Inspiration
Cities generate vast amounts of service request data, yet inefficiencies in response times persist. We aimed to leverage data science to optimize city services, making urban management smarter and more proactive.
What it does
Our project analyzes historical service request data to identify trends, predict response times, and optimize resource allocation for city agencies. This enables faster resolutions and improved service efficiency.
How we built it
We processed and cleaned service request data, applied exploratory data analysis (EDA), and leveraged machine learning models to predict response times. Visualization tools like Matplotlib and Seaborn were used to present insights, while Prophet helped with seasonal forecasting.
Challenges we ran into
Handling missing and inconsistent data Identifying the most impactful features for prediction Optimizing model accuracy for real-world application Balancing computational efficiency with predictive power
Accomplishments that we're proud of
Successfully cleaning and structuring a large dataset for analysis Building predictive models to estimate response times Generating insightful visualizations for better decision-making Identifying key trends that could improve city service management
What we learned
Advanced data preprocessing techniques The importance of feature selection in predictive modeling How different machine learning models perform on time-series data Effective ways to visualize urban service trends for stakeholders
What's next for Datathon-proj-01
Enhancing model accuracy with additional external factors (e.g., weather, events) Deploying the model into a real-time dashboard for city officials Exploring reinforcement learning for optimizing city resource allocation Expanding the analysis to multiple cities for broader impact
Built With
- google-collab
- python
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