Add model card

README.md

@@ -5,20 +5,29 @@ tags:
 - tabular-data
 - classification
 ---
+# Tensorflow Keras Implementation of Structured data classification from scratch
+This repo contains models and notebook for [Structured data classification from scratch](https://keras.io/examples/structured_data/structured_data_classification_from_scratch/).  
+This example demonstrates how to do structured data classification, starting from a raw CSV file. Our data includes both numerical and categorical features. We will use Keras preprocessing layers to normalize the numerical features and vectorize the categorical ones.  
+Full credits to [François Chollet](https://twitter.com/fchollet), creator of Keras!
 
 ## Model description
 
-More information needed
+The model is a very simple MLP with only one hidden layer.
+This example showcases how to perform preprocessing of common tabular data *inside* a Keras model.
+It uses tensorflow.keras.layers.{IntegerLookup, Normalization, StringLookup} to process numerical and categorical (integer or string) features.
+
+
 
 ## Intended uses & limitations
 
-More information needed
+This tool does not provide medical advice It is intended for informational purposes only. It is not a substitute for professional medical advice, diagnosis or treatment. 
 
 ## Training and evaluation data
 
-More information needed
+[Our dataset](https://archive.ics.uci.edu/ml/datasets/heart+Disease) is provided by the Cleveland Clinic Foundation for Heart Disease. It's a CSV file with 303 rows. Each row contains information about a patient (a sample), and each column describes an attribute of the patient (a feature). We use the features to predict whether a patient has a heart disease (binary classification).
 
 ## Training procedure
+Training proceeds for 50 epochs with default Adam optimizer on binary crossentropy.
 
 ### Training hyperparameters