This repository contains a complete end-to-end deep learning project using an Artificial Neural Network (ANN) for binary classification. The model is trained on a real-world dataset and demonstrates the key stages of building and deploying a deep learning model using Python.

• Objective: Predict whether a customer will exit a bank based on various attributes such as credit score, geography, age, balance, and more.
• Model: Artificial Neural Network (ANN)
• Tools Used: Python, TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn

📦 End-to-End-Deep-Learning-Project-Using-ANN
├── data/                        # Dataset used for training/testing
├── model/                       # Trained ANN model
├── ann\_classifier.ipynb        # Main Jupyter Notebook (model building + EDA)
├── README.md                   # Project description and setup

• Python 3.x
• TensorFlow / Keras
• Pandas & NumPy
• Scikit-learn
• Matplotlib & Seaborn
• Jupyter Notebook

1. Data Preprocessing

   • Load dataset
   • Handle missing values
   • Encode categorical data
   • Normalize features
   • Train-test split

2. Model Building

   • Create ANN using Keras Sequential API
   • Add hidden layers with ReLU activation
   • Use Sigmoid activation in the output layer
   • Compile with Adam optimizer and binary cross-entropy loss

3. Model Training

   • Train on the processed dataset
   • Track loss and accuracy

4. Evaluation

   • Evaluate using confusion matrix, accuracy, precision, recall, and F1-score
   • Plot training/validation accuracy and loss

5. Prediction

   • Test the model on new/unseen data

• Achieved high accuracy and consistent performance on both training and testing data.
• Model generalizes well and can be deployed in a production-ready pipeline.

1. Clone the repository:

   git clone https://github.com/udityamerit/End-to-End-Deep-Learning-Project-Using-ANN.git
   cd End-to-End-Deep-Learning-Project-Using-ANN
   
   

2. Install the required packages:

   pip install -r requirements.txt

3. Run the notebook:

   jupyter notebook ann_classifier.ipynb

The dataset used in this project is a modified version of a customer churn dataset. You can find it in the /data folder or from sources like Kaggle.

Uditya Narayan Tiwari B.Tech CSE (AI & ML) | VIT Bhopal University 🔗 Portfolio Website 💼 LinkedIn 📁 GitHub

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This project is licensed under the MIT License.