Lazy Predict 2.0 to help you benchmark models without much code and understand what works better without any hyyper-parameter 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

This Program is for Prediction of Breast Cancer

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

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

This is our Design Project for the semester.

Analysis and predictions of Traffic Accidents in the City of Madrid

Exploring a real-world dataset for regression analysis.

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

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

Attrition prediction of Employees

Testing LazyPredict performance

LazyPredict - Comparer rapidement plusieurs modèles de Machine Learning

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

The main objective of this project is to utilize machine learning using the LazyPredict library to predict values of a specific column. The primary focus is on minimizing the error index (RMSE) to achieve the highest possible accuracy in our predictions.

Predict home prices using regression, optimizing for R2 and RMSE metrics to create a robust model with minimal prediction deviation.