Exploring a real-world dataset for regression analysis.
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Updated
Aug 25, 2023 - Jupyter Notebook
Exploring a real-world dataset for regression analysis.
Testing LazyPredict performance
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.
This Program is for Prediction of Breast Cancer
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.
The projects aim is to find the to best ML algorithm evaluated on its efficiency in predicting whether homes should be classified as expensive or not expensive.
Utilizing LazyPredict, Feature Engine, Feature Tools: Narrow down base models automating various aspects of the eda process. Blog post at link.
Predict home prices using regression, optimizing for R2 and RMSE metrics to create a robust model with minimal prediction deviation.
Lazy Predict 2.0 to help you benchmark models without much code and understand what works better without any hyyper-parameter tuning.
Attrition prediction of Employees
Project is about predicting Class Of Beans using Supervised Learning Models
Trabalho desenvolvido para o SoulMaster Upskilling da Soulcode Academy que busca resolver um problema específico na área de educação com o uso de Análise Preditiva com Vertex AI e LazyPredict.
This is our Design Project for the semester.
This Program is for Prediction of Crop Recommendation based on Rainfall,Humidity,Amount of Potassium and Amount of Nitrogen
In this repository, I have displayed some of the datasets I've worked upon.
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
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