Update README.md

README.md

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 metrics:
 - accuracy
 pipeline_tag: tabular-classification
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+---
+# Human Activity Recognition with CNN-LSTM and Residual Connections
+
+## 📌 Overview
+This project implements a **Human Activity Recognition (HAR)** model using the **WISDM dataset**.  
+The dataset consists of accelerometer and gyroscope sensor data collected from smartphones.  
+The proposed model combines **Convolutional Neural Networks (CNNs)** with **Long Short-Term Memory networks (LSTMs)** and integrates **residual connections** to enhance feature extraction and improve gradient flow.
+
+The model achieved **~94% accuracy** with augmentation applied, making it suitable for academic and engineering exploration.
+
+---
+
+## 🎯 Motivation
+- Human activities generate sequential time-series signals from sensors.  
+- Classical ML methods often fail to capture **temporal dependencies** and **complex motion patterns**.  
+- This project leverages:
+  - **CNNs** for local temporal feature extraction.  
+  - **Residual connections** for stable training of deeper CNNs.  
+  - **LSTMs** for long-term sequential modeling.  
+
+---
+
+## 🛠️ Data Preprocessing
+- **Dataset:** [WISDM (Wireless Sensor Data Mining)](https://www.cis.fordham.edu/wisdm/dataset.php).  
+- **Challenge:** Class imbalance in activity distribution.  
+- **Solution:**  
+  - Applied **data augmentation** using the **jittering technique** (adding Gaussian noise).  
+  - Augmentation performed on both training and test sets to increase variability and improve robustness.  
+
+---
+
+## 🏗️ Model Architecture
+The CNN-LSTM model with residual connections is structured as follows:
+
+1. **Input Layer**  
+   - Time-series windows from accelerometer & gyroscope data.  
+
+2. **CNN Block 1**  
+   - Conv1D (64 filters, kernel size 5) + BatchNorm + Dropout.  
+   - Residual Conv1D (64 filters) added back to the block output.  
+
+3. **CNN Block 2**  
+   - Conv1D (128 filters) + BatchNorm + Dropout.  
+
+4. **LSTM Layer**  
+   - LSTM (128 units) to capture long-term dependencies.  
+
+5. **Dense Layers**  
+   - Dense (128 units, ReLU) + Dropout.  
+   - Final Dense layer with **Softmax activation** for multi-class classification.  
+
+---
+
+## ⚙️ Training Strategy
+- **Cross-validation:** 5-fold **Stratified K-Fold**.  
+- **Optimizer:** Adam.  
+- **Loss Function:** Sparse categorical cross-entropy.  
+- **Regularization:**  
+  - L2 weight decay.  
+  - Dropout.  
+  - Early stopping & ReduceLROnPlateau.  
+- **Evaluation Metrics:**  
+  - Accuracy.  
+  - Macro F1-score.  
+  - Detailed classification reports per fold.  
+
+---
+ 
+
+## 📊 Results
+- Achieved an average **accuracy of ~94%** on the WISDM dataset.  
+- Residual connections and the CNN-LSTM hybrid improved performance over standalone CNN or LSTM baselines.  
+- Macro F1-scores across folds showed **balanced classification across both majority and minority activity classes**.  
+
+---
+## ⚠️ Notes
+- This work was conducted for **academic and experimental purposes**.  
+- Results are not intended as an official benchmark submission.  
+- Data augmentation (jittering) was applied to improve robustness but may not reflect standardized evaluation protocols.
+  
+## 📖 References
+- WISDM Dataset: [https://www.cis.fordham.edu/wisdm/dataset.php](https://www.cis.fordham.edu/wisdm/dataset.php)  
+- Residual Networks: K. He, et al. *Deep Residual Learning for Image Recognition*. CVPR 2016.  
+- CNN-LSTM applications in HAR: Ordóñez & Roggen. *Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition*. Sensors 2016.  
+## Requirements
+
+This project requires Python 3.8+ and the following Python libraries:
+
+- [TensorFlow](https://www.tensorflow.org/) (for building and training the CNN-BiLSTM model)
+- [Keras](https://keras.io/) (high-level API for neural networks, integrated with TensorFlow)
+- [NumPy](https://numpy.org/) (numerical computations)
+- [Pandas](https://pandas.pydata.org/) (data handling and preprocessing)
+- [Matplotlib](https://matplotlib.org/) (visualization, e.g., loss curves, PCA plots)
+- [Scikit-learn](https://scikit-learn.org/) (PCA, preprocessing, metrics)
+- [Seaborn](https://seaborn.pydata.org/) (optional, for enhanced plots)
+- [tqdm](https://tqdm.github.io/) (optional, progress bars for training loops)
+
+## Installation (via pip)
+
+```bash
+pip install tensorflow keras numpy pandas matplotlib scikit-learn seaborn tqdm
+