This repository contains classification model for insurance claim

implementing harry potter sorting hat using multi class logistic regression

Bank card fraud detection using machine learning. Web application using Streamlit framework

Credit Scoring Project: Perform a Weight of Evidence Logistic Regression Modelling (WoELR) to generate credit scorecard for loan approval.

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

This project analyzes tumor cell data from 550 patients using Python. It involves data cleaning, exploratory analysis, feature engineering, and machine learning to classify tumors as malignant or benign. Techniques include PCA, logistic regression, and k-fold cross-validation to ensure model accuracy and reliability.

research aimed at predicting outcomes such as: post-operation mortality, Impact of comorbidities on ICU admission after cardiac surgeries, total icu stay post emergency operation, as well as utilizing deep learning to perform CT/MRI segmentation.

Titanic Survival prediction: Titanic dataset- how many people survive and how many were Male and Female

This project includes Twitter Social Media Analytics, Facebook Graph Analysis and News API Data Analysis. See the final report for full understanding.

This project includes both Diabetes Prediction using Machine Learning Algorithms and Graph Analysis using Neo4j. Have a look at the Report for complete understanding.

Flags if the statment is true or false. Works on the news article upon which the model has been trained. Use case: User can provide varying inputs for the same news.

Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza

Classification of Sober and Intoxicated Faces using Image Analysis

Proyecto de Titulación: Cálculo de Pérdidas Esperadas basado en 3 modelos de Credit Scoring para una institución financiera del Ecuador

A collection of 8 Applied Data Science projects.

Build a Web App called AI-Powered Heart Disease Risk Assessment App

The aim of this project to predict whether the product from an e-commerce company will reach on time or not. This project also analyzes various factors that affect the delivery of the product as well as studies the customer behavior.

Built a Logistic Regression model to predict whether a person has a heart disease or not.

This project is a web application for predicting loan approval status based on various financial and personal attributes. It uses a machine learning model that I trained on historical loan data to make predictions. I built the web application using Flask for the web framework, SQLite for the database, and the pre-trained model saved with joblib.

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.