Detección de cardiopatías en pacientes mediante el uso de datos clínicos utilizando técnicas de Machine Learning y Deep Learning.

Given the data of the user activity of a month, can predict user activity for the upcoming month.

A Breast Cancer Prediction Model

We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.

Classification problem in Heart-Disease Data

The top 5% of the titanic competition in Kaggle. achieved this through ensemble of models

Water potability classifier using simple logistic regression to advanced gradient boosting algorithms

Credit card defaulter prediction using data science techniques

Notes, tutorials, code snippets and templates focused on CatBoost for Machine Learning.

Built a Machine Learning Supervised classification algorithm, for predicting the risk of cardiovascualar disease withing coming 10 years by analyzing Patients medical History.

This project predicts MBTI personality types from users' recent 50 posts using NLP and ML techniques.

The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be a fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.

classifying a patient has a heart disease or not

Amazon employee data to predict approval/ denial

This repository contains the project where the goal is to develop a machine learning model that can accurately predict car prices based on various features. We explored multiple models including K-Nearest Neighbor, Decision Tree, Catboost Classifier, and Light Gradient Boosting Classifier.

We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.

Solution for competition of workshop ecom-t and Deep Learning School

Global plastic waste is a pressing environmental issue, with massive production, limited recycling, and high risks to ecosystems and human health

Create a machine learning model to predict whether an individual earns above 50,000 in a specific currency or not.