Hybrid-Machine-Learning-Model-

Hybrid Machine Learning Model combining Logistic Regression, Random Forest, and XGBoost to improve classification accuracy through ensemble voting. Includes full pipeline: data preprocessing, EDA, PCA, model building, evaluation, and performance comparison. Ideal for showcasing advanced modeling techniques and real-world problem-solving skills.

🚀 Features

• Data Cleaning & Preprocessing
• Exploratory Data Analysis (EDA)
• Dimensionality Reduction using PCA
• Training:
  • Logistic Regression
  • Random Forest Classifier
  • XGBoost Classifier
• Voting Classifier Ensemble (Hard/Soft Voting)
• Performance Comparison with Metrics & Visualization

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📂 Project Structure

• data/: Dataset files
• notebooks/: EDA, PCA, and modeling notebooks
• scripts/: Modular scripts for preprocessing and model training
• models/: Saved trained models
• results/: Visualizations and performance metrics
• README.md: Project documentation

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📊 Visuals

Confusion Matrix	ROC Curve

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🧪 Technologies Used

• Python (NumPy, Pandas, Scikit-learn)
• XGBoost
• Matplotlib & Seaborn
• Jupyter Notebook

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📈 Performance Metrics

Model	Accuracy	Precision	Recall	F1-score
Logistic Regression	0.86	0.83	0.84	0.84
Random Forest	0.89	0.87	0.88	0.87
XGBoost	0.91	0.89	0.90	0.89
Hybrid (Voting)	0.93	0.91	0.92	0.91

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📌 How to Run

# Clone the repo
git clone https://github.com/your-username/Hybrid-Machine-Learning-Model.git
cd Hybrid-Machine-Learning-Model

# Install dependencies
pip install -r requirements.txt