https://github.com/nickenshidqia/house_price_prediction
Build machine learning model to predict residential property prices based on various features
https://github.com/nickenshidqia/house_price_prediction
data-science house-price-prediction linear-regression machine-learning
Last synced: about 1 month ago
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Build machine learning model to predict residential property prices based on various features
- Host: GitHub
- URL: https://github.com/nickenshidqia/house_price_prediction
- Owner: nickenshidqia
- Created: 2024-01-28T14:53:40.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-28T15:40:41.000Z (over 1 year ago)
- Last Synced: 2025-04-10T00:46:22.881Z (6 months ago)
- Topics: data-science, house-price-prediction, linear-regression, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 405 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine Learning Project of House Price Prediction
## Project Description
The House Price Prediction project involves the development of a machine learning model to predict residential property prices based on various features. Utilizing a dataset containing information such as square footage, number of bedrooms, location details, and other relevant attributes, the project aims to build an accurate and reliable predictive model.
**Challenges :**
Build a machine learning model that can predict House Price Prediction.
## Project Goal
The project goals is to provide valuable insights for homebuyers, real estate agents, and property investors in understanding and estimating house prices.
## Tools & Library Used
[
](https://www.python.org/)
[
](https://jupyter.org/)
## Project Result
[Click here to get full code](https://github.com/nickenshidqia/House_Price_Prediction/blob/50d7ddab4844e568f11130b522d206c97cc5192c/House%20Price%20Prediction.ipynb)
### Dataset Used
Even though in this dataset, there is categorical values, for example waterfront, and view columns, but it's already in numeric version (1, 0). So we do not need to do label encoding, just standardization using StandardScaler.
### Modelling
I already tried several algorithm such as :
- Linear Regression
- Hyperparameter Tuning using Ridge, Lasso, and ElasticNet
- Decision Tree
- Random Forest
- Support Vector Regressor
But The **Best Evaluation Score** is using **Linear Regression model**.
- The r2 score between the train data and test data is not far, so the model does not overfit.
- The error from this model is small 25%.
### House Price Prediction using Best Model
From the best model, we could predict the House Price using Linear Regression :
### Feature Importance of House Price Prediction
Top 10 of features that have the most significant impact on the predicted target variable :
1. 'grade',
2. 'sqft_living',
3. 'lat',
4. 'sqft_above',
5. 'waterfront',
6. 'view',
7. 'bathrooms',
8. 'sqft_basement',
9. 'condition',
10. 'sqft_living15',