https://github.com/harmanveer-2546/house-price-prediction-

We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.
https://github.com/harmanveer-2546/house-price-prediction-

catboost-classifier linear-regression machine-learning matplotlib numpy pandas python random-forest seaborn svm

Last synced: 6 days ago
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We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.

• Host: GitHub
• URL: https://github.com/harmanveer-2546/house-price-prediction-
• Owner: harmanveer-2546
• Created: 2024-06-04T17:36:43.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-06-04T17:47:36.000Z (6 months ago)
• Last Synced: 2024-06-04T19:54:46.698Z (6 months ago)
• Topics: catboost-classifier, linear-regression, machine-learning, matplotlib, numpy, pandas, python, random-forest, seaborn, svm
• Language: Jupyter Notebook
• Homepage:
• Size: 1.44 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
House prices increase every year, so there is a need for a system to predict house prices in the future.

People looking to buy a new home tend to be more conservative with their budgets and market strategies.

This project analyzes various parameters like average income and area and predicts the house price accordingly.

This application will help customers invest in an estate without approaching an agent

To provide a better and faster way of performing operations.

To provide proper house prices to the customers.

To eliminate the need for real estate agents to gain information regarding house prices.

To provide the best price to users without getting cheated.

To enable users to search for homes as per the budget.

The aim is to predict efficient house pricing for real estate customers concerning their budgets and priorities. By analyzing previous market trends price ranges, and also upcoming developments future prices will be predicted.

House price prediction can help the developer determine the selling price of a house and can help the customer arrange the right time to purchase a house.