Data Science Project - Full Depth analysis AND Prediction Using LogisticRegression and GBM using Balancing techniques like Class_Weight and ADASYN

This repository shows all of my Jupyter Notebook or Google CoLab projects. Through the Promoting Inclusivity in Computing program at San Francisco State, I have learned many coding skills. These include, Interdisciplinary Python Biological Applications, Data Structures, and now Machine Learning.

Welcome to the Python workshop repository. This repository includes the source codes that were taught in the workshop held at Shiraz University to familiarize PhD students with Python, important libraries and basics of machine learning concepts.

Using supervised machine learning to look at credit risk.

This project leverages machine learning with Python to classify loan applicants as "healthy" (low-risk) or "high-risk." Using historical loan data, it supports financial institutions in responsible lending and risk management.

Stroke: Statistical analysis of risk factors and creation of predictive models using machine learning

Text Classification for Sentiment Analysis using Multinomial Naive Bayes in C++

The goal of this project is to build a predictive model to estimate the likelihood of a hospital readmission based on patient data. By identifying factors that contribute to readmissions, hospitals can optimize care and reduce costs associated with repeated visits.

MBTI Personality Prediction from Text Data This project leverages machine learning to predict Myers-Briggs Type Indicator (MBTI) personality types based on textual data, specifically from social media posts.

Random Forest Classification project demonstrating data preprocessing, feature engineering, and model evaluation on structured data, with insights into feature importance and accuracy metrics.

DETECTER DES FAUX BILLETS AVEC PYTHON

Multi-class confusion matrix library in Python

Neo: Hierarchical Confusion Matrix Visualization (CHI 2022)

scikit_learn

This project leverages BERT for Named Entity Recognition (NER) on a medical dataset. The notebook provides a step-by-step guide, from dataset preparation to fine-tuning and saving the trained model for medical NER tasks.

This project aims to build a model which classifies the type of an unseen image as accurate as possible, by implementing, evaluating, and comparing amongst 2 different multi-layer perceptron Neural Networks.

Built a Multinomial Naïve Bayes classifier and compared the results with the built-in classifier in Sklearn. Developed a classifier with accuracy 82%.

Twitter Sentiment Analysis using NLP , ML models , Deep Neural Networks and Transformers

This project focuses on semantic segmentation using the BDD100K dataset, a large-scale, diverse dataset for autonomous driving. The main objective is to accurately segment and identify various objects in street scenes, which is important for improving the perception capabilities of autonomous vehicles.