[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes

Volume Under the Surface: Accuracy Measure for Time Series Anomaly Detection

This contains the Jupyter Notebook and the Dataset for the mentioned Classification Predictive Modeling Project

🪨 Machine learning project using logistic regression to classify sonar signals as either rocks or mines. Uses scikit-learn to train a binary classifier on sonar dataset with 60 numerical features for accurate underwater object detection.

🍾 A comprehensive machine learning project using Random Forest algorithm to predict wine quality based on physicochemical properties. Features EDA, model training, hyperparameter tuning, feature importance analysis, and detailed documentation.

🫀 A machine learning project using logistic regression to predict heart disease risk from clinical data. Built with Python, scikit-learn, and Jupyter notebooks. Achieves 85%+ accuracy on 303-patient dataset with 13 medical features. Complete ML pipeline from data exploration to model evaluation.

🩺 Machine Learning diabetes prediction model using Support Vector Machine (SVM) classifier. Analyzes 8 medical features (glucose, BMI, age, etc.) from Pima Indian dataset to predict diabetes risk with 75-80% accuracy. Built with Python, scikit-learn, pandas. Includes data preprocessing, model training, and prediction system for diabetes..

Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample th…

I aim in this project to analyze the sentiment of tweets provided from the Sentiment140 dataset by developing a machine learning sentiment analysis model involving the use of classifiers. The performance of these classifiers is then evaluated using accuracy and F1 scores.

This Model is used to Predict Emails data. Either emails are Spam or Normal (Ham) Mail.

This project develops an activity recognition model for a mobile fitness app using statistical analysis and machine learning. By processing smartphone sensor data, it extracts features to train models that accurately recognize user activities.

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

Code in which an initial approach to decision trees and bagging will be made, and an attempt will be made to ensure that the model can be trained with any dataset coming from Kaggle (for this, we will again use the 'connect with Kaggle' project).

This project develops a deep learning model that trains on 1.6 million tweets for sentiment analysis to classify any new tweet as either being positive or negative.

This project focuses on predicting customer churn in the telecom industry using machine learning techniques. The model is trained to identify factors that influence customer retention and accurately predict whether a customer is likely to stay or leave.

Here we are trying to predict the closing price of the particular Netflix stock on a given trading day.

To create a Decision Tree classifier and visualize it graphically, the purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.

This repository contains all the Machine Learning projects I did using different Machine Learning methods. Python being the main software used.

📚Obesity Risk Detection 🧩Gradient Boosting 🔧Hyperparameter Tuning

Predicting credit risk with machine learning algorithms and help financial institutions detect anomalies, reduce risk cases, monitor portfolios with statistical functions.