Data Science

Predicting Falcon 9 Reusability

Classification pipeline for first-stage landing outcomes

Overview

A data science workflow predicting whether a Falcon 9 first stage will land successfully, using feature engineering, exploratory analysis, and model comparison.

Problem

Reusable first-stage boosters can reduce launch costs, but landing outcomes depend on launch profile, payload, orbit, and historical flight context. The project frames landing prediction as a supervised classification problem.

Dataset

Public SpaceX API data ingested with Python Requests and transformed with Pandas into launch-level model features.

Approach

Automated data ingestion, engineered mission-level features, built Scikit-learn classification pipelines, and applied stratified sampling for class imbalance.

Results

Improved accuracy from 86% to 98% and reached a 96% F1 score while keeping the workflow reproducible.

Lessons Learned

Strong predictive workflows need both model performance and trustworthy data lineage. Careful feature definitions mattered as much as the final classifier.

Model / Pipeline

The implementation combines Python, Pandas, Scikit-learn, Requests, REST API with a repeatable workflow for data preparation, evaluation, and communication.