Using various machine learning models (Logistic Regression, Gaussian Naïve Bayes, KNN, Gradient Boosting Classifier, Decision Tree Classifier, Random Forest Classifier.) to predict whether a company will go bankrupt in the following years, based on financial attributes of the company; Addressed the issue of imbalanced classes, different importance

Malicious URL detector built with deep exploration on feature engineering.

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

Churn Prediction Web Application for streamlit

Prediction possible strokes using RandomForest and PCA

Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it

Perform an exploratory data analysis and provide actionable insights for a food aggregator company to get a fair idea about the demand of different restaurants and cuisines, which will help them enhance their customer experience and improve the business

The project provides information about breast cancer to help doctors predict if a person has it.

in this project we used image processing Technique to classify 9 class malwares our final goal is to reach an appropriate model with high accuracy and small size and computational cost

Using Machine Learning Classification Algorithms, Predicting the Customer will default or not..

Scoring model for financial company - all files

Classification project - dealing with imbalanced dataset

Comparison of XGBoost, CatBoost, and LGBM for Parkinsons Classification

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

Over- and under-sampled data using four algorithms and compared two machine learning models that reduce bias to identify the most reliable credit risk prediction model.

This is a customer churn prediction project using machine learning algorithms like Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machine, XGBoost, and Gradient Boosting. The project aims to analyze and predict customer churn in a dataset, using techniques like class weighting and SMOTE to handle class imbalance

A newsletter conversion predictor

As the there are couple of classification algorithms in supervised machine learning so some of them as here..

This repository contains files on Predict probability whether a given blight ticket will be paid