Gradient-Boosting Algorithm 📊

👨‍💻 This repository contains code for evaluating various machine learning models for binary classification.

Features Selection 🧐

🔍 Features used for the models are selected through the Feature Importance method using Random Forest.

Data Import 📈

The dataset is loaded from "FMat.pkl", and features are extracted for training and testing.

Data Preprocessing 🔄

Categorical data is encoded using LabelEncoder, and features are normalized.

Data Split 🧩

The dataset is split into training and testing sets (80% training, 20% testing) with a random seed of 42.

Models 🤖

The following models are evaluated:

• K-Nearest Neighbors (KNN)
• Logistic Regression
• Naive Bayes
• Decision Tree
• Random Forest
• LightGBM
• XGBoost

Model Evaluation 📈

Model performance is assessed using:

• F1-score
• Area Under the Curve (AUC)

Results 📊

Results for each model are displayed, including F1-score and AUC. 🚀

Use this code to evaluate the performance of various machine learning models for binary classification tasks. Explore and modify the code for your datasets and model evaluations. 📚

For more details, refer to the code comments and documentation. Feel free to adapt and improve it for your specific needs. 😊