recodehive / machine-learning-repos

A curated list of awesome machine learning frameworks, libraries and software (by language). I
https://machine-learning-repos.vercel.app/
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Price of House price prediction #949

Closed sd-coder07 closed 2 months ago

sd-coder07 commented 2 months ago

Is there an existing issue for this?

Feature Description

Feature Description

  1. Location (Latitude and Longitude):

Description: The geographical coordinates of the property. Role in Prediction: Essential for capturing the neighborhood characteristics and proximity to amenities, which significantly impact house prices.

  1. Number of Bedrooms:

Description: The total number of bedrooms in the property. Role in Prediction: Directly correlates with the size and suitability of the house for families, affecting its market value.

  1. Number of Bathrooms:

Description: The total number of bathrooms. Role in Prediction: More bathrooms typically increase the convenience and appeal of the property, impacting its price.

  1. Square Footage:

Description: The total area of the house in square feet. Role in Prediction: Larger homes generally have higher values, making this a critical feature for prediction.

  1. Lot Size:

Description: The size of the property’s lot in square feet or acres. Role in Prediction: Larger lots can add significant value, especially in suburban or rural areas.

Use Case

Use Case

Real Estate Agencies and Brokers: Description: Real estate professionals can use house price prediction models to provide accurate pricing recommendations for buyers and sellers. Impact: Helps in setting competitive prices, attracting buyers, and ensuring sellers get the best value for their property.

Home Buyers: Description: Potential buyers can use prediction tools to assess whether the asking price of a property is fair. Impact: Enables buyers to make informed decisions and potentially negotiate better deals.

Home Sellers: Description: Sellers can determine the optimal listing price for their properties. Impact: Helps in selling the property faster and at a fair market value.

Mortgage Lenders: Description: Lenders can use prediction models to assess the value of a property for mortgage approval. Impact: Reduces the risk of lending and ensures loans are backed by adequately valued assets.

Benefits

Benefits

  1. Accurate Valuation:

Benefit: Provides precise and data-driven property valuations. Impact: Reduces the risk of overpricing or underpricing properties.

  1. Market Insights:

Benefit: Offers insights into market trends and property value drivers. Impact: Helps stakeholders understand the real estate market dynamics.

  1. Enhanced Decision-Making:

Benefit: Supports informed decision-making for buyers, sellers, and investors. Impact: Leads to better financial outcomes and satisfaction among stakeholders.

  1. Time Efficiency:

Benefit: Automates the valuation process, saving time for real estate professionals and clients. Impact: Speeds up transactions and reduces the time properties spend on the market.

  1. Risk Management:

Benefit: Helps lenders and investors manage risks by ensuring properties are accurately valued. Impact: Minimizes financial losses and enhances portfolio stability.

Add ScreenShots

No response

Priority

High

Record

github-actions[bot] commented 2 months ago

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊

Note: I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue.

You can also check our CONTRIBUTING.md for guidelines on contributing to this project.
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sanjay-kv commented 2 months ago

this is already done

github-actions[bot] commented 2 months ago

Hello @sd-coder07! Your issue #949 has been closed. Thank you for your contribution!