Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

Développer un modèle de scoring de la probabilité de défaut de paiement du client pour étayer la décision d'accorder ou non un prêt à un client potentiel.

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.

Big data manipulation and modelling projects

Classify antioxidant property on protein sequence based on protein sequence feature

Trabajo fin de máster de ciencia de datos UC-UIMP-CSIC 2018-2019

Churn Prediction Web Application for streamlit

Bank Customer Behaviour Prediction

RFE (Recursive Feature Elimination) with cross-validation

Hospitals contain large databases. We can use that data to discover new useful and potentially life saving knowledge. Here we use datamining especially to predict type 2 diabetes mellitus.Predicting the percentage of chance of occurrence of Diabetes mellitus type 2 with less time complexity and high accuracy.

We have a data of retail transactions over two year. Apart from data analysis and visualization, a regression model is developed to predict the price of retail items belonging to different categories. Foretelling the Retail price can be a daunting task due to the huge datasets with a variety of attributes ranging from Text, Numbers(floats, integ…

This Jupyter notebook demonstrates a Recursive Feature Elimination with Cross-Validation (RFECV) feature selection process with a random forest model.

End to End Machine Learning Projects Examples

Streamlit app developed for bank customer deposit prediction, using a fine-tuned XGBClassifier model.

This repository contains a collection of hacks and tips for feature engineering. It is a great resource for anyone who wants to learn how to improve the performance of their machine learning models.

Explored data using data visualisation and exploratory data analysis. Used Logistic Regression to create a basic prediction model. Improved model using recursive feature elimination.

In this project we're going to explore a workflow to easily compete in the Kaggle Titanic competition, using a pipeline of functions to reduce the number of dimensions you need to focus on.