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

The purpose of this analysis was to create a supervised machine learning model that could accurately predict credit risk using python's sklearn library.

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

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

Supervised Machine Learning and Credit Risk

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

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…

Customer Churn Prediction

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

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.

CampusX Internship Task 3

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

Stock Price Prediction of APPLE Using Python

Improving a Machine Learning Model

This is my Hamoye Stage C tag-along project. The notebook focuses on applying Machine Learning Classification models and Measuring Classification Performance.

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.

Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.

The model should predict whether is it going to rain the next day coming or it isn't. The models that have been deployed were TensorFlow Sequential, Random Forest Classifier and GradientBoostingClassifier. The best model on both training and test set was achieved with Gradient Boosting Classifier with 95.2% and 85.5% accuracy on the train and test.

Exploratory data analysis exercises to understand the main characteristics of a given data set before performing more advanced analysis or further modeling

The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.