PCE Price Index Annual Change in the United States increased to 2.74 percent in August from 2.60 percent in July of 2025. This dataset includes a chart with historical data for the United States PCE Price Index Annual Change.

Core PCE Price Index in the United States increased to 126.71 points in August from 126.42 points in July of 2025. This dataset provides - United States Core Pce Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Core PCE Price Index Annual Change in the United States increased to 2.91 percent in August from 2.85 percent in July of 2025. This dataset includes a chart with historical data for the United States Core Pce Price Index Annual Change.

Overview with Chart & Report: Core PCE Price Index y/y reflects price changes on durable and non-durable goods and services in the reporting month compared to the same month of the previous year. Food and energy prices are

PCE Price Index in the United States increased to 127.29 points in August from 126.95 points in July of 2025. This dataset provides the latest reported value for - United States Personal Consumption Expenditure Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States PCE: 1992p: NDG: Others: Magazines & Newspaper: Newspaper data was reported at 13.764 USD bn in Sep 1999. This records a decrease from the previous number of 13.893 USD bn for Aug 1999. United States PCE: 1992p: NDG: Others: Magazines & Newspaper: Newspaper data is updated monthly, averaging 11.551 USD bn from Jan 1989 (Median) to Sep 1999, with 129 observations. The data reached an all-time high of 14.076 USD bn in Jul 1999 and a record low of 10.379 USD bn in May 1992. United States PCE: 1992p: NDG: Others: Magazines & Newspaper: Newspaper data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A211: NIPA 1995: Personal Consumption Expenditure: 1992 Price.

United States IPD: PCE: NDG: Others: Magazines & Newspaper: Newspaper data was reported at 127.220 1992=100 in Sep 1999. This records an increase from the previous number of 126.810 1992=100 for Aug 1999. United States IPD: PCE: NDG: Others: Magazines & Newspaper: Newspaper data is updated monthly, averaging 108.170 1992=100 from Jan 1989 (Median) to Sep 1999, with 129 observations. The data reached an all-time high of 127.220 1992=100 in Sep 1999 and a record low of 82.200 1992=100 in Jan 1989. United States IPD: PCE: NDG: Others: Magazines & Newspaper: Newspaper data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A213: NIPA 1995: Implicit Price Deflator: Personal Consumption Expenditure: 1992=100.

United States PCE: 1992p: NDG: Others: Magazines & Newspaper data was reported at 28.950 USD bn in Sep 1999. This records a decrease from the previous number of 29.220 USD bn for Aug 1999. United States PCE: 1992p: NDG: Others: Magazines & Newspaper data is updated monthly, averaging 23.447 USD bn from Jan 1989 (Median) to Sep 1999, with 129 observations. The data reached an all-time high of 29.606 USD bn in Jul 1999 and a record low of 20.978 USD bn in May 1992. United States PCE: 1992p: NDG: Others: Magazines & Newspaper data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A211: NIPA 1995: Personal Consumption Expenditure: 1992 Price.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Consumer Spending And Sentiment (EMVMACROCONSUME) from Jan 1985 to Oct 2025 about volatility, uncertainty, equity, PCE, consumption expenditures, consumption, personal, and USA.

Context

As we all know, Fake-News has become the centre of attraction worldwide because of its hazardous impact on our society. One of the recent example is spread of Fake-news related to Covid-19 cure, precautions, and symptoms and you must be understood by now, how dangerous this bogus information could be. Distorted piece of information propagated at the times of election for achieving political agenda is not hidden from anyone.

Fake news is quickly becoming an epidemic, and it alarms and angers me how often and how rapidly totally fabricated stories circulate. Why? In the first place, the deceptive effect: the fact that if a lie is repeated enough times, you’ll begin to believe it’s true.

You understand by now that fake news and other types of false information can take on various appearances. They can likewise have significant effects, because information shapes our world view: we make important decisions based on information. We form an idea about people or a situation by obtaining information. So if the information we saw on the Web is invented, false, exaggerated or distorted, we won’t make good decisions.

Hence, Its in dire need to do something about it and It's a Big Data problem, where data scientist can contribute from their end to fight against Fake-News.

Content

Although, fighting against fake-News is a big data problem but I have created this small dataset having approx. 10,000 piece of news article and meta-data scraped through approx. 600 web-pages of Politifact website to analyse it using data science skills and get some insights of how can we stop spread of misinformation at broader aspect and what approach will give us better accuracy to achieve the same.

This dataset is having 6 attributes among which News_Headline is the most important to us in order to classify news as FALSE or TRUE. As you notice the Label attribute clearly, there are 6 classes specified in it. So, it's totally up-to you whether you want to use my dataset for multi-class classification or convert these class labels into FALSE or TRUE and then, perform binary classification. Although, for your convenience, I will write a notebook on how to convert this dataset from multi-class to binary-class. To deal with the text data, you need to have good hands on practice on NLP & Data-Mining concepts.

• News_Headline - contains piece of information that has to be analysed.
• Link_Of_News - contains url of News Headlines specified in very first column.
• Source - this column contains author names who has posted the information on facebook, instagram, twitter or any other social-media platform.
• Stated_On - This column contains date when the information is posted by the authors on different social-media platforms.
• Date - This column contains date when this piece of information is analysed by politifact team of fact-checkers in order to labelize as FAKE or REAL.
• Label - This column contains 5 class labels : True, Mostly-True, Half-True, Barely-True, False, Pants on Fire.

So, you can either perform multi-class classification on it or convert Mostly-True, Half-True, Barely-True as True and drop Pants on Fire and perform Binary-class classification.

