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https://github.com/rayyan9477/data-driven-house-price-prediction-and-property-recommendation-app
The app leverages algorithms to accurately predict house prices and recommend similar properties based on a saved dataset through content-based filtering. It is tailored for homebuyers seeking their dream house and real estate investors looking for profitable opportunities, providing powerful insights and data-driven decision-making support.
https://github.com/rayyan9477/data-driven-house-price-prediction-and-property-recommendation-app
data-science eda html machine-learning numpy pandas python scikit-learn
Last synced: 5 days ago
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The app leverages algorithms to accurately predict house prices and recommend similar properties based on a saved dataset through content-based filtering. It is tailored for homebuyers seeking their dream house and real estate investors looking for profitable opportunities, providing powerful insights and data-driven decision-making support.
- Host: GitHub
- URL: https://github.com/rayyan9477/data-driven-house-price-prediction-and-property-recommendation-app
- Owner: Rayyan9477
- License: mit
- Created: 2024-07-30T20:13:37.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-08-20T18:55:50.000Z (3 months ago)
- Last Synced: 2024-08-23T14:50:20.094Z (3 months ago)
- Topics: data-science, eda, html, machine-learning, numpy, pandas, python, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 11.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Data Driven House Price Prediction and Property Recommendation App
- [LinkedIn - Rayyan Ahmed](https://www.linkedin.com/in/rayyan-ahmed9477/)
- [Github - Rayyan Ahmed](https://github.com/Rayyan9477/)
## About The Project
This repository contains a Flask web application designed for house price prediction and property recommendation using advanced data science, machine learning, and analytics techniques. The app leverages algorithms to accurately predict house prices and recommend similar properties based on a saved dataset through content-based filtering. It is tailored for homebuyers seeking their dream house and real estate investors looking for profitable opportunities, providing powerful insights and data-driven decision-making support.
## About the Data
The goal is to predict the price of a given Housing Property (Regression Analysis) and also to recommend 6 similar Housing Properties (Content-Based Filtering).
There are 70 independent variables in the raw data, I am here explaining only the important features ( 7 features ) used for feature engineering and model prediction. (To make the task easier I have joined bedroom and bathroom feature as BHK for model prediction and recommendation, Although You can modify it as you like):
- propertyType: The type or category of the house or property (e.g., apartment, villa, commercial).
- locality: The specific locality or neighborhood where the house or property is located.
- furnishing: The level of furnishing provided in the house or property (e.g., fully furnished, semi-furnished, unfurnished).
- city: Location of the property, setting the context for its surroundings and amenities.
- bedrooms: Number of sleeping spaces available in the property for residents.
- bathrooms: Count of bathing facilities in the property, indicating convenience and functionality.
- RentOrSale: Specifies whether the property is available for rent or sale, defining its market status.
### Target variable:
- exactPrice : The exact price of the house or property.
## Table of Contents
- [My Sweet Home - House Price Prediction \& Recommendation Project](#my-sweet-home---house-price-prediction--recommendation-project)
- [About The Project](#about-the-project)
- [About the Data](#about-the-data)
- [Target variable:](#target-variable)
- [Table of Contents](#table-of-contents)
- [Images](#images)
- [Installation and Dependencies](#installation-and-dependencies)
