Lazy Predict Nightly enables rapid benchmarking of machine learning models with just a few lines of code. It helps identify the best-performing algorithms on your dataset without requiring hyperparameter tuning.

The objective of the project is to perform advance regression techniques to predict the house price in Boston.

The objective of this project is to determine the risk of default that a client presents and assign a risk rating to each client. The risk rating will determine if the company will approve (or reject) the loan application

Analysis and predictions of Traffic Accidents in the City of Madrid

Utilizing LazyPredict, Feature Engine, Feature Tools: Narrow down base models automating various aspects of the eda process. Blog post at link.

This Program is for Prediction of Breast Cancer

Project is about predicting Class Of Beans using Supervised Learning Models

This Program is for Prediction of Crop Recommendation based on Rainfall,Humidity,Amount of Potassium and Amount of Nitrogen

Predicting whether a customer is happy based on the results from a survey.

Containerised tool for machine learning model exploration using lazypredict

Binary Classification problem. Contains Classifiers from various AutoML libraries such as AutoGluon, FLAML, Lazypredict, & TPOT

Attrition prediction of Employees

This is our Design Project for the semester.

In this repository, I have displayed some of the datasets I've worked upon.

Predict patient satisfaction using machine learning based on doctor experience, reviews, fees, and wait times. Includes data prep and model comparison with LazyPredict.

Exploring a real-world dataset for regression analysis.

Crop yields prediction using climatic factors

The primary goal of this project is to conduct an Exploratory Data Analysis (EDA) in Formula 1 using Tableau and a pre-made MySQL database created with web scraping using Selenium. Our secondary objective is to develop a web page with Streamlit and apply machine learning with LazyPredict to predict events in Formula 1.

Practical exercise using the "Richter's Predictor: Modeling Earthquake Damage" competition on DrivenData