First push

README.md

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+Simple NN that takes in 27 parameters related to a cryptocurrency and classifies the datapoint as 0 OR 1.

model.py

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+import tensorflow as tf
+
+class CryptoBinaryClassifier(tf.keras.Model):
+    def __init__(self, *args, **kwargs):
+        super(CryptoBinaryClassifier, self).__init__()
+        # Define your model architecture here
+        self.model = tf.keras.Sequential([
+            tf.keras.layers.Input(shape=(27,)),
+            tf.keras.layers.Dense(64, activation='relu'),
+            tf.keras.layers.Dense(32, activation='relu'),
+            tf.keras.layers.Dense(1, activation='sigmoid')
+        ])
+    
+    def call(self, inputs, training=False):
+        return self.model(inputs)
+
+    def __init__(self, *args, **kwargs):
+        super(CryptoBinaryClassifier, self).__init__()
+        # Load your pre-trained weights
+        self.load_weights('AVAXUSDT_x22.xlsx_binary_classification_model.h5')
+
+    def predict(self, input_data):
+        # Preprocess input_data if necessary
+        return self(input_data)