I tried to implemented two Data Mining theories in Object Detection Problem. One of them is Object Recognition with descriptive statistical feature, and the other one is with Decision Tree.

Heart disease detection using different classifiers and neural network with feature engineering.

Data Science project for loan approval prediction

ML application on wearable devices to predict a person's activities.

This repository covers a "Lending Club" analysis with decision tree and random forest with data visualizations and exploration.

The project focuses on identifying the speaker accent to be US or not US using binary classification. This project uses various Machine Learning classification methods like Logistic Regression, KNN, Binary Tree and Random Forests. Using the listed methods, evaluated the performance on the baseline models. To increase the accuracy and to prevent …

In this repository, use of diffrent types of trees models

This model is for detection of fake news using four different classifiers.

This project aims to address the challenge of predicting whether it will rain or snow in Hungary based on various meteorological variables.

A Diabetes prediction Machine learning model using diagnostic data and algorithms such as logistic regression and decision trees. Evaluate their performance and deploy the top-performing model for accurate diabetes prediction.

Diabetes Prediction Project: Decision Tree Classifier implementation using Pima dataset. Explore data, visualizations, and contribute to enhance accuracy and interpretability. Contributions welcome!

Predictive models for breast cancer classification using machine learning algorithms. Explore various classification techniques to identify malignant and benign tumors from medical imaging data.

基于RFM和决策树模型构建专家推荐系统。融合了RFM模型和决策树模型，结合专业运营人员的业务经营，发掘潜在用户，进行推荐营销召回。

Exploring MNIST Dataset using Tensorflow and Keras

This repository contains a project for predicting house prices using multiple regression techniques and machine learning models, including boosting algorithms. The goal is to train several models on historical house price data and evaluate their performance using the R² score.

this project involves analyzing customer churn data to identify patterns and prevent customer churn by understanding the behaviors and characteristics that lead to it.