https://github.com/shibam120302/flight_fare_prediction

A step-by-step guide to your flight fare prediction machine learning project!
https://github.com/shibam120302/flight_fare_prediction

data-science jupyter-notebook ml predictive-modeling

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A step-by-step guide to your flight fare prediction machine learning project!

• Host: GitHub
• URL: https://github.com/shibam120302/flight_fare_prediction
• Owner: shibam120302
• License: mit
• Created: 2023-03-10T03:11:19.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-03-10T03:16:38.000Z (over 2 years ago)
• Last Synced: 2025-01-21T17:50:45.592Z (9 months ago)
• Topics: data-science, jupyter-notebook, ml, predictive-modeling
• Language: Jupyter Notebook
• Homepage:
• Size: 2.58 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          

#  Flight Fare Prediction

### Problem Statement

 The price of airline ticket changes frequently

nowadays and there's plenty of difference. Price change

keeps happening within few hours for the identical flight.

The shoppers want to induce the most cost-effective possible

price while the airline companies want the utmost profit and

revenue possible. To unravel this problem,we

introduce different models to avoid wasting consumers

money- minimum price predicting model and models that

tell us an optimal time to shop for a ticket while airlines use

techniques like demand prediction and price discrimination

to maximize their revenue.

![image](https://i0.wp.com/collegeclue.com/medical/wp-content/uploads/2021/07/air-1.jpg?fit=1000%2C667&ssl=1)

## Tech Stack Used

1. Python 

2. FastAPI 

3. Machine learning algorithms

4. Docker

5. Google cloud Platform

## Infrastructure Required.

1.  GCP Container Registry

2.  Google Kubernetes Engine (GKE)

## Directory Tree 

```

|---Notebook

|   |--Flight_Fare_Prediction.ipynb

|static 

│   ├── styles.css

├── templates

│   ├── index.html

├── README.md

├── app.py

├── prediction_model.pkl

|── scaler.pkl

|── Dockerfile

├── requirements.txt

```

## Project Archietecture

![Flight_Fare drawio](https://user-images.githubusercontent.com/52121048/223192362-9d68ef4f-6f56-4525-b05f-4166996d46a1.png)

## Deployment Archietecture

![dp](https://user-images.githubusercontent.com/52121048/223192432-4b689c88-7bd5-440f-ba8a-a2bed7bdb7f0.jpg)

## Overview of the ML solution 

In conclusion, the ML solution using Random Forest Regressor for flight fare prediction has shown promising results with a maximum R2 score of 89. This indicates that the model has a high level of accuracy in predicting flight fares.

Random Forest Regressor is a powerful machine learning algorithm that can handle complex datasets and provide accurate predictions. It works by creating multiple decision trees and combining them to make a final prediction, which helps to reduce overfitting and increase the generalizability of the model.

With the high accuracy of this model, it can be used to provide valuable insights for airlines and customers alike. Airlines can use it to optimize pricing strategies and predict demand, while customers can use it to plan and budget their trips more effectively.

In conclusion, the use of Random Forest Regressor for flight fare prediction is a promising solution that can provide accurate and valuable insights for the airline industry and its customers.

## Demo 

Link : http://35.234.9.63/