https://github.com/mariiasam/rent-in-brazil
https://github.com/mariiasam/rent-in-brazil
elasticnet-regression joblib lasso-regression linear-regression numpy pandas random-forest-regressor ridge-regression scikit-learn streamlit svr-regression-prediction
Last synced: 4 days ago
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- Host: GitHub
- URL: https://github.com/mariiasam/rent-in-brazil
- Owner: MariiaSam
- Created: 2025-01-12T19:27:33.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-03-25T16:13:33.000Z (7 months ago)
- Last Synced: 2025-05-30T07:31:14.170Z (5 months ago)
- Topics: elasticnet-regression, joblib, lasso-regression, linear-regression, numpy, pandas, random-forest-regressor, ridge-regression, scikit-learn, streamlit, svr-regression-prediction
- Language: Jupyter Notebook
- Homepage: https://rent-brazil1.streamlit.app/
- Size: 96.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Regression model: Rent in Brazil
# A model for predicting the cost of rent in Brazil
This project was developed to determine the cost of rent in Brazil. The interactive interface is based on [**Streamlit**](https://rent-brazil1.streamlit.app/), which allows you to easily interact with the model and analyze the results.
## Description
**1. Project objective:** To create an analytical tool for researching the value of rents in Brazil, including various factors, including regions.
**2. Project tasks:**
- **Data analysis**: to identify the key factors that influence the cost;
- **Model building**: using machine learning and statistical analyses to create a model that can predict rental values;
- **User interface**: develop an interactive interface that will allow users to enter new data, analyze the results and make predictions based on the model.
## Technologies
The project was implemented using the following technologies:
- **Python**: the main programming language;
- **Docker Compose**: to simplify the process of deploying and managing the project in the Docker environment.
## Libraries
- **Pandas**: for data processing;
- **Numpy**: for numerical calculations;
- **Scikit-learn**: for building and evaluating machine learning models;
- **Matplotlib** and **Seaborn**: for data visualization;
- **Streamlit**: for creating an interactive interface;
- **Joblib**: for efficient serialization (saving) and loading of Python objects.
## Dataset
**The dataset used for this project has the following characteristics:**
- **https://www.kaggle.com/datasets/iamsouravbanerjee/house-rent-prediction-dataset**
- format: `.csv`;
- contains the following key columns: `city`, `rent amount`, `bill_avg`, `parking spaces`, etc.
This dataset contains 10692 rental properties with 13 different characteristics:
- **city**: the city in which the property is located;
- **area**: area of the property;
- **rooms**: number of rooms;
- **bathroom**: number of bathrooms;
- **parking spaces**: number of parking spaces;
- **floor**: floor;
- **animals**: permission to stay with animals;
- **furniture**: furniture;
- **hoa (R$)** : homeowners association tax;
- **rent amount (R$)**: the amount of rent;
- **property tax**: Municipal property tax;
- **fire insurance (R$)**: the cost of fire insurance;
- **total (R$)**: the total sum of all values.
## The cost of renting an apartment depending on the number of rooms
## Rental housing costs depending on the city
✅ **_In São Paulo, the average cost of rent is higher than in other cities._**
**_And this is not surprising, as it is the center of a large agglomeration with a population of 23 million, also one of the largest in the world._**
## Correlation analysis conclusions for rent amount:
**_Correlation analysis conclusions for rent amount:_**
**_Strong positive relationship_**: variables that are highly positively correlated with rent amount have a direct relationship. That is, when the value of these variables increases, the rent also increases;
- **fire insurance (0.987)**: very strong correlation with rent amount. This is expected as the amount of insurance can be proportional to the rent;
- **bathrooms (0.666)**: having more bathrooms is associated with higher rents. This indicates that living spaces with more amenities are more expensive;
- **parking spaces (0.574)**: housing with parking spaces has higher rents, as it is often a sign of luxury or convenience;
- **rooms (0.537)**: a larger number of rooms is also associated with higher rents, which is consistent with the logic of larger areas;
- **City São Paulo (0.25)**: location strongly influences higher rents.
**_Weak positive relationship_**:
- **area (0.178)**: area has a moderate positive correlation, but is not a key factor. This may indicate that a larger area does not always mean significantly higher rents;
- **property tax (0.107)**: property tax has a weak impact. It is probably taken into account by property owners, but is not a direct indicator of rents.
**_Almost neutral impact:_**
- **floor (0.071)**: floor has a very weak impact on rents, which may depend on the city and the architecture of the buildings;
- **animal accept (0.06)**: minor impacts on rent levels;
- **hoa (0.052)**: small relationship with homeowners' association fees. This may only affect certain types of housing (e.g., apartments in condominiums);
**_Negative correlation_**: variables with a negative correlation have an inverse relationship, i.e., when the value of the variable increases, the rent decreases.
