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README.md

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+# Human Activity Recognition with LSTM
+
+## Overview
+This project focuses on **Human Activity Recognition (HAR)** using **LSTM-based neural networks**. The goal is to classify different human activities based on motion sensor data.
+
+### Dataset Used
+The model is trained on the **UCI HAR Dataset**, a widely used benchmark dataset for human activity recognition. It contains data collected from accelerometers and gyroscopes of smartphones while subjects performed daily activities.
+
+## Model Performance
+### Classification Report
+Below are the precision, recall, and F1-score for each activity class:
+
+
+         precision    recall  f1-score   support
+
+ Class 0       0.92      0.98      0.95       496
+ Class 1       0.95      0.91      0.93       471
+ Class 2       0.98      0.95      0.96       420
+ Class 3       0.92      0.94      0.93       491
+ Class 4       0.94      0.93      0.94       532
+ Class 5       1.00      0.99      1.00       537
+
+accuracy                           0.95      2947
+
+macro avg 0.95 0.95 0.95 2947 weighted avg 0.95 0.95 0.95 2947
+
+### Confusion Matrix
+The confusion matrix below visualizes the model's performance in classifying different activities:
+
+![Confusion Matrix](https://huggingface.co/MicS2/Human-Activity-Recognition/Figure_1.png)
+
+## Next Steps
+- Improve the model with **GRU & CNN architectures**.
+- Expand testing with **real-world sensor data**.
+- Fine-tune hyperparameters for better generalization.
+
 ---
 license: mit
 ---