[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.

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.

Here are some fun projects to learn ML using Handson approach

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.

Predicting house price

Stock Price Prediction of APPLE Using Python

Supervised Machine Learning and Credit Risk

Covid-19 prediction (For Nepal) with different MODELS (Sigmoidal, Linear Regressor, Random Forest Regressor) and comparisons

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

It's a Python based Movie Recommendation System, which recommends movies to the users based on the similarity to previously watched movies. I have solved a machine learning classification problem and evaluated the model's performance using confusion matrix, accuracy score, recall score, precision and f1 score.

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

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

👨‍💻👨‍💻 Bank deposit prediction model by Binomial Logistic Regression