Deep-Learning-with-MLFlow

Gender Classification with Neural Network and MLflow

This project demonstrates how to build a deep learning model using TensorFlow for gender classification and integrate it with MLflow for experiment tracking. The model is trained on a pre-processed dataset, and the experiments are logged to a local MLflow server.

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Features

• Data Preprocessing: Handles categorical data using LabelEncoder and scales features using StandardScaler.
• Neural Network Architecture: A multi-layer dense neural network built with TensorFlow/Keras.
• Experiment Tracking: Logs parameters, metrics, and model artifacts to MLflow.

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Requirements

To run this project, you need the following:

• Python 3.7+
• TensorFlow
• MLflow
• pandas
• scikit-learn