U.S. Flight Data Analysis & Simulation
Budget: $30 – $250 USD
The project is split into two main parts:
1. Simulation Algorithm (Part 1)
You’ll implement a simulation using the Metropolis-Hastings algorithm to generate samples from a Laplace distribution. Then you’ll check if the algorithm converges properly and estimate things like the mean and variance from the samples.
2. Flight Data Analysis (Part 2)
You’ll work with a huge dataset of U.S. flight records. The goal is to analyze delays, find patterns (like which days/times are best), check if older planes get delayed more, and build a logistic regression model to predict flight diversions.
What you’ll need:
• Use both R and Python for the whole project.
• Be comfortable with data cleaning, visualizations, and statistical modeling.
• Should know how to explain your process and results clearly in a report.
Deadline: March 28, 2025
1. Simulation Algorithm (Part 1)
You’ll implement a simulation using the Metropolis-Hastings algorithm to generate samples from a Laplace distribution. Then you’ll check if the algorithm converges properly and estimate things like the mean and variance from the samples.
2. Flight Data Analysis (Part 2)
You’ll work with a huge dataset of U.S. flight records. The goal is to analyze delays, find patterns (like which days/times are best), check if older planes get delayed more, and build a logistic regression model to predict flight diversions.
What you’ll need:
• Use both R and Python for the whole project.
• Be comfortable with data cleaning, visualizations, and statistical modeling.
• Should know how to explain your process and results clearly in a report.
Deadline: March 28, 2025
Related categories:
Python
Statistics
Machine Learning (ML)
R Programming Language
Statistical Analysis