This project was made as a final exam report for a Data Mining course at Universitas Indonesia.

An overview of my understanding of PCA for dimensionality reduction and Logistic Regression for model training and evaluation.

This project combines meticulous data preprocessing-visualization-machine learning techniques, featuring Decision Tree, integrating SVM, Logistic Regression, K-Nearest Neighbors models. Prioritizes model interpretability-accuracy through feature selection, optimizing performance evaluation for species classification using sepal & petal features.

Implementasi Metode Logistic Regression dalam Analisis Sentimen Twitter Terhadap Perkembangan AI ChatGPT.

A collection of 8 Applied Data Science projects.

FAST Change Point Detection in R

A predictor of GPCR couplings with G-proteins/B-arrs using Transformers

This repository contains a Jupyter Notebook exploring the adult income dataset. The notebook performs Exploratory Data Analysis (EDA), including visualizations with charts and graphs. Additionally, it implements various classification models to predict income and analyzes their accuracy.

Machine Learning | Fall 2023

Multivariate Data Analysis | Spring 2023

Predicting Hepatocellular Carcinoma through Supervised Machine Learning

This code loads network data, preprocesses it, reduces dimensions with an autoencoder, and trains multiple classifiers (KNN, RF, LR, SVM) for anomaly detection.

Logistic regression analysis using machine learning

A comprehensive exploration of machine learning techniques and data science best practices applied to the UCI Heart Disease dataset. Focusing on data preprocessing, exploratory analysis, and predictive modelling to identify key factors in heart disease. Part of Big Data Management and Analytics (BDMA) program.

Machine Learning project - CMP2024 - Computer Engineering - Cairo University

Machine Learning project - CMP2024 - Computer Engineering - Cairo University

Practice Assignments for Data Science Coursework

Implementation of algorithms such as normal equations, gradient descent, stochastic gradient descent, lasso regularization and ridge regularization from scratch and done linear as well as polynomial regression analysis. Implementation of several classification algorithms from scratch i.e. not used any standard libraries like sklearn or tensorflow.

Analyze and visualize features affecting student performance