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Jupyter notebooks for analysis of US federal debt levels, tax revenues, budget deficit, evolution of yields on treasury borrowings, treasury yield curves and inflation expectations, unemployment and participation rates, quantitative easing, industrial production, personal consumption and savings. All analysis is based on data provided by FRED.
This project is an end-to-end machine learning solution for analyzing the unemployment rate of a particular region. The project provides a user-friendly interface for visualizing the estimated number of employees and the unemployment rate according to different regions of India during Covid-19.
Unemployment Analysis with machine learning .Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force. We have seen a sharp increase in the unemployment rate during Covid-19.
This project shows step by step how to clean & prepare a data set(data wrangling) for analysis using different tools like Pandas etc. and further do a Exploratory data analysis to give an idea and make the data usable for further reaserches and models.
This program seeks to explore the relationship between the Effective Fed Funds Rate (target variable) and the most common parameters the Fed uses to justify their rate hikes/drop decisions when communicating to public, namely personal consumption expenditures inflation, real gross domestic production growth, and unemployment rate (predictor vari…
The aim of this project is to create automated report which shows current coronavirus information scraped from Wikipedia and unemployment analysis based on downloaded dataset from OECD website.