Flight_delay_prediction

Inspiration

Flight delays are a common issue in air travel that affects passengers and airline operations. We wanted to explore how machine learning could help predict delays in advance so that travelers and airlines can make better decisions.

What it does

Flight Delay Prediction is a machine learning application that predicts the arrival delay of flights based on flight data. Users can upload a dataset, and the system trains a model to predict delays and display results along with evaluation metrics and visualizations.

How we built it

We built the project using Python for data processing and machine learning. The dataset was handled using Pandas, and a Linear Regression model from Scikit-learn was used to train and generate predictions. We created an interactive interface using Streamlit so users can upload datasets and view predictions easily.

Challenges we ran into

One of the main challenges was preparing the dataset and selecting appropriate features for training the model. We also faced issues while deploying the application and managing required dependencies for the Streamlit environment.

Accomplishments that we're proud of

We successfully built a working machine learning model that can predict flight delays and created a user-friendly interface to interact with it. Deploying the project online and integrating evaluation metrics and visualizations was also a major achievement.

What we learned

Through this project, we learned about the complete machine learning workflow including data preprocessing, model training, evaluation metrics, and deploying applications using Streamlit.

What's next for Flight_delay_prediction

n the future, we plan to improve the model by using larger real-world datasets and more advanced machine learning algorithms. We also aim to include additional factors such as weather conditions and airline traffic to improve prediction accuracy.

Built With

• matplot
• numpy
• pandas
• python
• scikit-learn
• streamlit