-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
1 changed file
with
47 additions
and
0 deletions.
There are no files selected for viewing
47 changes: 47 additions & 0 deletions
47
Applied Data Science Specialization/Applied Data Science Capstone/README.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,47 @@ | ||
| # SpaceX Falcon 9 First Stage Landing Prediction | ||
|
|
||
| ## Overview | ||
|
|
||
| This project predicts the landing success of SpaceX Falcon 9's first stage to analyze cost-efficiency and improve reusable rocket technology. The goal is to assess factors influencing the success of first-stage landings, such as payload, orbit type, and launch site, and to determine the best-performing predictive model. | ||
|
|
||
| ## Objectives | ||
|
|
||
| - **Exploratory Data Analysis (EDA):** Explore patterns and relationships in SpaceX's launch data using SQL, interactive maps, and visualizations. | ||
| - **Predictive Analysis:** Build, tune, and evaluate classification models to predict landing success. | ||
| - **Insights for SpaceX:** Understand factors like orbit type, payload mass, and launch site trends to aid decision-making. | ||
|
|
||
| ## Project Highlights | ||
|
|
||
| ### 1. **Data Collection and Preparation** | ||
| - Data was gathered via: | ||
| - **API Calls:** SpaceX's API provided structured data on launches. | ||
| - **Web Scraping:** Extracted supplementary data from Wikipedia using BeautifulSoup. | ||
| - Preprocessing steps included cleaning, encoding categorical variables, and standardizing numeric data. | ||
|
|
||
| ### 2. **Exploratory Data Analysis (EDA)** | ||
| - SQL queries and visualizations provided key insights: | ||
| - **Launch Sites:** Most launches occurred at CCAFS SLC-40, followed by KSC LC-39A. | ||
| - **Payload Analysis:** Heavier payloads were associated with higher failure rates, particularly at certain launch sites. | ||
| - **Orbit Type Challenges:** Higher-altitude orbits (e.g., GTO) posed greater mission challenges. | ||
|
|
||
| ### 3. **Predictive Modeling** | ||
| - Several classification models were built and compared: | ||
| - **Decision Tree:** Achieved the highest test accuracy (88.8%), demonstrating strong generalization and reliability. | ||
| - Other models like Logistic Regression, SVM, and KNN performed reasonably well (~83.3% test accuracy). | ||
| - **Confusion Matrix Analysis:** Highlighted false positives as a challenge for some models. | ||
|
|
||
| ### 4. **Key Insights** | ||
| - SpaceX's landing success improved significantly over time, showcasing the effectiveness of iterative development. | ||
| - Payload mass and orbit type were critical factors influencing mission outcomes. | ||
| - Decision Tree proved to be the most effective model for predicting landing success. | ||
|
|
||
| ## Repository Contents | ||
|
|
||
| - **Presentation Slides:** A detailed summary of the methodology, results, and conclusions. | ||
| - **Code:** Scripts for data collection, preprocessing, EDA, and modeling. | ||
|
|
||
| ## Conclusions | ||
|
|
||
| - SpaceX's iterative development approach has significantly enhanced landing success rates. | ||
| - Predictive modeling provides valuable insights for optimizing future missions. | ||
| - Decision Tree stands out as the most reliable model for predicting landing outcomes. |