Traffic Accident Analysis using python machine learning

This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

Our goal was to create a ML bot that analyzes real time trading data to determine the most opportune times buy and sell stock

Utilizing machine learning to examine deforestation rates in the undeveloped region of Paraguay's Chaco

Case study from UDACITY Data Scientist Nanodegree

Corporate Credit Rating Prediction System - Knowledge Engineering Project - A.A. 2023-2024

Credit Score Prediction is a machine learning project that classifies credit scores ('Good', 'Standard', 'Poor') using a streamlined pipeline. It involves data extraction, cleaning, and preprocessing, with key techniques like Mutual Information for feature selection, PCA for dimensionality reduction, and XGBoost for efficient and accurate model tr

Imbalanced Intent classification model with deployment

Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite, Accuracy: 98% AUC-ROC

Course Project for CS273A: Machine Learning at UCI

Machine learning is used to analyze and predict attrition flags in credit card customers.

Facial skin disease detection using Neural Networks

Data Science Classification General Notebook

Prediction module for Tumor Teller - primary tumor prediction system

Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis

🛡️ Detect fraudulent credit card transactions with CardShield AI, an advanced machine learning pipeline utilizing SMOTE and optimized classification models.

An analysis on credit risk

Portfolio of my data science projects which i have completed for learning, skill development .

Using Scikit-learn and Imbalanced-learn to build and evaluate ML models that predict credit risk

Successfully trained a machine learning model which can predict whether a given transaction is fraud or not.

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This project aims to develop a machine learning model to classify SMS messages as spam or not spam. The project encompasses the entire pipeline from data collection and preprocessing to model training, evaluation, and deployment using Streamlit for an interactive user interface.

Fraudulent Credit Transaction detection system using SMOTE, Random Forest Classifier and Streamlit

These are my notes for the interview prep workshop I led on Random Forests

🛡️ CardShield AI – Fraud Identification System Advanced credit card fraud detection system leveraging machine learning, SMOTE for imbalance handling, optimized Random Forest, feature scaling, and an interactive Streamlit app for single and batch transaction predictions.

Solutions for task at STEM in CU & baseline for Kaggle Competition

Parkinson’s disease is a progressive disorder that affects the nervous system and the parts of the body controlled by the nerves. Symptoms are also not that sound to be noticeable. Signs of stiffening, tremors, and slowing of movements may be signs of Parkinson’s disease.

Modelo XGBoost para predição de acidentes de trânsitos fatais nas rodovias federais do BRASIL 🔰

Building a tabular binary classification neural network to predict Telco's Customer Churn from their publicly available dataset on Kaggle.