steveee27/Chest-X-Ray-Lung-Disease-Classification

A Flask-based web application for classifying chest X-ray images into 15 different lung disease categories. The application includes Grad-CAM visualization to highlight areas of the X-ray that contribute to the model's predictions.

Language: Python - Size: 456 KB - Last synced at: about 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

steveee27/Hotel-Recommendation-System

This project develops a hotel recommendation system using content-based filtering. By analyzing hotel features such as room types, amenities, and pricing, it provides personalized suggestions for users. The model uses techniques like TF-IDF and evaluates its performance based on Precision@5, achieving high accuracy in recommendations.

Language: Jupyter Notebook - Size: 8.46 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Time-Series-Stock-Price-Prediction-using-LSTM-Model

This project implements a LSTM (Long Short-Term Memory) model to predict stock prices of AAPL (Apple Inc.) and AMD (Advanced Micro Devices) using historical data. The dataset includes stock prices with features like Open, High, Low, Close, Adjusted Close, and Volume. The model is trained using LSTM to learn the temporal dependencies in the data.

Language: Jupyter Notebook - Size: 1.12 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Financial-Transaction-Balance-Prediction-using-Time-Series

This project predicts financial account balances using time series forecasting and machine learning models. It trains models on transaction data to forecast future balances, using features like time of transaction and past balances.

Language: Jupyter Notebook - Size: 2.26 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Financial-Transaction-Analysis-Dashboard

This project develops an interactive Financial Transaction Analysis Dashboard to analyze customer transactions and account data. Using Tableau, it provides insights on transaction volumes, account types, balances, and trends over time. Filters for Date, Account Type, and Account Status enable detailed analysis.

Size: 2.45 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 1

steveee27/Multi-Label-Clothing-Classification-Predicting-Type-and-Color

This project focuses on creating a multi-label classification system to categorize clothing images based on type (T-shirt or Hoodie) and color (Red, Yellow, Blue, Black, White). By leveraging advanced deep learning techniques, this solution aims to streamline product categorization for fashion e-commerce platforms like Matos Fashion.

Language: Jupyter Notebook - Size: 43.9 KB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Lolify-Music-Streaming-Platform

Lolify is a music streaming platform designed to provide a personalized and interactive user experience, offering features like music recommendations, playlist management, and user analytics. This project focuses on its interface design, evaluation, and user testing to enhance user engagement and satisfaction.

Language: HTML - Size: 36.3 MB - Last synced at: 16 days ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Generative-Adversarial-Network-for-Fashion-MNIST-Image-Generation

This project explores the use of a Generative Adversarial Network (GAN) to generate fashion images from the Fashion MNIST dataset. The generator creates fake images, and the discriminator distinguishes them from real ones. Performance is evaluated using Fréchet Inception Distance (FID) to assess the quality of the generated images.

Language: Jupyter Notebook - Size: 1.01 MB - Last synced at: 4 days ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Autoencoder-for-Dimension-Reduction-in-Fashion-MNIST-Dataset

This project uses an Autoencoder for dimension reduction on the Fashion MNIST dataset, which contains grayscale clothing images. The goal is to reduce the 784-dimensional images (28x28) to a 128-dimensional latent space while reconstructing the images. The performance is evaluated using the Structural Similarity Index (SSIM).

Language: Jupyter Notebook - Size: 666 KB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Fruit-Classification-with-VGG-16-Architecture

This project classifies fruit images into four categories: Acai, Acerola, Apple, and Avocado. Using Convolutional Neural Networks (CNN), it predicts fruit types with VGG-16. The model is optimized with data augmentation, dropout, and batch normalization for better performance and accuracy.

Language: Jupyter Notebook - Size: 8.17 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Bank-Loan-Granting-Prediction-using-Backpropagation-Neural-Network

This project aims to build a model that predicts whether a loan application will be granted based on customer features such as age, income, credit history, and more. Using the Backpropagation Neural Network (BPNN) architecture, the model is trained to classify loan approvals as either "approved" or "denied."

Language: Jupyter Notebook - Size: 578 KB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Batik-Motif-Classification-Using-Deep-Learning-and-Transfer-Learning

This project involves the use of deep learning models to classify Indonesian batik motifs. By utilizing deep learning techniques such as Convolutional Neural Networks (CNNs) and Transfer Learning, this project aims to automatically identify three well-known batik motifs: Parang, Mega Mendung, and Kawung.

