Property-Price-Prediction

1. Description
2. Algorithm used
3. Server
4. How to install and run the project
5. How to use the project
6. About the Model
7. Screenshots
8. About the Author

Description

This is a project to predict the price of a house based on the features of the house. The dataset used is the USA_Housing dataset which is available in the kaggle. The dataset contains 50000 rows and 7 columns. The columns are as follows:

1. Avg. Area Income
2. Avg. Area House Age
3. Avg. Area Number of Rooms
4. Avg. Area Number of Bedrooms
5. Area Population
6. Price
7. Address

Algorithm used

The algorithm used is the Linear Regression algorithm. The algorithm is implemented using the sklearn library. The accuracy of the model is 91.9%.

• Train technique used: train_test_split with 30% of the data as test data and random_state = 123
• Accuracy: 91.9%

Server

The server is implemented using Django. The server is hosted on Render. The link to the server is: https://price-prediction-3l6g.onrender.com/

• The server is hosted on Render.com

How to install and run the project

# clone the repository
git clone https://github.com/Adosh74/Property-Price-Prediction

# install the requirements
pip install -r requirements.txt

# run the server
python manage.py runserver

• note: if you use linux comment line 28 in views.py and uncomment line 25

How to use the project

• Go to the link: https://price-prediction-3l6g.onrender.com/predict/
• Enter the values of the features
• Click on the predict button
• The predicted price will be displayed on the screen

About the Model

• The model is trained using the Linear Regression algorithm with the sklearn library
• The model is trained on the Boston Housing dataset
• The model is trained with 70% of the data and tested with 30% of the data
• The accuracy of the model is 91.9%
• Model Code

Screenshots

• Home Page
• Predict Page
• Result Page

About the Author

• Name: Mohamed Shebl
• Collage: Faculty of Computer and Artificial Intelligence, Helwan University Software Engineering Department
• Blog: https://mohamedshebl.me
• Facebook: https://www.facebook.com/shebl74
• LinkedIn: https://www.linkedin.com/in/shebl74
• Email: mohamedshebla@gmail.com