Project27: smart parking demand prediction

Playground/prashantthapa/placeholder.txt

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+Added by Prashant Thapa (224946285)

Playground/project-27-smart-parking-demand-prediction/README.md

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+# Smart Parking Demand Prediction
+
+This project focuses on analysing Melbourne parking sensor data to understand parking patterns and predict parking demand across different zones and time periods. The goal is to support better parking management and reduce traffic caused by vehicles searching for parking.
+
+## Team Members
+- Prashant
+- Supriya
+- Rishab
+- Shree
+
+## Project Overview
+We are using Melbourne open parking data (arrivals, departures, and parking restrictions) to:
+- analyse parking trends
+- identify peak demand times
+- build a machine learning model to predict parking demand
+
+## Project Structure
+- `data/` → contains raw and processed datasets  
+- `notebooks/` → shared notebooks for data cleaning, analysis, and modelling  
+- `src/` → optional Python scripts for reusable code  
+- personal folders → individual work and experiments  
+
+## Main Notebook
+- `notebooks/05_final_pipeline.ipynb`  
+This notebook contains the complete workflow including data processing, analysis, and model results.
+
+## Workflow
+All team members are working on the same group branch. Each member is responsible for a specific part of the project such as data cleaning, analysis, feature engineering, or modelling. Work is first tested individually and then added to shared notebooks.
+
+## Current Progress
+- Initial dataset exploration completed  
+- Data cleaning in progress  
+- Feature extraction started  
+- Model development planned  
+
+## Notes
+- Always pull latest changes before pushing updates  
+- Avoid editing the same notebook at the same time  
+- Use personal folders for testing ideas before adding to shared files