In this repo. i have a explained in detail about dask. Dask is a library that supports parallel computing in python. and how to use it. with project. explanation is available in my youtube channel. Flight Delay Model using Dask This is a project to build a machine learning model to predict flight delays using Dask, a parallel computing library for Python. The model is built using a dataset of flight information, which includes features such as flight date, airline, departure and arrival times, and distance. The goal is to predict whether a given flight will be delayed by 15 minutes or more, based on these features.

To run the project, you'll need to install Python 3 and the following libraries:

Dask Scikit-learn Pandas Matplotlib

pip install dask scikit-learn pandas matplotlib

Once you have installed the required libraries, you can clone the repository and run the code:

git clone https://github.com/saikumar0605/Dask-ml.git python flight_delay_model.py The code will train the model using the flight data, split the data into training and testing sets, and evaluate the model's performance using accuracy and AUC metrics.

it's avaliable on the repo

The machine learning model used in this project is a logistic regression model, implemented using scikit-learn. The model is trained on the flight data using Dask, which allows for efficient parallel computing on large datasets. The model is evaluated using accuracy and AUC metrics, and can be used to predict flight delays based on the input features.

The model achieved an accuracy of 70% . can be improved Conclusion This project demonstrates how Dask can be used to build a machine learning model on large datasets, such as flight data. The model can be used to predict flight delays with reasonable accuracy, which can be useful for airlines, passengers, and other stakeholders.