Challenge 15 - Rocket Landing Success Analysis

<p><strong>Challenge 15: Rocket Landing Success Analysis</strong></p><p><strong>Level:</strong> Easy</p><p><strong>Description:</strong><br>You are a data analyst working for a private space exploration company analyzing the landing performance of its flagship rocket. Your task is to understand what factors drive successful first-stage landings and generate insights that could improve future landing attempts.</p><p><em>Beginner-friendly objectives:</em></p><ol><li><p>Parse the JSON file (launches.json) and extract the following fields:</p><ul><li><p>flight_number, name, date_utc, success, rocket (ID)</p></li><li><p>payloads[0] (only the first payload per mission)</p></li><li><p>cores[0].gridfins, cores[0].legs, cores[0].reused, cores[0].landing_attempt, cores[0].landing_success</p></li></ul></li><li><p>Retain records where landing_attempt AND mission success is true (focus on actual landing attempts from successful missions), and enrich the dataset by:</p><ul><li><p>Using Rocket ID to fetch rocket_name from Rocket_Details.csv</p></li><li><p>Using the Payload ID to fetch payload_mass_kg and orbit from Payload_Details.csv</p></li></ul></li><li><p>Clean and preprocess the integrated dataset by:</p><ul><li><p>Handling missing or null values appropriately</p></li><li><p>Focus analysis on missions involving "Explorer XI" (i.e., rocket_001)</p></li><li><p>Preparing data for visualization</p></li></ul></li><li><p>Create visualizations: Show total landing successes, total flights, max payload mass, and percentage of missions with core reuse. Visuals should be arranged in a dashboard layout within a component.</p></li></ol><p><strong>Solution Summary</strong>: We start our solution by reading and transforming JSON and CSV data, followed by data aggregation to calculate metrics like landing success rates. We also add interactive charts and graphs to provide insights into mission outcomes.<br><br><strong>Solution Details:</strong> The workflow starts with the JSON Reader node to load the `launches.json` file, followed by two instances of the CSV Reader node to import `Rocket_Details.csv` and `Payload_Details.csv`. The JSON Path node is used to extract key fields from the JSON data, creating new columns for analysis. Next, the Ungroup node converts collections into individual rows, facilitating further data manipulation. Data aggregation is performed using instances of the GroupBy node, calculating metrics such as mean landing success rates and payload capacities. We then use the Rule Engine node to categorize missions based on hardware configurations. We create visualizations with a few different nodes, e.g., Generic ECharts View and Line Plot. They allow us to implement interactive charts displaying mission data insights.</p>