Final Project

Overview:

Euroconsult, a consulting firm specializing in space markets, presented two projects to Ironhack. The first project involved creating an interactive dashboard to provide an overview of satellite activities and budgets for each country. The second project focused on modeling the satellite budget per country for the next 10 years based on the budgets from the previous 22 years. Two datasets were provided by Euroconsult and were cleaned and adapted as needed. The budget dataset consisted of 97,459 rows and 10 columns, while the satellites dataset had 2,500 rows and 9 columns. Various Python libraries were used for data analysis and visualization, including Pandas, Numpy, Matplotlib.pyplot, Seaborn, Plotly, Flask, and Scikit-Learn.

Features:

• Interactive dashboard to provide an overview of satellite activities and budgets for each country
• Modeling of satellite budgets per country for the next 10 years based on historical data
• Data cleaning and adaptation including aggregation, pivot tables, and dummy variables
• Machine learning models used for data modeling and prediction

Summary:

Euroconsult approached Ironhack with two projects: creating an interactive dashboard for satellite activities and budgets, and modeling satellite budgets for the next 10 years. The datasets provided were cleaned and adapted using Python libraries like Pandas and Numpy. The dashboard was built using Plotly and Flask, while Scikit-Learn was used for machine learning modeling. The project's current state shows that the dashboard is operational, but the machine learning model needs more data or a different approach to improve its prediction capability. The suggestion is to explore using RBF neural networks for further predictive analysis.