Power BI EDA

404. Gas Not Found

Inspiration

We’re passionate about data science and love spending time together, helping each other grow. Tackling real-world problems gives us the chance to apply our skills in meaningful ways that can drive impact, and the vehicle population prediction project is the perfect example.

What It Does

Our project predicts vehicle population using both numerical and categorical data. We apply data preprocessing techniques such as one-hot encoding, min-max scaling, and standard scaling to optimize our functions and improve prediction accuracy.

How We Built It

We used libraries like:

scikit-learn
pandas
numpy

For modeling, we implemented regression models and random forests to make predictions, where we used RMSE to evaluate model performance and measure the prediction error.

Challenges We Ran Into

Manipulating and understanding the dataset was challenging, as it wasn’t entirely clear.
Finding the right relationships between variables and optimizing functions for better performance also presented difficulties, but we powered through with perseverance.

Accomplishments That We're Proud Of

Achieving a low RMSE, improving prediction accuracy.
Building a beautiful front-end interface thanks to Insiya and Maya.
Adding a personalized touch to show outreach and impact from this project.

What We Learned

Gained hands-on experience with optimization techniques such as one-hot encoding and scaling to improve data manipulation.
Conducted deep analysis using Power BI.
Developed front-end UI using React.js.

What's Next for 404. Gas Not Found?

We’re excited to participate in future hackathons and collaborate with student organizations. Our ultimate goal is to expand our reach, share our findings, and network with others who are passionate about data science to make a positive impact on the world.