✈️ Flight Delay and Cancellation Analysis

This project provides a detailed analysis of U.S. domestic flight delays and cancellations using 2015 data. It covers data cleaning, exploratory data analysis (EDA), feature engineering, and predictive modeling to uncover insights and forecast flight disruptions effectively.

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📁 Project Structure

├── data/
│   ├── flights.csv       # Flight data
│   ├── airlines.csv      # Airline information
│   └── airports.csv      # Airport details
├── _mywork_1_1.ipynb     # Jupyter Notebook with analysis and modeling
└── README.md             # Project documentation

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🧠 Objectives

• Analyze flight delay and cancellation patterns.
• Identify key factors contributing to delays and cancellations.
• Develop predictive models to classify and forecast delayed or canceled flights.

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📊 Techniques & Tools Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn
• Processes:
  • Data Cleaning & Preprocessing
  • Exploratory Data Analysis (EDA)
  • Feature Engineering
  • Predictive Modeling

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📌 Key Steps

1. Data Loading
   Import flight, airline, and airport datasets from CSV files.

2. Data Cleaning

   • Handle missing values.
   • Drop irrelevant columns.
   • Convert data types.
   • Clean and preprocess categorical features.

3. EDA

   • Create visualizations to identify trends and anomalies.
   • Perform statistical summaries to understand data distributions.

4. Feature Engineering

   • Extract meaningful features such as day of the week, delay types, cancellation reasons, and time-based patterns.

5. Modeling

   • Train classification models (e.g., Logistic Regression, Decision Trees) to predict delays or cancellations.

6. Evaluation

   • Use metrics like accuracy, precision, recall, and confusion matrix to assess model performance.

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📈 Insights

• Delays: More frequent during specific hours and months.
• Cancellations: Often caused by weather and carrier-related issues.
• Key Predictors: Time of day, flight distance, and carrier name significantly influence delays and cancellations.

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🚀 Getting Started

1. Clone the repository or download the notebook.
2. Place the dataset files (flights.csv, airlines.csv, airports.csv) in the data/ folder.
3. Open _mywork_1_1.ipynb in Jupyter Notebook or VSCode.
4. Execute the notebook step by step to reproduce the analysis and results.

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📌 Requirements

• Python: Version 3.8 or higher
• Environment: Jupyter Notebook
• Dependencies:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn

Install the required packages using:

pip install -r requirements.txt

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🌟 Future Work

• Extend the analysis to include more recent datasets.
• Explore advanced machine learning models for better predictions.
• Develop a web-based dashboard for real-time delay and cancellation monitoring.

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