Initial commit of my Keras model

README.md

@@ -18,4 +18,88 @@ The above  model  is a simple neural network built using TensorFlow/Keras. It is
 
 
 
+# Keras Model for Time Series Prediction
 
+This repository contains a Keras model trained for time series prediction. The model has been deployed to Hugging Face Hub and can be used to predict numeric values based on timestamped data.
+
+## Model Overview
+
+The model is a simple neural network built using Keras. It is designed to perform regression tasks, predicting a numeric value from input features.
+
+### Architecture
+
+- **Input Layer**: 10 neurons, ReLU activation
+- **Hidden Layer**: 10 neurons, ReLU activation
+- **Output Layer**: 1 neuron (for regression)
+
+## How to Use the Model
+
+To use this model, follow the steps below:
+
+### 1. Install Required Libraries
+
+Make sure you have the necessary libraries installed:
+
+```bash
+pip install tensorflow huggingface-hub pandas matplotlib
+
+#load the model from the Hugging Face Hub:
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the model from Hugging Face Hub
+model = from_pretrained_keras("iiitbh18/bhaskar1")
+
+# Prepare Dummy Data
+
+# Create a DataFrame with dummy data
+dummy_data = {
+    'timestamp': [
+        '2024-08-07 02:43:28.788',
+        '2024-08-07 02:43:28.788',
+        '2024-08-07 02:43:43.788',
+        '2024-08-07 02:43:43.788',
+        '2024-08-07 02:43:58.788'
+    ],
+    'value': [
+        99.00000005960464,
+        98.90000000596046,
+        98.70000004768372,
+        99.00000005960464,
+        98.89999993145466
+    ]
+}
+
+df = pd.DataFrame(dummy_data)
+
+#Prepare Data for Prediction
+
+X_dummy = df['value'].values.reshape(-1, 1)  # Reshape to match model's input shape
+
+
+
+
+
+
+
+
+# Make Predictions
+
+predictions = model.predict(X_dummy)
+print("Predictions:", predictions)
+
+#Visualize the Results
+
+# Plot actual vs. predicted values
+plt.figure(figsize=(10, 6))
+plt.scatter(df['timestamp'], df['value'], color='blue', label='Actual Values')
+plt.scatter(df['timestamp'], predictions, color='red', label='Predicted Values')
+plt.xlabel('Timestamp')
+plt.ylabel('Value')
+plt.title('Actual vs Predicted Values')
+plt.legend()
+plt.xticks(rotation=45)
+plt.show()