets and provides insightful visualizations through Streamlit.

資料科學的日常研究議題

From data preprocessing to deep learning