urgpan/quijoman

Queijomen

Content

1. Project Aim

2. Technologies

3. Explore the Data.

4. Build the Model.

5. Project Status.

6. Sources.

7. Additional Information.

Project Aim

We are three students, currently enrolled in an IronHack data bootcamp.

Our project was to build a model that will predict the price of a house based on features provided in the dataset.

Within our python code we split the challenge into three components: ‘Explore the data, ‘Build the model’, ‘Visualise’.

Additionally, our task required using Tableau to graph the data and MySql to explore particular groupings and trends.

Technologies

• Python 3.6
• MySQL 8.0
• Tableau 2020.4

Explore the Data

Exploring the dataset we decided to change two variables:

• Utilize Uber’s hex_3 library rather zip codes.
• Create additional time variables from our date data: season, month_year, week_year.

Build a Model

Regression models used were:

• Linear Regression
• KNeighbors Regression
• Lasso Lars
• Polynomial
• SGDC Classsifier
• Random Forest Regression
• SGD Regressor
• XGB Regressor
• Gradient Boosting Regressor

Project Status

Project has finished, and was done in 5 days. We found that Gradient Boosting Regressor, coupled with Power Transformer, produced the best model for house price prediction with the Washington dataset.

Sources

Useful websites that we used:

https://eng.uber.com/h3/

https://towardsdatascience.com/why-you-shouldnt-use-zip-codes-for-your-hyperlocal-last-mile-analysis-3b9f8613bcc1

https://www.gislounge.com/modifiable-areal-unit-problem-gis/

https://towardsdatascience.com/stop-using-zip-codes-for-geospatial-analysis-ceacb6e80c38

https://hbr.org/2015/11/a-refresher-on-regression-analysis

https://www.upgrad.com/blog/types-of-regression-models-in-machine-learning/

https://www.earthdatascience.org/courses/use-data-open-source-python/use-time-series-data-in-python/date-time-types-in-pandas-python/