- **animal_not_accept (-0.06)**: in premises where animals are not allowed, rents are slightly lower;
- **furniture_not furnished (-0.17)**: show an inverse relationship, possibly due to tenant preferences.
Cities, for example, city_Belo Horizonte, city_Campinas: In cities with a negative correlation with rent amount, rents may be lower compared to the base cities (e.g. city_São Paulo).
## Models:
- the following models were tested in the project: **LinearRegression; Linear regression with Lasso, Ridge, ElasticNet regularization; SVR, RandomForestRegressor**;
- was used to select the best hyperparameters: GridSearchCV.
# Compare results:
| Model | MAE | MSE | RMSE | R2 |
| ------------------------------------------------------------- | ---------- | ------------ | ----------- | -------- |
| **Linear Regression (Train)** | 296.182627 | 2.156508e+05 | 464.382169 | 0.981444 |
| **Linear Regression (Test)** | 321.153450 | 6.703823e+05 | 818.768761 | 0.946516 |
| **Linear regression with Lasso regularization (Train)** | 293.663125 | 2.247485e+05 | 474.076464 | 0.980661 |
| **Linear regression with Lasso regularization (Test)** | 315.717036 | 3.939180e+05 | 627.628853 | 0.968573 |
| **SVR (Train)** | 284.258514 | 1.639049e+06 | 1280.253446 | 0.858968 |
| **SVR (Test)** | 291.741362 | 4.293574e+05 | 655.253668 | 0.965745 |
| **Random Forest Regressor (Train)** | 123.370415 | 5.525399e+04 | 235.061670 | 0.995246 |
| **Random Forest Regressor (Test)** | 342.022606 | 9.401107e+05 | 969.593048 | 0.924996 |
| **Linear regression with Ridge regularization (Train)** | 296.434150 | 2.156853e+05 | 464.419328 | 0.981441 |
| **Linear regression with Ridge regularization (Test)** | 321.891060 | 6.876466e+05 | 829.244573 | 0.945138 |
| **Linear regression with Elastic Net regularization (Train)** | 294.810219 | 2.231826e+05 | 472.422034 | 0.980796 |
| **Linear regression with Elastic Net regularization (Test)** | 294.810219 | 2.231826e+05 | 472.422034 | 0.980796 |
# **_Comparisons and conclusions_**
Model evaluation is based on several metrics: MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and R² (coefficient of determination). The best model can be chosen depending on the purpose of the analysis, but in general, lower errors (MAE, MSE, RMSE) and higher R² indicate a better model.
❎ **_Best model:_**
**Best model: Elastic Net (a regularized linear regression)**. Why:
High R² on training (0.9808) and test (0.9808) data.
The lowest RMSE on the test data (472.42), indicating the best fit to real-world values.
Elastic Net balances the regularization, avoiding overfitting.
**Alternative model: Lasso Regression (a regularized linear regression)**. Why:
The second best R² on the test data (0.9686).
Low RMSE (627.63), which also indicates good consistency.
Less sensitive to collinearity than simple linear regression.
⭕ **_Worst Model:_**
Random Forest overfits the training data, with the highest test errors and reduced generalizability. It may require hyperparameter tuning to improve its performance.
## Run locally
**Clone the repository:**
```
git clone https://github.com/MariiaSam/Rent-in-Brazil.git
cd Rent-in-Brazil
```
**Set up the virtual environment with Poetry**
Set up project dependencies:
```
poetry install
```
To activate the virtual environment, run the command:
```
poetry shell
```
To add a dependency to a project, run the command:
```
poetry add
```
To pull in existing dependencies:
```
poetry install
```
# Using
Run the Streamlit application with the command:
```
streamlit run app.py
```
# Docker
This project also supports Docker containerization, which makes it easy to run the application without having to manually configure the environment.
**1. Starting a project with Docker**
Use the command to run a container based on your Docker Image:
```
docker run --rm -d -p 8880:8880 rentinbrazil:latest
```
- --rm - automatically removes the container after stopping;
- -d - runs the container in the background;
- -p 8880:8880 - displays port 8880 on the local machine to access the application.
**2. Access to the application:**
After starting the container, the application will be available at the address:
```
http://localhost:8880
```
3. **Stopping the project:**
To stop the project, do:
```
docker ps
```
Then use the command to stop the container:
```
docker stop
```
Where is your container's identifier, obtained from the docker ps command.