Flight Delay Prediction Project
Project Overview:
This project predicts flight delays using historical flight and weather data. The focus is on building a machine learning model that incorporates probabilistic techniques such as Bayesian methods or Monte Carlo simulations to account for uncertainty caused by weather conditions.
Key Features:
Data Processing: Handling historical weather and flight data, cleaning, and preprocessing for modeling.
Machine Learning Model: Training predictive models to estimate flight delays.
Uncertainty Quantification: Employing probabilistic approaches to capture the effects of weather variability on predictions.
Visualization: Generating plots to explain model performance and uncertainty effects.
Report Generation: Documenting findings, including potential improvements and key insights.
Project Structure:
Data Loading & Exploration: Reading and understanding historical flight and weather datasets. Exploratory data analysis (EDA) to identify key features affecting flight delays.
Preprocessing:
Data cleaning: Handling missing values and anomalies.
Feature engineering: Creating meaningful input features. Normalization or scaling as required.
Modeling: Baseline model implementation.Advanced modeling using Bayesian inference or Monte Carlo methods to include uncertainty quantification.
Evaluation:Assessing model performance using metrics like RMSE, MAE, or others. Visualizing predictions with confidence intervals.
Visualization: Displaying model predictions, actual delays, and uncertainty bounds.
Results and Discussion: Interpreting the results, model strengths, and areas for improvement.
Requirements:
Languages and Tools:
- Python 3.x
- Jupyter Notebook
Libraries:
Data Analysis & Manipulation: pandas, numpy
Visualization: matplotlib, seaborn
Machine Learning: scikit-learn
Probabilistic Modeling: PyMC3 or TensorFlow Probability
Other Utilities: scipy, statsmodels
To install all required packages, run: pip install -r requirements.txt
Usage Instructions:
Clone the Repository: https://github.com/Dinesh-2311/predict_flight_delay_datasets/edit/master.git
Run the Notebook Open the Jupyter Notebook:** jupyter notebook PREDICT_FLIGHT_DELAY.ipynb
follow the Notebook Sections : Execute each cell sequentially for step-by-step implementation.
Deliverables:
Code: Fully functional Jupyter Notebook.
Visualization: Figures showing model performance and uncertainty.
Report: Discussion of results, uncertainty quantification, and improvement recommendations.
Future Work:
Enhance feature engineering with real-time data streams.
Experiment with additional probabilistic models for improved uncertainty quantification.
Integrate the model into a web-based decision-support system for real-world deployment.