Capstone-Project

SpaceX and Elon Musk's success are mostly based on the fact that their rockets are reusable resulting to cheaper launch projects. The purpose of this research is to build the best model to determine or classify if the first stage of a Falcon 9 rocket will land so that we can determine the cost of a launch. To achieve this, we have gathered data from SpaceX REST API and web scraping of a Wikipedia page that contains data regarding the said project. The data that was collected from the two sources were consolidated and cleaned. EDA was perfomred on the data using SQL and visualizations including folium to map out the locations of the different launch sites and their proximities to identify the optimal location for a launch. Additionally a Plotly Dash App was created to easily interact, navigate, and visualize the data. After leaning the features that are relevant in classifying the success of a launch such as payload weight, launch site locations, orbit type, booster versions, etc., different machine learning models were tested on different parameters to identify the best model in classifying launches that will succesfully land a first stage of a rocket. The best models were LR, SVM, and KNN, all having 94% accuracy score.