📊 Machine Learning Tabular Model (Keras + TensorFlow)

A simple but effective machine learning pipeline built with TensorFlow/Keras for training on structured/tabular data using a CSV file as input.

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🚀 Features

• ✅ Automatic encoding and normalization for numeric and categorical features
• 🧠 Dense Neural Network (DNN) architecture
• 📦 TensorFlow tf.data.Dataset pipeline
• 🔎 Model visualization with keras.utils.plot_model
• 📑 CSV-based training data
• 🧪 Separate training (main.py) and testing (test.py) scripts

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📁 Project Structure

File/Folder	Description
main.py	Trains the model and processes the dataset
test.py	Loads the model and makes predictions
data.csv	Tabular dataset including input features + target
Data/	(Optional) Additional files or output directory

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🔍 Technical Notes Numeric features are normalized using Normalization layers.

Categorical features are encoded using StringLookup.

The target column must be named Target in the CSV.

Dataset is processed using the efficient tf.data.Dataset pipeline.

The model structure can be visualized using plot_model.

🔧 How to Run

1. Install dependencies:

pip install tensorflow pandas numpy
python main.py
python test.py