This project detects AI-generated text using an ensemble of classifiers: Multinomial Naive Bayes, Logistic Regression, LightGBM, and CatBoost. It includes robust data preprocessing, model development, and evaluation, ensuring accurate identification of AI-generated content from a diverse text dataset.

4 Boosting Algorithms You Should Know – GBM, XGBoost, LightGBM & CatBoost

Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio

predicting a continuous target (regression) based on number of anonymous features columns given in the data.

All Possible Machine Learning algorithms implementation in jupyter notebook with csv file.

Repository contains Vehicle Loan Default Prediction of L&T which involved EDA, Statistical Analysis and Model Building

Credit Risk Analysis with Machine Learning

Deploying Flight Price Prediction via Microsoft Azure

Analytics Vidhya AmExpert ML Competition

Using Data to Bring Customers Home

Data Storm 1.0 - Business Problem - Team Fixzels