https://github.com/davydantoniuk/lublin-house-price-predict

Predicting house prices in Lublin, Poland, by comparing ML algorithms and deploying a FastAPI app for real-time predictions.
https://github.com/davydantoniuk/lublin-house-price-predict

catboost deep-learning fastapi house-price-prediction lublin machine-learning otodom python pytorch sklearn

Last synced: about 2 months ago
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Predicting house prices in Lublin, Poland, by comparing ML algorithms and deploying a FastAPI app for real-time predictions.

• Host: GitHub
• URL: https://github.com/davydantoniuk/lublin-house-price-predict
• Owner: davydantoniuk
• License: mit
• Created: 2024-12-27T14:22:49.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-01-04T11:56:47.000Z (10 months ago)
• Last Synced: 2025-01-04T12:42:38.950Z (10 months ago)
• Topics: catboost, deep-learning, fastapi, house-price-prediction, lublin, machine-learning, otodom, python, pytorch, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 7.24 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🏠 Lublin House Price Prediction

https://davydantoniuk.github.io/lublin-house-price-predict/

This project aims to predict house prices in Lublin using various machine learning and deep learning models. The dataset includes property listings from [Otodom](https://www.otodom.pl/pl/wyniki/sprzedaz/mieszkanie/lubelskie/lublin/lublin/lublin?viewType=listing&page=) web page.

## **Results**

-   After comparing various models, the **Random Forest Regressor** and **CatBoost** models showed the best performance and were combined in a stacking model.

-   The FastAPI app allows easy interaction with the trained model for real-time house price predictions.

## Visualizations

1. **Model Results Comparison**: A bar plot comparing the performance of all models, showcasing their metrics.

   ![Bar Plot of Model Results](description_images/model_comparison.png)

2. **Prediction Example**:

    - **Input Example**: The last advertisement for sale on otodom.

      ![Example Ad](description_images/image2.jpg)

    - **Predicted Output**: The model's prediction.\

      ![Predicted vs Actual](description_images/example_output1.png)

      ![Predicted vs Actual](description_images/example_output2.png)

## 📁 **Project Structure**

### 1. Introduction

-   Overview of the project goals and objectives.

### 2. Data Collection & Preprocessing

-   Scraped property data and handled issues like:

    -   Data type conversions

    -   Duplicate listings

    -   Missing values with KNN imputation

    -   One-hot encoding for categorical features

### 3. Exploratory Data Analysis (EDA)

-   Performed detailed analysis on:

    -   Price and area distribution

    -   Price per sqm

    -   Floor and regional analysis

    -   Temporal trends and property characteristics

### 4. Feature Engineering & Data Preparation

-   Transformed key features:

    -   Floor and regional-based transformation

    -   Outlier detection and handling

    -   Train-test-validation split

### 5. Machine Learning Models

-   Compared various ML models:

    -   Linear Regression, Elastic Net, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost

    -   Built a stacking model for better performance

### 6. Deep Learning Models

-   Developed deep learning models:

    -   Feedforward Neural Network

    -   Convolutional Neural Network

### 7. Model Comparison and Interpretation

-   Compared performance metrics of all models

-   Analyzed feature importance

-   Investigated worst predictions

### 8. Final Model & Predictions

-   Saved the best-performing model

-   Made predictions on new data

### 9. FastAPI App

-   Developed a FastAPI-based web application to:

    -   Serve the best model

    -   Provide real-time predictions

## Installation & Usage

### Run the FastAPI App

1. **Clone the repository**:

    ```bash

    git clone https://github.com/davydantoniuk/lublin-house-price-predict.git

    cd lublin-house-price-predict/app

    ```

2. **Install the required packages**:

    ```bash

    pip install -r requirements.txt

    ```

3. **Run the FastAPI app**:

    ```bash

    uvicorn app:app --reload

    ```

4. Access the app: Open your browser and navigate to `http://127.0.0.1:8000`

### Build the Docker Image

1. **Clone the repository**:

    ```bash

    git clone https://github.com/davydantoniuk/lublin-house-price-predict.git

    cd lublin-house-price-predict/app

    ```

2. **Build the Docker image**:

    ```bash

    docker build -t lublin-house-price-predict .

    ```

3. **Run the Docker container**:

    ```bash

    docker run -p 80:80 lublin-house-price-predict .

    ```

4. Access the app: Open your browser and navigate to `http://localhost:80`