# 🧠 ANN-Based Classification Model
## 📌 Overview
This project implements an Artificial Neural Network (ANN) for solving classification problems. It includes data preprocessing, model training, evaluation, and performance optimization.
The system is designed to demonstrate how deep learning models can be applied to structured datasets for accurate classification.
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## 🎯 Problem Statement
Traditional machine learning models may struggle with complex, non-linear relationships in data.
This project solves that by:
- Using ANN to capture non-linear patterns
- Automating feature learning
- Improving classification performance
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## 🚀 Features
- Data preprocessing and feature scaling
- ANN model implementation
- Training and evaluation pipeline
- Hyperparameter tuning
- Performance metrics calculation
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## 🛠️ Tech Stack
- Python
- TensorFlow / Keras
- Scikit-learn
- NumPy
- Pandas
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## ⚙️ System Architecture
1. Input Dataset
2. Data Preprocessing
3. Feature Scaling
4. ANN Model Training
5. Evaluation and Prediction
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## 🔄 Workflow
1. Load dataset
2. Clean and preprocess data
3. Apply feature scaling
4. Train ANN model
5. Evaluate using metrics
6. Generate predictions
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## 📊 Results
- Accuracy: XX%
- Precision: XX
- Recall: XX
- F1 Score: XX
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## 📂 Project Structure├── data/ ├── models/ ├── src/ ├── notebooks/ ├── requirements.txt └── README.md
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## 🔧 Installation
```bash
pip install -r requirements.txt
python train.py
python predict.py- Classification predictions
- Confusion matrix
- Model evaluation metrics
- Captures complex non-linear relationships
- Improves classification performance over basic models
- Scalable and extendable architecture
- Add feature engineering
- Optimize hyperparameters
- Deploy model using FastAPI
- Improve generalization with regularization
Contributions are welcome. Please fork the repository and submit a pull request.
This project is licensed under the MIT License.
Abhishek Sharma GitHub: https://github.com/brogrammercodes LinkedIn: https://www.linkedin.com/in/abhishek-sharma27012003/