Creating the 2030 United States Census using Machine Learning
A few months ago we started working on building a forecast for the United States 2030 Census at our company.
The reason why we started doing it was that if a corporation or a real estate investor can understand how many people will live in a specific city and neighborhood in the years to come, then they can do better planning, get better returns on their investment and be ahead of their competitors.
However, forecasting the movement of people is not an easy task. And that is where Machine Learning and the use of Data Analytics come in.
Step 1: Data Collection: We began by gathering comprehensive datasets encompassing historical population data, socio-economic factors, migration patterns, and any other relevant variables.
Step 2: Data Preprocessing: We needed to clean and preprocess the collected data to ensure it is consistent, accurate, and ready for analysis. This step was crucial to enhance the reliability of the machine learning model.
Step 3: Model Selection: We selected a specific statistical method that allowed us to input 3 years of historical data, run the model, and generate the forecast for the following year.
What was interesting about that approach was that we already had the real data for that forecasted year.
This allowed us to understand if the forecast was accurate or not and calibrate the model. We did this 5 times since we had 12 years of historic data.
Step 4: Fine-Tuning: Then we spent time optimizing the model by fine-tuning hyperparameters to enhance its accuracy. This iterative process involves adjusting the model’s settings to achieve optimal results.
Should you be interested in exploring some of our findings, we have made a dataset accessible on Snowflake, and also a dataset available on Databricks, two of the most prominent Data Analytics platforms.
