Update README.md

hamzariffic · Feb 3, 2024 · 7528a23 · 7528a23
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-**Overview
+Overview
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 The Smart HomeGuard Capstone Project aims to develop an innovative home security solution. The project integrates an Android app, machine learning models, cloud-based APIs, and various libraries to create a cutting-edge security system for residential properties.
 
 *Key Components*
 1. Android App: The Android app serves as the user interface, providing real-time property surveillance, notification alerts, and control.
 
-2. Machine Learning Models: Machine learning models will help to classify individuals and objects, specifically focusing on intruder detection.
+2. Machine Learning Models: Machine learning models will help to classify individuals and objects, specifically focusing on intruder detection. 
 
 3. Cloud-Based API: A cloud-based APIs from Azure, AWS or GCP will be used to host the machine learning models and expose endpoints for real-time data communication.
 
@@ -26,15 +26,20 @@ Evaluate models for accuracy and reliability in real-world scenarios.
 Set up cloud infrastructure on GCP/AWS for hosting models.
 Deploy machine learning models to the cloud server.
 Create and expose API endpoints for real-time communication.
-4. **Integration and Testing**:
-Integrate the Android app with the cloud-based API for real-time data exchange.
-Perform unit and integration tests for app components, models, and API.
-Validate detection accuracy and responsiveness in real-world scenarios.
-5. **Documentation and Deployment**:
+
+
+5. **Integration and Testing**:
+
+
+
+6. **Documentation and Deployment**:
 Prepare user documentation for app usage and features.
 Deploy the Android app to an emulator or physical device.
 Ensure proper deployment and accessibility of the cloud-based API.
 
+7. **Demo**
+Soon!
+
 **Goals and Impact**
 The Smart HomeGuard Capstone Project aims to revolutionize home security by leveraging advanced technologies to provide homeowners with an intelligent, proactive, and reliable security system. The project's success will be measured by achieving accurate detection, real-time monitoring, and seamless communication between the Android app, machine learning models, and cloud-based API.