IBM-Data-Science-Capstone-Project

Introduction

Background

SpaceX, a leader in the space industry, strives to make space travel affordable for everyone. Its accomplishments include sending spacecraft to the international space station, launching a satellite constellation that provides internet access and sending manned missions to space. SpaceX can do this because the rocket launches are relatively inexpensive ($62 million per launch) due to its novel reuse of the first stage of its Falcon 9 rocket. Other providers, which are not able to reuse the first stage, cost upwards of $165 million each. By determining if the first stage will land, we can determine the price of the launch. To do this, we can use public data and machine learning models to predict whether SpaceX – or a competing company – can reuse the first stage.

Explore

How payload mass, launch site, number of flights, and orbits affect first-stage landing success
Rate of successful landings over time
Best predictive model for successful landing (binary classification)

Executive Summary

The research attempts to identify the factors for a successful rocket landing. To make this determination, the following methodologies where used:

Collect data using SpaceX REST API and web scraping techniques
Wrangle data to create success/fail outcome variable
Explore data with data visualization techniques, considering the following factors: payload, launch site, flight number and yearly trend
Analyze the data with SQL, calculating the following statistics: total payload, payload range for successful launches, and total # of successful and failed outcomes
Explore launch site success rates and proximity to geographical markers
Visualize the launch sites with the most success and successful payload ranges
Build Models to predict landing outcomes using logistic regression, support vector machine (SVM), decision tree and K-nearest neighbor (KNN)

Results

Exploratory Data Analysis:

Launch success has improved over time
KSC LC-39A has the highest success rate among landing sites
Orbits ES-L1, GEO, HEO, and SSO have a 100% success rate

Visualization / Analytics:

Most launch sites are near the equator, and all are close to the coast

Predictive Analytics

All models performed similarly on the test set. The decision tree model slightly outperformed when looking at .best_score_

Methodology

Data Collection - API

Request data from SpaceX API (rocket launch data)
Decode response using .json() and convert to a dataframe using .json_normalize()
Request information about the launches from SpaceX API using custom functions
Create dictionary from the data
Create dataframe from the dictionary
Filter dataframe to contain only Falcon 9 launches
Replace missing values of Payload Mass with calculated .mean()
Export data to csv file

Data Collection - Web Scraping

Request data (Falcon 9 launch data) from Wikipedia
Create BeautifulSoup object from HTML response
Extract column names from HTML table header
Collect data from parsing HTML tables
Create dictionary from the data
Create dataframe from the dictionary
Export data to csv file

Data Wrangling

Convert outcomes into 1 for a successful landing and 0 for an unsuccessful landing

EDA with Visualization

Create charts to analyze relationships and show comparisons

EDA with SQL

Query the data to understand more about the data

Maps with Folium

Create maps to visualize launch sites, view launch outcomes and see distance to proximities

Dashboard with Plotly Dash

Create dashboard
Pie chart showing successful launches
Scatter chart showing Payload Mass vs. Success Rate by Booster Version

Predictive Analytics

Create NumPy array from the Class column
Standardize the data with StandardScaler. Fit and transform the data.
Split the data using train_test_split
Create a GridSearchCV object with cv=10 for parameter optimization
Apply GridSearchCV on different algorithms: logistic regression (LogisticRegression()), support vector machine (SVC()), decision tree (DecisionTreeClassifier()), K-Nearest Neighbor (KNeighborsClassifier())
Calculate accuracy on the test data using .score() for all models
Assess the confusion matrix for all models
Identify the best model using Jaccard_Score, F1_Score and Accuracy

Conclusion

Model Performance: The models performed similarly on the test set with the decision tree model slightly outperforming
Equator: Most of the launch sites are near the equator for an additional natural boost - due to the rotational speed of earth - which helps save the cost of putting in extra fuel and boosters
Coast: All the launch sites are close to the coast
Launch Success: Increases over time
KSC LC-39A: Has the highest success rate among launch sites. Has a 100% success rate for launches less than 5,500 kg
Orbits: ES-L1, GEO, HEO, and SSO have a 100% success rate
Payload Mass: Across all launch sites, the higher the payload mass (kg), the higher the success rate

Additional Things to Consider

Dataset: A larger dataset will help build on the predictive analytics results to help understand if the findings can be generalizable to a larger data set
Feature Analysis / PCA: Additional feature analysis or principal component analysis should be conducted to see if it can help improve accuracy
XGBoost: Is a powerful model which was not utilized in this study. It would be interesting to see if it outperforms the other classification models

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