Real Estate Price Prediction

[Pranshu-jais]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Real Estate Price Prediction :red_circle: Aim : Building an ML model to predict real estate price :red_circle: Dataset : https://www.kaggle.com/datasets/amitabhajoy/bengaluru-house-price-data :red_circle: Approach : Creating a Real Estate Price Prediction Model using ml algorithm like XGBoost,Random Forest,Lasso Regression etc or a deep learning model trained on relevant features that significantly impact real estate prices

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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! 😊

[ramana2074]

I would like to work on this. can you please assign me @abhisheks008

[abhisheks008]

Can you please share your approach for solving this problem statement?

[ramana2074]

The approach involves data preprocessing, feature engineering, linear regression modeling, and prediction analysis to predict house prices in Bengaluru based on various features.

The models used in this approach are:

Linear Regression: A linear regression model is used to predict the house prices in Bengaluru based on various features. StandardScaler: A StandardScaler is used to scale the data, which is a common preprocessing step in machine learning.

[Gitika-26]

@abhisheks008 I am interested in working on this project. My approach would be implementing ANN, RNN, and LSTM models for real estate price prediction. ANN will capture non-linearity between features like property size and location. RNN and LSTM will analyze sequential trends, like historical prices. Model performance will be compared using MAE and RMSE. I am avoiding CNN since they are better suited for image analysis

[abhisheks008]

Hi all, thanks for sharing your approach. @Gitika-26 you can start working on this issue. Make sure you implement at least 4 models for this problem statement.

[Gitika-26]

yea started working on this. I will be implementing 5 models for this one. Will get back to you next week with the code.

[KUMUD-TECH]

Anyone working on this project currently??

[abhisheks008]

Anyone working on this project currently??

No. If you are interested please mention all the required details as per the issue template. Also mention in which open source event you are participating in.

@KUMUD-TECH

[KUMUD-TECH]

✅ To be Mentioned while taking the issue :

Full name : Kumud Verma GitHub Profile Link : https://github.com/KUMUD-TECH Email ID :[[email protected] Approach for this Project : As mentioned above What is your participant role? SWOC 2025

[abhisheks008]

Hi @KUMUD-TECH can you please share your approach for solving this problem statement?

[KUMUD-TECH]

I will use Linear Regression when the relationship between features and the target variable is simple and linear and will use XGBoost to handle complex interactions between features and capture non-linear patterns in the data.

[abhisheks008]

I will use Linear Regression when the relationship between features and the target variable is simple and linear and will use XGBoost to handle complex interactions between features and capture non-linear patterns in the data.

Hi @KUMUD-TECH in this project repository we mainly focus on deep learning methods over machine learning methods. can you rephrase your approach and comment it again?