This repository contains datasets and Python scripts for applying mathematical concepts to real-world problems. It serves as a resource for analyzing economic indicators, sentiment analysis, consumer data, and more.
- Source: FRED (Federal Reserve Economic Data)
- Description: Contains the Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL). This index measures the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services (e.g., food, energy, housing). It covers approximately 88% of the total population and is a primary metric for identifying periods of inflation or deflation. Note that the core CPI (CPILFESL) is often used for more accurate long-term trends as it excludes volatile food and energy prices.
- Source: FRED
- Description: Data for Real Personal Consumption Expenditures. This dataset tracks consumer spending on goods and services, adjusted for inflation, providing a key indicator of economic health and consumer demand.
- Source: GitHub (manthanpatel98)
- Description: A dataset designed for Sentiment Analysis containing 10,000 rows and 8 columns. It includes restaurant reviews labeled as "Positive" or "Negative." The data is suitable for training machine learning models (e.g., LSTM, RandomForest, SVM) to predict sentiment. Stemming has been applied using the PortStemmer method.
- Source: USDA Economic Research Service (ERS)
- Description: Provides average retail prices for over 150 fruits and vegetables based on 2013-2023 retail scanner data. Prices are reported per pound, pint, or edible cup equivalent. This data supports research into diet affordability and the cost of meeting federal dietary guidelines. Note: Due to methodology changes, this data is not suitable for year-to-year price comparisons.
- Source: Self-generated
- Description: A simple dataset outlining sales targets broken down by region.
- Source: US Census Bureau
- Description: Contains 2020 State-level population data for the United States.