/House-Prices-Visualization-Prediction

Predict sales prices and practice different machine learning regressors.

Primary LanguageJupyter Notebook

House Prices: Visualization & Prediction

Predict sales prices and practice different machine learning regressors. ( ⭐️ Star us on GitHub — it helps! )

Getting started with competitive data science can be quite intimidating. So I build this notebook for a quick overview of the House Prices: Advanced Regression Techniques competition. For your convenience, please view it in kaggle.

I encourage you to fork this kernel/GitHub repo, play with the code and enter the competition. Good luck!

Requirements

This project requires Python 2.7 and the following Python libraries installed:

You will also need to have the software installed to run and execute an iPython Notebook

Code

An ipython notebook is used for data preprocessing, feature transforming and outlier detecting. All core scripts are in file .ipynb" folder. All input data are in input folder and the detailed description of the data can be found in Kaggle.

Content in Notebook

  1. Exploratory Visualization
  2. Data Cleaning & Preprocessing
  3. Feature Engineering
    3.1 Value Mapping
    3.2 Simplification 3.3 Feature Selection
    3.4 Handling Categorical Data
  4. Modeling & Evaluation 4.1 Cross-validation method 4.2 Model scoring function 4.3 Setting Up Models
  5. Train & Fit Model
  6. Ensemble Methods
    6.1 Weight Average of Model 6.2 Blend with Top Kernals submissions
  7. Output 7.1 Prediction file ( .csv) 7.2 Model Comparison
  8. Acknowledgments

FlowChart

Prediction & Submission

The final price prediction for each house is present in the output folder as a .csv file. The final model used for scoring is blended one as a weighted average of all different types of models.

Contributors

Rohit Kumar Singh (IIT Bombay)

Feedback

Feel free to send us feedback on file an issue. Feature requests are always welcome. If you wish to contribute, please take a quick look at the kaggle.

Acknowledgments

Inspirations are drawn from various Kaggle notebooks but majorly motivation is from the following :

  1. https://www.kaggle.com/poonaml/house-prices-data-exploration-and-visualisation

  2. https://www.kaggle.com/lavanyashukla01/how-i-made-top-0-3-on-a-kaggle-competition

  3. https://www.kaggle.com/itslek/blend-stack-lr-gb-0-10649-house-prices-v57/data?scriptVersionId=11189608

  4. https://www.kaggle.com/hemingwei/top-2-from-laurenstc-on-house-price-prediction/notebook

  5. https://www.kaggle.com/jesucristo/1-house-prices-solution-top-1

Credit for image to https://www.vancouverrealestatepodcast.com/tag/housing-price-prediction-2019/

Written with StackEdit.