https://github.com/t-mohamed-shafeek/linear-regression-on-housing-price-prediction

The dataset i have used gives some information about few houses in the United States, and the dataset is : USA_Housing.csv
https://github.com/t-mohamed-shafeek/linear-regression-on-housing-price-prediction

Last synced: 25 days ago
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The dataset i have used gives some information about few houses in the United States, and the dataset is : USA_Housing.csv

• Host: GitHub
• URL: https://github.com/t-mohamed-shafeek/linear-regression-on-housing-price-prediction
• Owner: T-Mohamed-Shafeek
• License: mit
• Created: 2023-02-10T16:10:58.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-09-07T12:51:07.000Z (4 months ago)
• Last Synced: 2024-09-08T14:03:52.838Z (4 months ago)
• Language: Jupyter Notebook
• Size: 1.38 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# **Housing Price Prediction using Linear Regression**

This project aims to predict housing prices based on various features such as area, number of bedrooms, bathrooms, parking spaces, and more. We use a dataset containing these attributes and apply a Linear Regression model to make predictions.

Table of Contents

Introduction

Dataset

Requirements

Installation

Exploratory Data Analysis (EDA)

Data Preprocessing

Model Building

Evaluation

Conclusion

Introduction

Predicting housing prices is a crucial aspect of the real estate market. This project builds a Linear Regression model to predict the price of a house based on factors like area, bedrooms, bathrooms, and more. This notebook demonstrates data preprocessing, feature selection, and model evaluation.

Dataset

The dataset used in this project consists of the following 13 features:

Price: Price of the house.

Area: Total area of the house (in square feet).

Bedrooms: Number of bedrooms.

Bathrooms: Number of bathrooms.

Stories: Number of stories in the house.

Mainroad: Whether the house is connected to a main road (Yes/No).

Guestroom: Whether the house has a guestroom (Yes/No).

Basement: Whether the house has a basement (Yes/No).

Hotwaterheating: Whether the house has hot water heating (Yes/No).

Airconditioning: Whether the house has air conditioning (Yes/No).

Parking: Number of parking spaces available.

Prefarea: Whether the house is in a preferred area (Yes/No).

Furnishing status: Furnishing status of the house (Fully Furnished, Semi-Furnished, Unfurnished).

## **Requirements :**

The following libraries are required to run the notebook:

pandas

numpy

matplotlib

seaborn

scikit-learn

## **Conclusion :**

This project demonstrates how to apply linear regression to predict housing prices. The model can be further improved by tuning hyperparameters, adding more features, or experimenting with other regression models.