Flight Price Prediction in Python | Machine Learning

In this coding tutorial, we will perform Flight Price Prediction in Python. This machine learning model is built to predict the price of a flight based on given features. This model will help the users to select the best date and the best time to reach their destination according to their own utility.

Objectives:

The objective behind building this machine learning model is to predict the price of a house based on given features so that the users can select and plan their journey accordingly and can select the best-suited flight according to their own utility.
This model also helps the airline and travel industries to set a price system for different flights to grow their business model.

Requirements:

Python
Jupyter Notebook

Timestamp:

00:12 - Project Overview
00:45 - Code Explanation
05:23 - Demonstration

Explanation of the code:

Initially, we declared all the necessary libraries to build our model and loaded our dataset in our notebook.

Then we cleaned our dataset by dropping the null values through dropna() function.

Then we perform feature engineering and data pre-processing to get the features and to make our dataset ready for further analysis.

We have used the concept of one hot encoding and label encoding with the features.

Then we applied algorithms like random forest classifier, hyper parameter tuning.

Accordingly, we trained our model, and then we predicted the values accordingly.

#machinelearning #python #flight