Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.

ML Project implementing decision trees, boosting and svm classification from scratch.

This repo contains exploratory data analysis and modeling code for employee churn prediction system.

Machine learning library for classification tasks

The objective of this project is to showcase the use of Machine Learning models to answer the question of loan default prediction based on certain parameters from the German bank dataset.

Integrative Biomechanical and Clinical Features Predict In-Hospital Trauma Mortality

Road Traffic Accident Severity: A Comparative Analysis

Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.

Built machine learning algorithms to best predict the loan approval based on the customer past data and demographics.

This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records.

Spam Email Detection using Machine Learning Classifier Algorithms

Python project for Banknotes Analysis.

Built machine learning algorithms (Decision Tree Classifier, Random Forest Classifier & Support Vector Classifier) to best predict the credit card approval.

To create a system that effectively detects and prevents credit card fraud using machine learning techniques, ensuring the security of financial transactions and protecting customers from fraudulent activities.

Data Science - Support Vector Machine Work

Machine learning to predict which passengers survived the Titanic shipwreck

This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).

Diabetes Predictor Web App Predict diabetes in patients using classification models such as Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machines. It is deployed in a Flask web application on AWS Elastic Beanstalk.