Climate Trend Predictor App

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  CLIMATE TREND PREDICTOR APP

I created ClimateTrend Predictor to better understand climate change and make key climate data accessible to everyone. Seeing rising CO₂ levels, increasing global temperatures, and rising sea levels inspired me to build a tool that combines historical data with predictive modeling in a simple, interactive interface.

What I Learned

Through this project, I learned how to:

Clean and Analyze Data: Handle missing values, format time-series data, and explore trends using Python and pandas.

Build Predictive Models: Apply linear regression to forecast future climate metrics and evaluate model accuracy.

Visualize Data: Create clear, interactive plots using matplotlib and Plotly.

Develop Web Apps: Build a user-friendly interface with Streamlit to display trends and predictions.

How I Built It

Collected NASA datasets for CO₂, temperature, and sea level.

Cleaned and prepared the data for modeling.

Used linear regression to forecast the next 20–30 years.

Created interactive visualizations comparing actual and predicted data.

Integrated everything into a Streamlit app for easy exploration.

Challenges Faced

Dealing with missing or inconsistent data in historical datasets.

Ensuring forecasts remained meaningful despite non-linear trends.

Designing an intuitive, responsive interface that combined data, predictions, and charts seamlessly.

Outcome

ClimateTrend Predictor transformed complex climate datasets into an intuitive, interactive, and actionable tool. I’m proud that it allows anyone to explore past trends and understand potential future scenarios in a visually engaging way.

Built With

• csv
• datasetspython
• github
• matplotlib
• nasa
• numpy
• numpy-machine-learning:-scikit-learn-(linear-regression)-visualization:-matplotlib
• pandas
• plotly
• python
• scikit-learn
• streamlit