Language: Jupyter Notebook - Size: 26.2 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Financial-Profile-Analysis-of-Companies-Using-Clustering-Techniques

This project analyzes the financial attributes of companies from various industries and uses clustering techniques to group them into two distinct clusters. Based on the clustering results, insights and recommendations are provided to improve business strategies, focusing on financial strength, market presence, and industry representation.

Language: Jupyter Notebook - Size: 336 KB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Black-Friday-Sales-Analysis

This repository analyzes Black Friday sales data to uncover customer behavior patterns and purchasing trends. Using EDA and predictive modeling (Linear and Random Forest Regression), the project provides actionable insights to help businesses optimize sales strategies for future events.

Size: 9.92 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Bayesian-Analysis-of-Housing-Price-Prediction

This project uses Bayesian analysis to predict housing prices based on features such as square footage, number of bedrooms, bathrooms, neighborhood, and year built. It applies two models: one with uninformative Gaussian priors and another with modified priors, exploring the impact of different priors on model performance.

Size: 229 KB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Australian-Vehicle-Price-Prediction-Using-PySpark

This project aims to predict vehicle prices in the Australian market using PySpark. The dataset includes multiple features like car type, fuel type, engine type, and more. The goal is to perform data preprocessing, exploratory data analysis (EDA), and build a linear regression model to predict vehicle prices based on these features.

Language: Jupyter Notebook - Size: 1.29 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Medical-Test-Results-Classification-using-Ensemble-Learning

This project uses a synthetic healthcare dataset to predict patient test results ("Normal," "Abnormal," or "Inconclusive") using machine learning. It employs ensemble methods such as Bagging, Random Forest, Boosting, and Stacking classifiers with feature engineering and preprocessing.

Language: Jupyter Notebook - Size: 1.22 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Employee-Mental-Health-Clustering-Analysis

This project analyzes employee mental health in the tech industry using clustering. The dataset includes demographics and work-related factors. K-Means clustering segments the data into two clusters, revealing patterns and offering recommendations for better mental health support in the workplace.

Language: Jupyter Notebook - Size: 843 KB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Emotion-Text-Classification-in-Social-Media-Using-Multiple-Text-Representation-and-Deep-RNN

This project explores emotion classification in social media texts using multiple text representation techniques (TF-IDF, Word2Vec, GloVe) and deep recurrent neural networks (GRU, LSTM, Bi-GRU, Bi-LSTM). The best model achieved an F1-Score of 0.9248, with Word2Vec and Bi-LSTM outperforming other combinations.

Language: Jupyter Notebook - Size: 22 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Topic-Extraction-and-Clustering-Analysis-of-YouTube-Comments-on-Digital-Transformation

This project analyzes public opinions on digital transformation in Indonesia by scraping YouTube comments. It utilizes unsupervised learning for clustering and topic extraction. Various text preprocessing methods, such as TF-IDF, Word2Vec, and LDA, are applied for deeper insights.

Language: Jupyter Notebook - Size: 573 KB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/Two-Stage-BERT-for-Sports-News-Classification-Using-LLM

This project scrapes sports news articles, classifies them using a Two-Stage BERT model with Large Language Models (LLM). The first stage distinguishes between football and non-football news, while the second classifies football articles into specific leagues like Liga Inggris, Liga Indonesia, etc.

Language: Jupyter Notebook - Size: 3.01 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

steveee27/FoodEase-RestaurantOrderingSystem

FoodEase is a web-based food ordering system that improves restaurant efficiency and enhances customer experience. Features include a digital menu, search, cart management, and AI chat assistance for personalized recommendations, aiming to reduce wait times and streamline the ordering process.

Language: TypeScript - Size: 13 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 1.77 MB - Last synced at: 6 days ago - Pushed at: 7 months ago - Stars: 1 - Forks: 0

steveee27/Bank-Subscription-Prediction-FASTAPI

A machine learning API built using FastAPI to predict customer subscription to long-term deposits based on marketing campaign data. This project preprocesses input data, trains models, and serves predictions through a RESTful API.

Language: Jupyter Notebook - Size: 1.16 MB - Last synced at: 2 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 1.75 MB - Last synced at: 3 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 5.05 MB - Last synced at: 2 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 17 MB - Last synced at: 2 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 1

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!

Language: Jupyter Notebook - Size: 3.16 MB - Last synced at: 2 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 1

steveee27/demo-repo2

Size: 1000 Bytes - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0