Building a Model to Predict Unemployment

Building a Model to Predict Unemployment

Overview:
Use Python as a platform to build a model to predict unemployment by combining traditional predictors and Google Trends data.
1. First use the three traditional indicators of GDP, consumer confidence index and consumer price inflation (which is usually negatively correlated with the unemployment rate) to predict the unemployment rate;
2. Add Google Trends data of four search terms (usually positively correlated with unemployment rate) to predict unemployment rate;

Require:
1. All data is unified into monthly, and the time axis of the data used is unified from April 2004 to April 2023, and the training set and test set are split during this period.
2. Comparing the prediction effect when there are only three traditional predictors and the Google Trends data with four search terms, it is best to draw a conclusion: the prediction effect is better after adding Google Trends data.
3. Provide full source code, including detailed comments and readme files (if necessary).


I am looking for a data scientist who can build a predictive model for unemployment. The ideal candidate should have experience in analyzing historical unemployment rates and using them as the primary data for the analysis. Additionally, familiarity with economic indicators and de mographic data would be beneficial .

Skills and Experience:
- Strong background in data analysis and modeling
- Proficiency in statistical modeling techniques such as linear regression, logistic regression, and decision trees
- Experience in working with historical unemployment rates and economic indicators
- Familiarity with demographic data and its impact on unemployment
- Ability to interpret and analyze complex datasets
- Attention to detail and accuracy in predictions

Note: No data collection is required, I will provide all the data that will be used.