👫 Gender Classification Using SVM

📌 Overview

This project implements a Support Vector Machine (SVM) classifier to predict gender (Male/Female) based on facial physical characteristics. The model utilizes the RBF (Radial Basis Function) Kernel to handle non-linear relationships in the data, achieving high accuracy on both training and testing datasets.

📂 Dataset Features

The dataset consists of 5001 samples with the following features:

• long_hair: (0 or 1)
• forehead_width_cm: Continuous value
• forehead_height_cm: Continuous value
• nose_wide: (0 or 1)
• nose_long: (0 or 1)
• lips_thin: (0 or 1)
• distance_nose_to_lip_long: (0 or 1)
• gender: Target variable (Male/Female)

🛠️ Technologies Used

• Python: Core programming language.
• Pandas: For data manipulation and analysis.
• Plotly: For interactive Exploratory Data Analysis (EDA) and visualizations.
• Scikit-Learn: For building the SVM model and evaluation metrics.

📊 Methodology

1. Data Loading & Inspection: Checking for null values and understanding data distribution.
2. EDA: Visualizing feature distributions using Plotly histograms differentiated by gender.
3. Preprocessing: Encoding the categorical target (gender) into numerical values (Male: 1, Female: 0).
4. Splitting: Dividing data into Training (80%) and Testing (20%) sets.
5. Model Training: Training an SVC model with kernel='rbf'.
6. Evaluation: Using Accuracy Score and Confusion Matrix.

📈 Results

The model demonstrated excellent performance with minimal overfitting:

Metric	Score
Training Accuracy	97.00%
Testing Accuracy	96.30%

Confusion Matrix (Testing Set)

• True Negatives (Female predicted correctly): 486
• True Positives (Male predicted correctly): 478
• False Positives: 16
• False Negatives: 21

🚀 How to Run

1. Clone the repository:

   git clone [https://github.com/samir-m0hamed/Gender-Classification-SVM.git](https://github.com/samir-m0hamed/Gender-Classification-SVM.git)

2. Install dependencies:

   pip install pandas numpy scikit-learn plotly

3. Run the Jupyter Notebook:

   jupyter notebook Gender_Classification.ipynb

🤝 Contributing

Contributions, issues, and feature requests are welcome!

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👤 Author

samir mohamed