Update README.md

README.md

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 ---
 license: mit
+
+tags:
+  - keras
+  - tensorflow
+  - time-series-classification
+  - sensor-data
+  - deep-learning-lab
 ---
+
+# Wearable Activity Classifier — CNN
+
+## Model description
+A 1D Convolutional Neural Network that classifies short wearable-sensor
+sequences into three physical activities: **Stationary**, **Walking**,
+and **Running**. Built as part of a beginner deep learning group lab
+comparing CNN, SimpleRNN, LSTM, and a CNN+LSTM hybrid on the same
+fixed dataset.
+
+## Intended use
+Educational demonstration of sequence classification on wearable
+sensor data. Not intended for production health/fitness monitoring.
+
+## Architecture
+
+Input (100 time steps, 1 sensor channel)
+→ Conv1D(32 filters, kernel_size=5, activation="relu")
+→ MaxPooling1D(pool_size=2)
+→ Flatten()
+→ Dense(32, activation="relu")
+→ Dense(3, activation="softmax")
+
+**Total parameters:** 49,475
+
+## Training data
+Fixed `Wearable_Activity_Dataset` release (seed 42 split): 600 training
+sequences, 150 validation, 150 test — each sequence is 100 time steps
+of a single sensor reading. Training set is perfectly class-balanced
+(200 Stationary / 200 Walking / 200 Running).
+
+## Training procedure
+- Optimizer: Adam (default learning rate)
+- Loss: sparse categorical crossentropy
+- Epochs: 6, batch size: 32
+- Same training configuration used across all four models in this lab,
+  for a fair comparison
+
+## Evaluation results
+
+| Model | Test Accuracy | Parameters | Train Time (s) |
+|---|---|---|---|
+| CNN | 1.000 | 49,475 | 2.81 |
+| SimpleRNN | 0.580 | 1,187 | 4.77 |
+| LSTM | 0.693 | 4,451 | 7.12 |
+| CNN+LSTM (hybrid) | 1.000 | 8,611 | 6.35 |
+
+## Limitations
+- Trained on a small, synthetic/fixed dataset — accuracy may not
+  generalize to real-world wearable sensor data with more noise,
+  sensor drift, or additional activity classes
+- Only 6 training epochs — SimpleRNN and LSTM in particular likely
+  hadn't converged; their reported accuracy understates what they
+  could achieve with more training
+- Fixed 100-step sequence length — not tested on longer or
+  variable-length sequences
+
+## How to use
+```python
+import tensorflow as tf
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense
+
+model = Sequential([
+    Conv1D(32, kernel_size=5, activation="relu", input_shape=(100, 1)),
+    MaxPooling1D(pool_size=2),
+    Flatten(),
+    Dense(32, activation="relu"),
+    Dense(3, activation="softmax")
+])
+model.load_weights("activity_model.weights.h5")
+
+# X: numpy array of shape (n_samples, 100, 1)
+predictions = model.predict(X)
+```
+
+## Authors
+Group lab submission — [Group 6 - Iqra University Main Campus], CNN–RNN–LSTM Model
+Challenge, Beginner Deep Learning Group Lab.