This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It's a great dataset for evaluating simple regression models.
The purpose of this streamlit is to demonstrate some analysis of a data set of the company House Rocket. This data set has houses sold in the years 2014 to 2015. The CEO asked him to carry out some analysis to present to the business team, being these questions.
- What properties should House Rocket buy and at what price?
- Once the property is purchased, when is the best time to sell it and at what price?
Step 01. Data collection: Download dataset from the Kaggle website and collect geographic locations with the API.
Step 02. Description of the data: In this stage the objective is to use statistical metrics to identify data outside the scope of the business.
Step 03. Feature Engineering: derive new attributes based on the original variables to better define the phenomenon.
Step 04. Exploratory Data Analysis: For this step, the objective is to explore the data to better understand the impact of variables on model learning and find insights.
Step 05. Data presentation: Created a dashboard using streamlit.
- Who is the project stakeholder? House Rocket's CEO
Used tools?
- Python 3.8
- Jupyter Notebook
- Visual code
- Final product? Dashboard in the streamlit, where the CEO can access the analysis and simulate the purchase of a house.
- What is the format?
- Problem type? Data analysis
- How we will deliver? Dashboard at streamlit
In this project, I acquired some knowledge such as creating hypotheses of business, collecting data from an API, building a page in the streamlit and manipulating the data.
- Add requirements.txt
- Record video of the solution
- Conclusion Project
- Translate the notebook