- [Working Directory](#working-directory)
- [Working with the code](#working-with-the-code)
- [Usage](#usage)
- [Contributing](#contributing)
- [Contact](#contact)
- [License](#license)
- [Reference](#reference)
## Images
Predicted and recommended Output :
Inputing Features :
## Installation and Dependencies
These are some required packages for our program which are mentioned in the Requirements.txt file
- pandas
- numpy
- seaborn
- matplotlib
- scikit-learn
- catboost
- xgboost
- Flask
- dill
- requests
- beautifulsoup4
- bs4
- jinja2
- joblib
- librosa
- lxml
## Working Directory
```
📦Housing_predict_recommend
┣ 📂artifact
┃ ┗ 📜Dataset.csv
┣ 📂artifacts
┃ ┣ 📜data_preprocessed_recommend.csv
┃ ┣ 📜model.pkl
┃ ┣ 📜model_rent.pkl
┃ ┣ 📜preprocessor.pkl
┃ ┣ 📜processed_data.csv
┃ ┣ 📜recommend_data.csv
┃ ┗ 📜testing.py
┣ 📂catboost_info
┣ 📂logs
┣ 📂NOTEBOOK
┃ ┣ 📂DATA
┃ ┃ ┗ 📜Scraped_Data.csv
┃ ┗ 📜indian-house-price-prediction.ipynb
┣ 📂src
┃ ┣ 📂components
┃ ┃ ┣ 📜data_ingestion.py
┃ ┃ ┣ 📜data_transformation.py
┃ ┃ ┣ 📜model_trainer.py
┃ ┃ ┗ 📜__init__.py
┃ ┣ 📂pipeline
┃ ┃ ┣ 📜predict_pipeline.py
┃ ┃ ┣ 📜scraping_pipeline.py
┃ ┃ ┣ 📜train_pipeline.py
┃ ┃ ┗ 📜__init__.py
┃ ┣ 📂recommender
┃ ┃ ┣ 📜data_transformation_recommend.py
┃ ┃ ┣ 📜house_recommender.py
┃ ┃ ┗ 📜__init__.py
┃ ┣ 📜exception.py
┃ ┣ 📜logger.py
┃ ┣ 📜utils.py
┃ ┗ 📜__init__.py
┣ 📂static
┃ ┣ 📂css
┃ ┃ ┗ 📜signup.css
┃ ┗ 📂img
┃ ┃ ┣ 📜beautiful_house.jpg
┃ ┃ ┣ 📜default_pic.png
┃ ┃ ┗ 📜No Suitable house image found.png
┣ 📂templates
┃ ┣ 📜get_elements.py
┃ ┣ 📜home.html
┃ ┣ 📜index.html
┃ ┗ 📜testing.html
┣ 📜.gitignore
┣ 📜.gitattributes
┣ 📜app.py
┣ 📜city_locality.npy
┣ 📜Dockerfile
┣ 📜main.py
┣ 📜README.md
┣ 📜requirements.txt
┗ 📜setup.py
```
## Working with the code
I have commented most of the neccesary information in the respective files.
To run this project locally, please follow these steps:-
1. Clone the repository:
Clone the Repo or Download the code
2. **Create a Virtual Environment** (Optional but recommended)
It's a good practice to create a virtual environment to manage project dependencies. Run the following command:
```shell
conda create -p python== -y
```
3. **Activate the Virtual Environment** (Optional)
Activate the virtual environment based on your operating system:
```shell
conda activate /
```
4. **Install Dependencies**
- Navigate to the project directory:
```
cd [project_directory]
```
- Run the following command to install project dependencies:
```
pip install -r requirements.txt
```
5. Run the Flask app: Execute the following code in your terminal.
```shell
python app.py
```
6. Access the app: Open your web browser and navigate to http://127.0.0.1:5000/ to use the House Price Prediction and Property Recommendation app.
(Additional Functionality)
1. You can use your own data and form the datasets as per your liking using the pipelines, Change the source of the dataset in data ingestion pipeline in the file directory `src/components/data_ingestion.py`
2. Run the following codes to make your clean, proprocess and make model based on your date (Modify the pre processing pipleline and model making pipeline based on your required result for your project)
```shell
python src/components/data_ingestion.py
python src/components/data_transformation.py
python src/components/model_trainer.py
```
3. Modify the index.html, home.html and app.py to suit your required features.
These file directories are:
`app.py`
`templates/home.html`
`templates/index.html`
## Usage
1. **House Price Prediction:** On the app's homepage, users can input the specific features of the house they are interested in. After submitting the details, the app will process the information and display the predicted price for the house.
2. **Property Recommendation:** Along with the house price predictions users will also get similar recommendation. The app will provide a list of 6 most similar properties that match the given criteria.
## Contact
Rayyan Ahmed - [[email protected]](mailto:[email protected]) Rayyan(https://www.linkedin.com/in/rayyan-ahmed9477/)