This repository contains a neural network classifier implemented in Python, leveraging TensorFlow/Keras to demonstrate the process of model building, training, evaluation, and visualization of results. The project is fully documented in a Jupyter Notebook, guiding you step-by-step through data preprocessing, model construction, and performance analysis.
- Data Preprocessing: Loading datasets, handling missing values, and preparing data for model training.
- Model Construction: Building a customizable neural network model using TensorFlow/Keras.
- Training and Tuning: Training the model on processed data with tunable hyperparameters.
- Evaluation & Visualization: Evaluating model performance and visualizing training results and metrics.
- Python: The main programming language for implementation.
- TensorFlow/Keras: For building and training the neural network.
- Pandas & Numpy: For data handling and numerical operations.
- Matplotlib & Seaborn: For creating visualizations of data and model metrics.
- Jupyter Notebook: For interactive code execution and result presentation.
- Python >= 3.6
- Jupyter Notebook or Jupyter Lab for interactive execution
- Packages: TensorFlow, Pandas, Numpy, Matplotlib, Seaborn
You can install the necessary packages using:
pip install -r requirements.txtOr manually:
pip install tensorflow pandas numpy matplotlib seaborn- Clone the Repository:
git clone https://github.com/your-username/neural-network-classification.git
- Navigate to the Project Folder:
cd neural-network-classification - Launch Jupyter Notebook:
jupyter notebook
- Run the Notebook: Open
NN-Classification.ipynbin Jupyter Notebook and execute the cells sequentially to reproduce the project results.
The project will produce:
- Training Metrics: Accuracy and loss plots over epochs.
- Evaluation Metrics: Confusion matrix, precision, recall, F1-score, and other relevant metrics.
- Visualization: Graphical representations of the model's performance and data insights.