Acknowledgements

• A very Big thanks to fact-checking team of Politifact.com website as they provide with correct labels by working hard manually. So that we data science people can take advantage to train our models on such labels and make better models. These are some research papers that will help you to get start with the project and clear your fundamentals.

• "https://journals.sagepub.com/doi/full/10.1177/2053951719843310">Big Data and quality data for fake news and misinformation detection by Fatemeh Torabi Asr, Maite Taboada

• "https://asistdl.onlinelibrary.wiley.com/doi/full/10.1002/pra2.2015.145052010082">Automatic deception detection: Methods for finding fake news by Nadia K. Conroy Victoria L. Rubin Yimin Chen

Inspiration

• I want to see which approach can solve this problem of combating Fake-News with greater accuracy.

Analysis of the US stock market retreat from record highs driven by persistent inflation data and losses in big tech stocks, despite indexes posting strong monthly gains.

The tldr_news dataset was constructed by collecting a daily tech newsletter (available at https://tldr.tech/newsletter). Then for every piece of news, the "headline" and its corresponding "content" were collected. Such a dataset can be used to train a model to generate a headline from a input piece of text.

United States PCE: PI: sa: NG: Others: Magazines, Newspaper etc (MN) data was reported at 124.923 2000=100 in May 2009. This records an increase from the previous number of 123.816 2000=100 for Apr 2009. United States PCE: PI: sa: NG: Others: Magazines, Newspaper etc (MN) data is updated monthly, averaging 57.819 2000=100 from Jan 1959 (Median) to May 2009, with 605 observations. The data reached an all-time high of 124.923 2000=100 in May 2009 and a record low of 14.257 2000=100 in Apr 1959. United States PCE: PI: sa: NG: Others: Magazines, Newspaper etc (MN) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A183: NIPA 2003: Personal Consumption Expenditure.

Personal Spending in the United States increased 0.60 percent in August of 2025 over the previous month. This dataset provides the latest reported value for - United States Personal Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context

MachineHack Predict The News Category Hackathon

Content

From the beginning, since the first printed newspaper, every news that makes into a page has had a specific section allotted to it. Although pretty much everything changed in newspapers from the ink to the type of paper used, this proper categorization of news was carried over by generations and even to the digital versions of the newspaper. Newspaper articles are not limited to a few topics or subjects, it covers a wide range of interests from politics to sports to movies and so on. For long, this process of sectioning was done manually by people but now technology can do it without much effort. In this hackathon, Data Science and Machine Learning enthusiasts like you will use Natural Language Processing to predict which genre or category a piece of news will fall in to from the story. Size of training set: 7,628 records Size of test set: 2,748 records FEATURES: STORY: A part of the main content of the article to be published as a piece of news. SECTION: The genre/category the STORY falls in. There are four distinct sections where each story may fall in to. The Sections are labelled as follows : Politics: 0 Technology: 1 Entertainment: 2 Business: 3

Acknowledgements

MachineHack

Core consumer prices in the United States increased 3 percent in September of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States IPD: PCE: NG: Others: Magazines, Newspaper & Sheet Music (MN&SM) data was reported at 114.765 1996=100 in Oct 2003. This records an increase from the previous number of 114.412 1996=100 for Sep 2003. United States IPD: PCE: NG: Others: Magazines, Newspaper & Sheet Music (MN&SM) data is updated monthly, averaging 52.338 1996=100 from Jan 1959 (Median) to Oct 2003, with 538 observations. The data reached an all-time high of 114.765 1996=100 in Oct 2003 and a record low of 15.332 1996=100 in Apr 1959. United States IPD: PCE: NG: Others: Magazines, Newspaper & Sheet Music (MN&SM) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A205: NIPA 1999: Implicit Price Deflator: Personal Consumption Expenditure: 1996=100.

PCE: 2000p: sa: NG: Others: Magazines, Newspaper etc (MN) data was reported at 36.769 USD bn in May 2009. This records a decrease from the previous number of 37.032 USD bn for Apr 2009. PCE: 2000p: sa: NG: Others: Magazines, Newspaper etc (MN) data is updated monthly, averaging 33.685 USD bn from Jan 1990 (Median) to May 2009, with 233 observations. The data reached an all-time high of 44.887 USD bn in May 2008 and a record low of 26.340 USD bn in May 1992. PCE: 2000p: sa: NG: Others: Magazines, Newspaper etc (MN) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A183: NIPA 2003: Personal Consumption Expenditure.

United States GDP: PCE: NDG: Others: Magazines, Newspaper & Sheet Music (MN&SM) data was reported at 35.013 USD bn in Oct 2003. This records a decrease from the previous number of 35.269 USD bn for Sep 2003. United States GDP: PCE: NDG: Others: Magazines, Newspaper & Sheet Music (MN&SM) data is updated monthly, averaging 12.937 USD bn from Jan 1959 (Median) to Oct 2003, with 538 observations. The data reached an all-time high of 35.837 USD bn in Aug 2001 and a record low of 1.979 USD bn in Dec 1961. United States GDP: PCE: NDG: Others: Magazines, Newspaper & Sheet Music (MN&SM) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A203: NIPA 1999: Personal Consumption Expenditure.

Implicit Regional Price Deflator for Virginia Beach-Norfolk-Newport News, VA-NC (MSA) was 117.31000 US PCE Index 2009 =100 in January of 2023, according to the United States Federal Reserve. Historically, Implicit Regional Price Deflator for Virginia Beach-Norfolk-Newport News, VA-NC (MSA) reached a record high of 117.31000 in January of 2023 and a record low of 93.20200 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Implicit Regional Price Deflator for Virginia Beach-Norfolk-Newport News, VA-NC (MSA) - last updated from the United States Federal Reserve on December of 2025.