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.

Exemplary, annotated machine learning pipeline for any tabular data problem.

Tutorial on how to perform feature encoding, feature scaling, and missing values imputation using the scikit-learn library

Categorical Feature Encoding using Logistic Regression

Multiple methods to (quickly) encode factor variables, using data.table

churn prediction using machine learning classification models

first-person activity recognition

This project analyzes and preprocesses the Online Retail dataset to uncover insights into customer purchasing behaviors, sales trends, and product performance. It includes data cleaning, exploration, and visualization, with the goal of enhancing understanding of online retail dynamics.

Source code and public data used for the project 'A machine learning framework for actionable genetic insights discovery: use in triple-negative breast cancer'.

4Geeks Academy data science boot camp data preprocessing assignment

This project uses predictive analytics to optimize marketing strategies by forecasting customer subscriptions to term deposits. It involves collecting and preprocessing data, training a model, and assessing its performance. Ongoing evaluation ensures adaptability to changing market dynamics, providing valuable insights for marketing analysis

🔴 Predicting Insurance Claim Amounts 🔴 This project analyzes the Medical Cost Personal Insurance Dataset to understand key factors influencing healthcare expenses. Through data cleaning, visualization, and feature engineering, important patterns in age, BMI, smoking, and region were uncovered.

Obesity severity level prediction based on different features.. these imp features are extracted then trained using polynomial regression

A machine learning project that predicts car prices based on a dataset.

This project predicts the likelihood of forest fires in Algeria using machine learning regression techniques based on weather and environmental data. It includes EDA, feature engineering, and model deployment.

Classification of movies as 'Fresh', 'Rotten', 'Certified-Fresh' using categorical predictors as well as review sentiment. Performed feature encoding and used Decision Tree, Random Forest Classifiers. Tackled class imbalance issues by assigning weights to classes. Used tokenization to generate word vectors for reviews to predict movie status.

👩🏻‍🍳🍽️Restaurant Success Prediction using ML

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Unofficial Pytorch Implementation of 'Uncorrelated feature encoding for faster style transfer'

Neural network that emulates DGE analysis

SAS Macro examples for the Blog Post "5 Categorical Encoding Techniques in SAS"

Predicting Hotel Booking Cancellation with Machine Learning

End-to-end data preprocessing techniques using Python, including handling missing values, feature encoding, scaling, and outlier detection.

🌾 A machine learning-based crop production prediction system using historical Indian agricultural data with advanced regression models and hyperparameter tuning.

This project shows a guide for improving the accuracy of regression model.

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🔍 Predict insurance claim costs using data analytics and machine learning to reveal insights about healthcare expenses and improve accuracy in predictions.