Water Conservation through Predictive Analysis

Developed a predictive model using Python, R, and SQL to analyze water usage patterns across major agricultural zones. The model forecasted water demand based on weather data, crop type, and historical consumption patterns.

Using tools like Matplotlib and Seaborn, I visualized the data to show regions at risk of water scarcity. This led to the implementation of targeted water conservation strategies, saving an estimated 20% in water usage in high-risk zones.

My cross-sector insights were crucial in understanding the nuanced demands of both agriculture and water sectors. By effectively communicating these insights to local authorities and farmers, we achieved not just conservation but also an increase in crop yields in areas previously affected by water shortages.

Outcome:

Achieved a 20% reduction in water usage in targeted zones, increased crop yields, and bridged the communication gap between data scientists and non-technical stakeholders.