We are working as an analyst for a real estate company. Our company wants to build a machine learning model to predict the selling prices of houses based on a variety of features on which the value of the house is evaluated.
Our job is to build a model that will predict the price of a house based on features in the dataset. Our management also wants to explore the characteristics of the houses using some business intelligence tools. One of those parameters includes understanding which factors are responsible for higher property value - $650K and above.
For our project we used the tools that allow us to work with an iterative and agile approach:
- Github: I set up a Github repo for easy acces and safe procedure.
- Trello: with Trello board I planed the development of the project.
- MySQL workbench: GUI for easy aproach to SQL.
- Tableau: BI tool with interesting functionalities.
- Jupyter Notebook: Python coding notebook to easly execute bloacks of code.
We used the dataset that they provided us and has information of about 22,000 properties sold between May 2014 to May 2015. Our general procedure was:
- Exploring the data with excel.
- Exploring the data with simple queries in SQL
- Working on our SQL excercices.
- Exploring the data with Tableau and doing the excercices.
- Exploring the data with python/pandas
- Cleaning, wrangling and visualizating the data with python/pandas
- Creating and iterating models (and modifying the data when needed)
The files where we did all the procedure are:
Thank you for interest!