Flight Fare Prediction

[Pranshu-jais]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Flight Fare Prediction :red_circle: Aim : Building Flask web app which predicts fare of Flight ticket. :red_circle: Dataset : https://www.kaggle.com/datasets/nikhilmittal/flight-fare-prediction-mh :red_circle: 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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:red_circle::yellow_circle: 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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:white_check_mark: 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[bot]]

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.

[ojaswichopra]

@abhisheks008 Can you please assign this issue to me. I plan to first conduct exploratory data analysis (EDA) to understand feature relationships and select relevant features and then Train models like Feedforward Neural Networks (FNN), Recurrent Neural Networks (RNN), and Gradient Boosting Trees (GBT), and evaluate their performance.

[abhisheks008]

@abhisheks008 Can you please assign this issue to me. I plan to first conduct exploratory data analysis (EDA) to understand feature relationships and select relevant features and then Train models like Feedforward Neural Networks (FNN), Recurrent Neural Networks (RNN), and Gradient Boosting Trees (GBT), and evaluate their performance.

Focus on deep learning models instead of machine learning methods. Update your approach ans share here.

[ojaswichopra]

I can use FNN, RNN and also LSTM. I can also try ensemble learning on these models.

[abhisheks008]

I can use FNN, RNN and also LSTM. I can also try ensemble learning on these models.

One issue at a time.

[rooods01]

@abhisheks008 Please assign this issue to me. I plan to first do EDA and then use models like FNN, RNN and LSTM. I 'll also do ensemble learning

[abhisheks008]

Follow the issue template and share your credentials as per the template @rudrasurana1025

[Pratzybha]

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 : Deep Learning Models: Model 1: Deep Neural Network (DNN) Model 2: Time Series Forecasting with LSTM/GRU Model 3: Hybrid Model Combine DNN and LSTM to account for both static features and time-based patterns.

📍 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. 🔴🟡 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. ✅ To be Mentioned while taking the issue :

Full name : Pratibha Balgi GitHub Profile Link : https://github.com/Pratzybha Email ID : [email protected] Participant ID (if applicable): PratibhaBalgi | Contributor Approach for this Project :As mentioned above What is your participant role?GSSoC 2024-ext

[abhisheks008]

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 : Deep Learning Models: Model 1: Deep Neural Network (DNN) Model 2: Time Series Forecasting with LSTM/GRU Model 3: Hybrid Model Combine DNN and LSTM to account for both static features and time-based patterns.

📍 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. 🔴🟡 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. ✅ To be Mentioned while taking the issue :

Full name : Pratibha Balgi GitHub Profile Link : https://github.com/Pratzybha Email ID : [email protected] Participant ID (if applicable): PratibhaBalgi | Contributor Approach for this Project :As mentioned above What is your participant role?GSSoC 2024-ext

One issue at a time. Already assigned to an issue.

[Chinmayee-cd]

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 : Deep Learning Models: Model 1: Random Forest Regression Model 2: K-Nearest Neighbors(KNN) Regression Model 3: Decision Tree Regression

📍 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. 🔴🟡 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. ✅ To be Mentioned while taking the issue :

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

[abhisheks008]

As this repository mainly focuses on deep learning models, hence requesting you to update your approach for this problem statement and revert back. @Chinmayee-cd