A powerful tree-based uplift modeling system.

Web editor for typed tree structures. (like decision / behaviour trees)

Breast cancer detection using 4 different models i.e. Logistic Regression, KNN, SVM, and Decision Tree Machine Learning models and optimizing them for even a better accuracy.

Insurance claim fraud detection using machine learning algorithms.

Smart disease prediction system made using traditional machine learning algorithms and to create an user interface using streamlit. 🚀

A web application to predicted whether a URL/Website is phishing or not by extracting its lexical features.

This study demonstrates how numerous factors have an impact on bike rentals. Due to our understanding that many Koreans hire bikes throughout the week, we assumed that most of their use is for commuting to work or school. The number of rentals varies depending on a number of factors, including the day of the week, the hour of the day.

In this data science project, we will predict borrowers chance of defaulting on loans by building a default prediction model.

In this regression project, We will make use of different features like age, BMI, region, sex, smoker, etc to predict the medical insurance cost for an individual.

几种常见的特征分箱与可视化的方法

Unity package for generating 'treescheme' files based on dotnet assemblies.

Evaluation and Implementation of various Machine Learning models for creating a "Banking/Financial Transaction Fraud Prevention System"

machine-learning algorithms using Python.

This repository contains all resources for Homework 1 of TDT4173 fall 2021.

In this notebook, I'm using this dataset called 'flight-price-prediction', which contains the traveller information.In this notebook, I'm trying to run a dummy variable regression model at first, and after that, I'm trying to build a supervised ML Model with higher accuracy of predation.

Prediction of customer will purchase iPhone or not using KNN classifier model and multiple supervised ML model.

Code for my Medium article: "How you can quickly deploy your ML models with FastAPI"

This project presents and discusses data-driven predictive models for predicting the defaulters among the credit card users.About Data Cleaning,Exploratory Data Analysis ,Handling Class Imbalance, Transforming Data , Fitting Different Model ,Cross Validation & Hyperparameter Tunning, Comparison of Model ,Combined ROC Curve, Feature Impotance.

Breast Cancer Detection: This project uses machine learning techniques to classify breast cancer as malignant or benign based on features extracted from breast mass biopsies. Models used include SVM, Decision Tree, Naive Bayes, and K-Nearest Neighbors.

Machine learning course at IDC. Implemented several amount of ML algorithms in Python using Jupyter notebooks