https://github.com/karan-malik/advancedregression
Using Various Regression Algorithms to Predict House Sales
https://github.com/karan-malik/advancedregression
adaboost-algorithm decision-tree-regression decision-trees gradient-boosting-regressor house-price-prediction linear-regression machine-learning machine-learning-algorithms python python3 random-forest-classifier random-forest-regressor regression regression-algorithms regression-models scikit-learn scikitlearn-machine-learning support-vector-regression svm
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Using Various Regression Algorithms to Predict House Sales
- Host: GitHub
- URL: https://github.com/karan-malik/advancedregression
- Owner: Karan-Malik
- License: mit
- Created: 2020-07-01T13:32:49.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-01T13:52:03.000Z (almost 5 years ago)
- Last Synced: 2025-01-14T08:52:32.050Z (3 months ago)
- Topics: adaboost-algorithm, decision-tree-regression, decision-trees, gradient-boosting-regressor, house-price-prediction, linear-regression, machine-learning, machine-learning-algorithms, python, python3, random-forest-classifier, random-forest-regressor, regression, regression-algorithms, regression-models, scikit-learn, scikitlearn-machine-learning, support-vector-regression, svm
- Language: Python
- Homepage:
- Size: 90.8 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# AdvancedRegression
Using Various Regression Algorithms to Predict House Sales
## Overview
This project uses Machine Learning to predict house sale prices using previously available data. The following regression algorithms are used:
1. Linear Regression
2. Decision Tree Regression
3. Random Forest Regression
4. AdaBoost Regression
5. Gradient Boosting Regression
6. Support Vector Regression
It also uses PCA to reduce data dimensionality.
## About the Dataset
The dataset used is available on [Kaggle](https://www.kaggle.com). It contains about 80 features belonging to over 1500 houses, which can be used to predict the sales of houses and the sale prices.
The dataset is also available in the repository as train.csv.
To access the data from Kaggle, click on the following [link](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data?select=train.csv)
## How to Run the Model on your System
1. Use this [link](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data?select=train.csv) to download the dataset and set the folder containing the downloaded data as the working directory.
2. Make sure you have all the libraries used in the Housesale.py file. In case you need to download any of the libraries, use this command on your Command Prompt:
```
pip install 'your library name'
```
3. Once you have all the libraries imported, copy the code from Housesale.py and run it.