Flight Fare Prediction

[Pranshu-jais]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : Flight Fare Prediction

🔴 Aim : Building Flask web app which predicts fare of Flight ticket.

🔴 Dataset : https://www.kaggle.com/datasets/nikhilmittal/flight-fare-prediction-mh

🔴 Approach : preprocessing dataset ,performing EDA and deep learning models like Feedforward Neural Networks (FNN),Recurrent Neural Networks (RNN),Gradient Boosting Trees (GBT) etc trained on relevant features and deploying the model using flask web app

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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✅ To be Mentioned while taking the issue :

• Full name : Pranshu Jaiswal
• GitHub Profile Link : https://github.com/Pranshu-jais
• Email ID :[email protected]
• Participant ID (if applicable):Pranshu | Contributor. Discord ID: anurag342
• Approach for this Project :As mentioned above
• What is your participant role?GSSoC 2024

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[abhisheks008]

Already assigned to an issue.

[adi271001]

can you please assign this to me @abhisheks008

[abhisheks008]

can you please assign this to me @abhisheks008

Please mention your approach for solving this issue in a detailed manner.

[Pranshu-jais]

But sir can you assign this same issue to me .@abhisheks008

[adi271001]

can you please assign this to me @abhisheks008

Please mention your approach for solving this issue in a detailed manner.

my approach is to first clean the dataset , then apply 5-6 models 1. CNN 2. RNN 3. LSTM 4. GRU 5. TCN 6. ANN please assign it to me under gssoc 24

[abhisheks008]

Hi @Pranshu-jais you are already being assigned to an issue, complete that first, then you can take up other issues.

As you have opened this issue, if you want to work on this issue later on after completing the previous task, I'll lock this issue and will assign to you only.

Looking forward to hearing from you.

[Pranshu-jais]

ok , i will complete the previous issue first and then this issue

[adityasingh-0803]

@abhisheks008 kindly assign the issue to me under gssoc

[abhisheks008]

@abhisheks008 kindly assign the issue to me under gssoc

Can you please share your approach for solving this issue?

[adityasingh-0803]

using ml algorithms we can we will calculate the flight fare
Define Goals: Aim to build a web application where users can input travel details and get predicted flight fares.

Data Collection: Scrape flight data from websites or use publicly available datasets from airlines.

Preprocessing: Clean data, handle missing values, and transform features like dates into numerical formats.

Model Building: Choose a suitable ML algorithm (e.g., Random Forest), train it on historical data, and evaluate its accuracy.

Deployment: Develop a Flask web app where users input their travel details, use the trained model to predict fares, and display results.

Iterate: Refine the model based on user feedback and new data to improve accuracy over time.
Kindly check @abhisheks008

[Pranshu-jais]

@abhisheks008 kindly assign the issue to me under gssoc

[abhisheks008]

Complete your previously assigned issue first.