Dataset Description

This dataset contains Consumer Price Index (CPI) forecast data for food items in the USA from 2002 to 2023. It consists of 41,624 rows and 6 columns:

1. consumer_price_index_item – The food category (e.g., Dairy products, All food, etc.).
2. month_of_forecast – The month in which the forecast was made.
3. year_of_forecast – The year in which the forecast was made.
4. year_being_forecast – The year for which the forecast applies.
5. attribute – The type of forecast data (e.g., Lower bound, Upper bound, Midpoint of prediction interval).
6. forecast_percent_change – The predicted percentage change in the CPI.

Dataset is Available here: https://www.kaggle.com/datasets/hrish4/cpi-inflation-analysis-and-forecasting

DAX Code and Explanation are given here : https://colorstech.net/power-bi/food-price-inflation-trends-in-the-usa-2002-2023-dashboard%f0%9f%a5%a4%f0%9f%a5%97%f0%9f%8d%94%f0%9f%8d%97%f0%9f%8d%9f%f0%9f%a5%93/

Potential Analyses

1. Inflation Trend Analysis
   • Analyze CPI trends over time for different food categories.
   • Compare forecasted vs. actual CPI changes (if actual values are available).
2. Category-wise Price Fluctuation
   • Identify which food items have the highest/lowest price volatility.
   • Compare inflation trends across different food categories.
3. Forecast Accuracy Evaluation
   • If actual CPI data is available, compare the forecasts with real outcomes.
   • Assess forecast errors and biases.
4. Seasonal Patterns
   • Investigate whether food prices show seasonal trends (e.g., higher in certain months).
5. Economic Impact Assessment
   • Evaluate how CPI fluctuations affect consumer purchasing power.
   • Analyze the correlation between food price inflation and macroeconomic factors (if additional data is available).