munshineel/ML-Project

Machine Learning Project on Flight Delay Prediction (Tunisian Airlines) # Flight Delay Prediction Using Machine Learning A Machine Learning project that predicts airline flight delays using historical flight operation data. This project applies the complete Data Science lifecycle, including Exploratory Data Analysis (EDA), feature engineering, model development, hyperparameter tuning, model evaluation, and deployment of a simple prediction interface. --- # Project Overview Flight delays are one of the most significant operational challenges faced by airlines and airports. Delays can lead to increased operational costs, inefficient resource utilization, passenger dissatisfaction, and disruptions across the transportation network. The objective of this project is to build a Machine Learning model capable of predicting flight delays before departure using historical flight data. --- # Problem Statement Given historical flight information, predict the expected flight delay duration in minutes. This is a **Supervised Machine Learning Regression Problem**, where: * **Input:** Flight operational features * **Output:** Delay duration in minutes --- ## Learning Objectives By the end of this repository, you should be able to: - Work on a data science project as a team, sharing the work across the data science lifecycle. - Frame a business problem as a machine learning task and choose a performance metric that fits the value you want to create. - Build a baseline model, then iterate with error analysis and at least three algorithms (including cross validation and hyperparameter tuning). - Communicate your findings and recommendations to a non-technical audience in a short presentation. --- # Dataset Description The dataset contains approximately **107,833 historical flight records**. | Feature | Description | | ------- | ------------------------ | | ID | Unique record identifier | | DATOP | Flight date | | FLTID | Flight number | | DEPSTN | Departure airport | | ARRSTN | Arri …