This repository hosts a Flight delay Prediction model built using supervised machine learning techniques. The model analyzes historical customer data to identify patterns and factors influencing flight delay.
- Data Exploration: We dive into the dataset to understand the distribution of features, identify correlations, and explore patterns within the data.
- Data Preprocessing: Data cleaning, handling missing values, and feature engineering are performed to prepare the dataset for model training.
- Model Selection: Multiple machine learning algorithms are considered, including Logistic Regression, Gradient boosting, and Naive Bayes model. The best-performing model is selected based on evaluation metrics.
- Hyperparameter Tuning: Optimized model performance through hyperparameter tuning for enhanced accuracy, precision, and recall.
- Model Evaluation: Model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to gauge the model's predictive power.
- Predictive Insights: We provide insights into the most influential features contributing to Flight delay and developed a prediction model.
- Open the Jupyter Notebook to access the project code and analysis.
- Run the notebook to perform data exploration, preprocessing, model training, and evaluation.
Contributions are welcome! Feel free to open an issue or create a pull request.