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This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.

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uzunb/house-prices-prediction-LGBM

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house-prices-prediction-LGBM

Open in Streamlit

Description

This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB & Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.

Data

The dataset is available at Kaggle.

Goal

The goal of this project is to predict the price of a house in Ames using the features provided by the dataset.

Features

The dataset contains the following features:

  • OverallQual: Overall quality of the house
  • GrLivArea: Above grade (ground) living area square feet
  • GarageCars: Number of garage cars
  • TotalBsmtSF: Total square feet of basement area
  • FullBath: Number of full baths
  • YearBuilt: Year house was built
  • TotRmsAbvGrd: Total number of rooms above grade (excluding bathrooms and closets)
  • Fireplaces: Number of fireplaces
  • BedroomAbvGr: Number of bedrooms above grade
  • GarageYrBlt: Year garage was built
  • LowQualFinSF: Lowest quality finished square feet
  • LotFrontage: Lot frontage square feet
  • MasVnrArea: Masonry veneer square feet
  • WoodDeckSF: Square feet of wood deck area
  • OpenPorchSF: Open porch square feet
  • EnclosedPorch: Enclosed porch square feet
  • 3SsnPorch: Three season porch square feet
  • ScreenPorch: Screen porch square feet
  • PoolArea: Pool square feet
  • MiscVal: Miscellaneous value
  • MoSold: Month house was sold
  • YrSold: Year house was sold
  • SalePrice: Sale price

Usage

# clone the repo
git clone https://github.com/uzunb/house-prices-prediction-LGBM.git

# change to the repo directory
cd house-prices-prediction-LGBM

# if virtualenv is not installed, install it
#pip install virtualenv

# create a virtualenv
virtualenv -p python3 venv

# activate virtualenv for LINUX or MACOS
source venv/bin/activate

# # activate virtualenv for WINDOWS
# venv\Scripts\activate.ps1
#     # throubleshooting for activation error in windows
#     Set-ExecutionPolicy RemoteSigned -Scope CurrentUser

# install dependencies
pip install -r requirements.txt

# run the script
streamlit run main.py

Model Development

Model

The model is based on a LightGBM algorithm.

Training

import lightgbm as lgb

model = lgb.LGBMRegressor(max_depth=3, 
                    n_estimators = 100, 
                    learning_rate = 0.2,
                    min_child_samples = 10)
model.fit(x_train, y_train)

Grid Search Cross Validation is used for hyper parameters of the model.

from sklearn.model_selection import GridSearchCV

params = [{"max_depth":[3, 5], 
            "n_estimators" : [50, 100], 
            "learning_rate" : [0.1, 0.2],
            "min_child_samples" : [20, 10]}]

gs_knn = GridSearchCV(model,
                      param_grid=params,
                      cv=5)

gs_knn.fit(x_train, y_train)
gs_knn.score(x_train, y_train)

pred_y_train = model.predict(x_train)
pred_y_test = model.predict(x_test)

r2_train = metrics.r2_score(y_train, pred_y_train)
r2_test = metrics.r2_score(y_test, pred_y_test)

msle_train =metrics.mean_squared_log_error(y_train, pred_y_train)
msle_test =metrics.mean_squared_log_error(y_test, pred_y_test)

print(f"Train r2 = {r2_train:.2f} \nTest r2 = {r2_test:.2f}")
print(f"Train msle = {msle_train:.2f} \nTest msle = {msle_test:.2f}")

print(gs_knn.best_params_)

Evaluation

from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
from sklearn.metrics import explained_variance_score
from sklearn.metrics import mean_squared_log_error

y_pred = model.predict(x_test)
print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))
print('Mean Squared Error:', mean_squared_error(y_test, y_pred))
print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))
print('Mean Squared Log Error:', mean_squared_log_error(y_test, y_pred))
print('Explained Variance Score:', explained_variance_score(y_test, y_pred))
print('R2 Score:', r2_score(y_test, y_pred))

Deployment

Simple model distribution is made using Streamlit.

import streamlit as st

st.title("House Prices Prediction")
st.write("This is a simple model for house prices prediction.")

st.sidebar.title("Model Parameters")

variables = droppedDf["Alley"].drop_duplicates().to_list()
inputDict["Alley"] = st.sidebar.selectbox("Alley", options=variables)

inputDict["LotFrontage"] = st.sidebar.slider("LotFrontage", ceil(droppedDf["LotFrontage"].min()), 
floor(droppedDf["LotFrontage"].max()), int(droppedDf["LotFrontage"].mean()))

Results

The model is trained on the dataset and tested on the test dataset. The results are shown demo with Streamlit below:

Open in Streamlit

Contributions

About

This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.

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