House Prices Prediction LGBM

Abstract

1. Introduction

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.

2. Objective

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.

This project demonstrates how Jupyter Notebook can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• 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

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/uzunb/house-prices-prediction-LGBM.git
cd house-prices-prediction-LGBM

# 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

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()))

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by uzunb and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.