HeatShift

About the Project — HeatShift Pakistan Inspiration

Pakistan is increasingly experiencing extreme temperatures and heatwaves, affecting public health, outdoor workers, and everyday life. We wanted to explore how Artificial Intelligence, Machine Learning, and historical climate data** could help people better understand temperature patterns and make informed decisions.

This inspired us to develop HeatShift Pakistan, a data-driven temperature forecasting and heat-awareness platform focused on Karachi, Lahore, and Multan.

What It Does

HeatShift Pakistan combines historical weather data, machine learning, and an interactive dashboard to provide meaningful temperature insights.

Our project includes:

24-Hour Temperature Prediction:Predicts station temperatures using historical observations.
Interactive Dashboard: Visualizes temperature patterns, predictions, and model comparisons.
Multiple Cities: Focuses on Karachi, Lahore, and Multan.
Data Transparency: Displays data quality, missing readings, and available historical information.
Heat Awareness: Connects temperature insights with evidence-based heat safety guidance.
User-Friendly Interface: Provides temperature unit conversion, dark mode, and downloadable results.

How We Built It

We developed the project using **Python, Machine Learning, and data visualization technologies.

Data Collection: Gathered historical weather observations from NOAA and used Open-Meteo data for additional comparisons.
Data Processing: Cleaned and organized weather records using Python, Pandas, DuckDB, and Parquet while preserving missing observations.
Machine Learning: Evaluated Ridge Regression against a persistence baseline for predicting temperatures 24 hours ahead.
Model Evaluation: Used Mean Absolute Error (MAE) and chronological data splitting to assess forecasting performance.
AI Integration : Incorporated an AI-assisted component for presenting relevant heat-awareness guidance.
Dashboard Development : Built an interactive interface using Gradio, with deployment through Hugging Face Spaces.

Challenges We Faced

One of our biggest challenges was working with real-world weather data. Some observations were missing, inconsistent, or delayed, making data cleaning and validation essential.

Another challenge was ensuring that our machine learning model produced meaningful results without accidentally using future information during training.

We also encountered difficulties integrating different project components, managing deployment requirements, and making technical information understandable through a simple dashboard.

These challenges encouraged us to focus on reliability, transparency, and responsible AI development.

What We Learned

Throughout this project, we gained practical experience in:

Collecting, cleaning, and validating real-world datasets.
Building and evaluating machine learning forecasting models.
Understanding the importance of preventing data leakage.
Integrating AI components into a functional application.
Developing interactive dashboards and deploying applications.
Collaborating as a team and solving real-world technical problems.

Most importantly, we learned that building reliable AI solutions is not just about achieving high accuracy—it is also about data quality, transparency, and practical usefulness.

What's Next?

We aim to improve HeatShift Pakistan by expanding coverage to more Pakistani cities, exploring additional forecasting models, incorporating more weather-related features, and improving the dashboard experience.

Our long-term vision is to make HeatShift Pakistan a more accessible and reliable platform for understanding temperature changes and supporting heat-risk awareness.

Conclusion

HeatShift Pakistan is our attempt to turn climate data into accessible, meaningful information through AI and Machine Learning.**

By combining temperature forecasting, data visualization, and evidence-based guidance, we hope to contribute toward a more climate-aware and resilient Pakistan.