Pinned Repositories
SSL-CXR
Customer-Churn-Prediction
A machine learning project to predict customer churn for a bank using XGBoost and Random Forest models. The project includes data preprocessing, feature engineering, model training with hyperparameter tuning, and deployment using Streamlit for real-time predictions.
E-Commerce-Product-Description-Classification
Classify e-commerce product descriptions into categories (Household, Books, Electronics, Clothing & Accessories) using SVM and Random Forest models with TF-IDF and Word2Vec representations. Includes data preprocessing, hyperparameter tuning, and model evaluation for performance comparison.
Facial-Expression-Detection
This machine learning project aimed at assessing public service officers' friendliness in real-time. Using models like MobileNet and SVM, it detects emotions such as happiness or anger from facial expressions. With datasets like KDEF and RAF-DB, this tool offers an efficient way to evaluate service quality through facial recognition.
LinkedIn-Job-Market-Dashboard
The LinkedIn Job Market Dashboard provides a dynamic view of the job landscape, showcasing top-paying roles, in-demand skills, and key hiring companies. This interactive tool helps professionals make informed career decisions based on salary trends and job availability.
Money-Laundering-Detection
This project leverages machine learning models like Random Forest and XGBoost to detect suspicious financial transactions that could be linked to money laundering. Using a dataset provided by IBM, we analyze real-world financial interactions between individuals, businesses, and banks.
Multiclass-Text-Classification-of-Presidential-Campaign-Tweets
Explore the Indonesian presidential campaign of 2024 through advanced text classification. This project transforms tweets into insights on national resilience using cutting-edge machine learning models and text preprocessing techniques. Dive into the intersection of politics and data science!
Netflix-Dashboard
InsightFlix is an interactive Tableau dashboard that explores Netflix's content library, highlighting production trends, geographic distribution, top genres, and leading actors. It provides valuable insights into how Netflix has evolved to cater to global audiences and informs future content strategy.
TRISTEP-Recommendation-System
Empower your career with TRISTEP, an AI-driven platform designed to bridge the digital talent gap in Indonesia. Discover industry trends, find the perfect job, and grow your skills through personalized recommendations—all in just three simple steps.
YouTube-Comments-Scraping-Analysis
This project scrapes YouTube comments from machine learning videos in Bahasa Indonesia. It includes preprocessing, text analysis, and visualization with word clouds. Techniques like One-Hot Encoding, CountVectorizer, and TF-IDF reveal key themes for further analysis.
steveee27's Repositories
steveee27/Customer-Churn-Prediction
A machine learning project to predict customer churn for a bank using XGBoost and Random Forest models. The project includes data preprocessing, feature engineering, model training with hyperparameter tuning, and deployment using Streamlit for real-time predictions.
steveee27/E-Commerce-Product-Description-Classification
Classify e-commerce product descriptions into categories (Household, Books, Electronics, Clothing & Accessories) using SVM and Random Forest models with TF-IDF and Word2Vec representations. Includes data preprocessing, hyperparameter tuning, and model evaluation for performance comparison.
steveee27/Facial-Expression-Detection
This machine learning project aimed at assessing public service officers' friendliness in real-time. Using models like MobileNet and SVM, it detects emotions such as happiness or anger from facial expressions. With datasets like KDEF and RAF-DB, this tool offers an efficient way to evaluate service quality through facial recognition.
steveee27/LinkedIn-Job-Market-Dashboard
The LinkedIn Job Market Dashboard provides a dynamic view of the job landscape, showcasing top-paying roles, in-demand skills, and key hiring companies. This interactive tool helps professionals make informed career decisions based on salary trends and job availability.
steveee27/Money-Laundering-Detection
This project leverages machine learning models like Random Forest and XGBoost to detect suspicious financial transactions that could be linked to money laundering. Using a dataset provided by IBM, we analyze real-world financial interactions between individuals, businesses, and banks.
steveee27/Multiclass-Text-Classification-of-Presidential-Campaign-Tweets
Explore the Indonesian presidential campaign of 2024 through advanced text classification. This project transforms tweets into insights on national resilience using cutting-edge machine learning models and text preprocessing techniques. Dive into the intersection of politics and data science!
steveee27/Netflix-Dashboard
InsightFlix is an interactive Tableau dashboard that explores Netflix's content library, highlighting production trends, geographic distribution, top genres, and leading actors. It provides valuable insights into how Netflix has evolved to cater to global audiences and informs future content strategy.
steveee27/TRISTEP-Recommendation-System
Empower your career with TRISTEP, an AI-driven platform designed to bridge the digital talent gap in Indonesia. Discover industry trends, find the perfect job, and grow your skills through personalized recommendations—all in just three simple steps.
steveee27/YouTube-Comments-Scraping-Analysis
This project scrapes YouTube comments from machine learning videos in Bahasa Indonesia. It includes preprocessing, text analysis, and visualization with word clouds. Techniques like One-Hot Encoding, CountVectorizer, and TF-IDF reveal key themes for further analysis.