Consumer_Price_Index_of_India

A Comparative Study of Machine Learning models And Deep learning model For Consumer Price index of India The Consumer Price Index is a key economic indicator that measures the average change in prices of goods and services consumed by households over a specific period of time. Accurate prediction of the Consumer Price Index is crucial for policymakers, investors, and other stakeholders in making informed decisions regarding inflation, monetary policy, and market trends. Given the importance of accurate predictions, researchers have explored the use of the three best predictive models to forecast the Consumer Price Index. These models include machine learning models such as Support Vector Machine, Random Forest and as well as deep learning models such as Long Short-Term Memory. Methods To conduct this comparative study, we collected historical data on the Consumer Price Index of India for 5 years from reliable sources such as the Reserve Bank of India and the Ministry of Statistics and Programmed Implementation. Next, we applied machine learning and deep learning techniques to the dataset and evaluated their performance using metrics such as mean squared error. The dataset contains urban and rural consumer price index values, as well as the corresponding variables that have an impact on price changes, such as inflation rate, GDP growth rate, and population density. CPI is important to measure inflation and the purchasing power of consumers, making accurate predictions essential for economic analysis and decision-making. The price data are collected from selected 1114 urban Markets and 1181 villages covering all States/UTs through personal visits by field staff of Field Operations Division of NSO, MoSPI on a weekly roster. During the month of March 2023, NSO collected prices from 100% villages and 98.5% urban Markets while the Market-wise prices reported therein were 90.4% for rural and 93.4% for urban