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

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

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.

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

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.

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.

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 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.

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

Here are some fun projects to learn ML using Handson approach

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

Predicting house price

Stock Price Prediction of APPLE Using Python

Supervised Machine Learning and Credit Risk

This github repositiory contains the Flight Price Prediction project aims to develop a machine learning model to predict flight ticket prices based on various factors such as departure and arrival locations, dates, airlines, and other relevant features.

In this project, we aim to identify different fruits: apples, bananas, oranges, and tomatoes; through different Machine Learning algorithms: CNN, XGBoost, InceptionV3 transfer learning, and VGG16 transfer learning

A Preprocessing, Analytical and Modeling Case Study using Supervised ML Models