AI-Based-Threat-Detection

📊 Exploratory Data Analysis (EDA) & Deep Learning Classification

This project combines Exploratory Data Analysis (EDA) with Neural Network-based classification to extract insights and build predictive models from a dataset. It involves feature engineering, PCA-based dimensionality reduction, and training deep learning models using TensorFlow/Keras.

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📁 Project Structure

project.ipynb # Main Jupyter Notebook with EDA and Deep Learning workflow README.md # Project overview and documentation

🔍 Key Concepts Covered

🧪 Exploratory Data Analysis

• Skewness Analysis:
  • |Skewness| < 1: Approximately symmetric
  • |Skewness| > 1: Highly skewed
  • 1 < |Skewness| < 2: Moderately skewed
• Statistical Summaries
• Outlier Detection
• Visualizations:
  • Histograms, Box Plots, Pair Plots
• Feature Scaling using StandardScaler
• Dimensionality Reduction using PCA

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🤖 Deep Learning Model

• Libraries: TensorFlow / Keras
• Model Architecture:
  • Feedforward Neural Networks (Multilayer Perceptron)
  • Input: Scaled and PCA-reduced features
  • Output: One-hot encoded target variable
• Training Details:
  • Loss function: Categorical Crossentropy
  • Optimizer: Adam
  • Epochs: 100
  • Early stopping & checkpoint callbacks used
• Evaluation Metrics:
  • Accuracy Score
  • Confusion Matrix
  • Classification Report (Precision, Recall, F1)

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📊 Example Results

• Training Accuracy: ✅ (Check notebook for exact value)
• Validation Accuracy: ✅ (Plotted using history)
• Confusion Matrix: ✔️ Reveals class-wise performance
• PCA Explained Variance: ✔️ Helps understand dimensionality contribution

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🛠️ Tech Stack

• Python 3.x
• Jupyter Notebook
• pandas, numpy, matplotlib, seaborn
• scikit-learn
• tensorflow / keras

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🚀 How to Run

1. Clone the Repository

   git clone https://github.com/yourusername/eda-deeplearning-project.git
   cd eda-deeplearning-project

2. Set Up Virtual Environment

   python -m venv venv
   source venv/bin/activate  # Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Run the Notebook

   jupyter notebook project.ipynb

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✅ Outcomes

• Insights on data distribution and skewness
• PCA-based dimensionality reduction
• Trained deep learning model with high accuracy
• Clear evaluation via confusion matrix and classification report

📌 Notes

• Ensure the dataset is placed correctly or loaded within the notebook.
• You can tune the model architecture, learning rate, or add regularization for improvement.