selectkbest

Chi-Squared For Feature Selection using SelectKBest

With the Student Alcohol Consumption data set, we predict high or low alcohol consumption of students.

Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

Implementing SelectKBest from Scratch for a Regression problem

Machine learning regression pipeline to predict delivery time using feature engineering, GridSearchCV optimization, automated testing and CI with GitHub Actions.

Heart Attack Prediction by implementing Feature Selection such as SelectKBest & Recursive Feature Elimination

Before training a model or feed a model, first priority is on data,not in model. The more data is preprocessed and engineered the more model will learn. Feature selectio one of the methods processing data before feeding the model. Various feature selection techniques is shown here.

Este projeto é o resultado do meu Trabalho de Conclusão de Curso (TCC) para o curso de Bacharelado em Sistemas de Informação pelo Instituto Federal de Minas Gerais – Campus Sabará.

This repository contains introductory notebooks for Decision tree

Predictive modeling project by implementing KNN regression model.

Machine Learning Techniques in Lung Cancer Prediction

Data and code for a machine learning exercise in which I predict the development of depression.

A financial institution wants to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI on the due date.

The Dairy Goods Sales Dataset provides a detailed and comprehensive collection of data related to dairy farms, dairy products, sales, and inventory management.

Logistic Regression project for Banking Marketing Campaign.

Machine Learning pipeline for smartphone price prediction using exploratory data analysis, feature engineering, model comparison, hyperparameter tuning, and XGBoost optimization.