According to a survey conducted by Deloitte in August 2022, ** percent of respondents perceived that prices has increased for grocery in India. By contrast, ** percent observed that prices increased for alcohol and tobacco.

For a quick summary of the case study, please click "US Economy Powerpoint" and download the Powerpoint.

This dataset was inspired by rising prices for essential goods, the abnormally high inflation rate in March of 7.9 percent of this year, and the 30 trillion-dollar debt that we have. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

I labeled all of the datasets to be self-explanatory based off of the title of the datasets. The US Economy Notebook has most of the code that I used as well as the four of the six phases of data analysis. The last two phases are in the US Economy Powerpoint. The "US Historical Inflation Rates" dataset could have also been labeled "The Inflation Of The US Dollar Month By Month". Lastly, the Average Sales of Houses in Jan is just a filtered version of "Average Sales of Houses in the US" dataset.

In 2025, around ** percent of people said the higher prices were going to impact their plans on Valentine's and/ or Galentine's Day. ** percent of people said it would not change their plans.

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

In August 2024, the global producer price index (PPI)/ wholesale price index inflation (WPI), excluding the U.S., stood at 174.01. In the United States, the index value amounted to 165.3. The PPI/WPI inflation tracks changes in the level of prices received by domestic producers for their goods and services. Consumer habits and price increases As of August 2024, consumers considered rising prices and inflation to be their biggest worry. Consumers are expressing this worry in numerous ways. Globally, over 20 percent of consumers have said they would shop less and seek cheaper options in response to price increases. Moreover, nearly 70 percent of surveyed consumers globally reduced their gift giving to extended family and friends during the holiday season in 2023 to stretch their budgets further. Impact of inflation on emerging economies Notably, emerging economies have a higher WPI value than advanced economies. Between 2021 and 2022, the average inflation rate in developing and emerging economies increased from 5.9 percent to 9.8 percent, before falling slightly to 8.34 percent in 2023. The countries with the highest inflation rates in 2023 include many developing and emerging economies, such as Zimbabwe, Argentina, Turkey, and Suriname.

Food price increases hit the egg category the hardest between December 2021 and December 2024 in the United States. The price of eggs increased by **** percent in 2024.

This dataset contains the predicted prices of the asset Rising Phoenix over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

1. Understanding the Features

Brand and Model: Analyze how different brands and models influence car prices. Are luxury brands significantly more expensive than economy brands? Year of Manufacture: Discuss the depreciation of car prices over time. How does the year affect pricing, and are there notable trends for specific brands? Engine Size: Explore the relationship between engine size and price. Does a larger engine correlate with a higher price, and how does this vary across different fuel types? Fuel Type: Evaluate how fuel types (Petrol, Diesel, Electric, Hybrid) impact pricing. Are electric vehicles priced higher due to their technology, or do they vary based on other factors? Transmission: Discuss if manual or automatic transmissions affect car pricing, especially in different markets or demographics.

2. Price Predictions and Modeling

Machine Learning Models: Explore which models (e.g., linear regression, decision trees, or ensemble methods) are best suited for predicting car prices using this dataset. Feature Importance: Discuss the importance of different features in predicting price. Which features contribute most to the price prediction accuracy, and how can feature selection improve the model?

3. Market Trends and Insights

Price Distribution: Analyze the distribution of car prices. Are there a lot of high-priced luxury cars, or is the dataset skewed towards more affordable options? Mileage vs. Price: Investigate the correlation between mileage and price. How does higher mileage affect pricing, and is there a threshold where price reduction becomes significant? Condition Impact: Discuss how the condition of the car (New, Used, Like New) influences the price. Are there significant price drops for used cars compared to new ones?

4. Regional Analysis

Location Impact: If geographic location is included, discuss how prices vary by region. Are there markets where certain brands/models are more popular and thus command higher prices? Economic Factors: Consider how broader economic factors (like inflation, fuel prices, and consumer preferences) might influence car prices in different regions.

5. Future Developments and Trends

Electric Vehicle Market: With the rise of electric vehicles, discuss how this dataset reflects the growing demand and pricing trends for EVs compared to traditional fuel cars. Impact of Technology: Consider how advancements in technology, safety features, and autonomous driving capabilities might influence future pricing.

6. Limitations of the Dataset

Data Completeness: Discuss any potential limitations in the dataset, such as missing values or biases in the data collection process. Generalization: Reflect on the ability to generalize the findings from this dataset to broader car markets or regions. Are there potential confounding factors that should be considered?

7. Potential Applications

Pricing Strategies: How can dealerships or private sellers utilize insights from this dataset to set competitive pricing? Consumer Decision-Making: Discuss how consumers can leverage this dataset to make informed purchasing decisions based on price predictions and feature evaluations.

These discussion points can help guide deeper analysis and insights into the Car Price Prediction dataset, making it a valuable resource for both academic and practical applications. If you have specific areas you want to focus on, let me know!

Graph and download economic data for Home Price Index (High Tier) for Portland, Oregon (POXRHTNSA) from Jan 1987 to Sep 2025 about high tier, Portland, HPI, housing, price index, indexes, price, and USA.

The controversy over how much to charge for health products in the developing world rests, in part, on whether higher prices can increase use, either by targeting distribution to high-use households (a screening effect), or by stimulating use psychologically through a sunk-cost effect. We develop a methodology for separating these two effects. We implement the methodology in a field experiment in Zambia using door-to-door marketing of a home water purification solution. We find evidence of economically important screening effects. By contrast, we find no consistent evidence of sunk-cost effects. (JEL C93, D12, I11, M31, O12)

Cost of food in South Africa increased 3.90 percent in October of 2025 over the same month in the previous year. This dataset provides the latest reported value for - South Africa Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The FAO vegetable oil Price Index* reached 178.32 index points in June of 2008 during the financial crisis. During the pandemic, the price index rose to 184.56 points in October of 2021. After the start of the war in Ukraine, the index jumped to over 251 points in March of 2022. As of September 2025, the index had slightly declined to 167.9 points. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page. For further information about the Russian invasion of Ukraine, please visit our dedicated page on the topic.

Graph and download economic data for Home Price Index (High Tier) for Washington D.C. (WDXRHTNSA) from Jan 1987 to Sep 2025 about high tier, Washington, HPI, housing, price index, indexes, price, and USA.

Import Prices in Chile increased to 105.76 points in the third quarter of 2025 from 105.50 points in the second quarter of 2025. This dataset provides the latest reported value for - Chile Import Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

A chunk of U.S. consumers stockpiled items for fear of future shortages, emergencies, or rising prices, as revealed in a survey conducted in August 2025. The leading product was toilet paper, with ** percent of consumers having stocked it. Canned/frozen foods were the leading food product stockpiled in case of a future situation, as chosen by ** percent of consumers.

Key information about House Prices Growth

• Japan house prices grew 3.4% YoY in Aug 2025, following an increase of 4.7% YoY in the previous month.
• YoY growth data is updated monthly, available from Apr 2009 to Aug 2025, with an average growth rate of 1.3%.
• House price data reached an all-time high of 10.2% in Apr 2022 and a record low of -9.4% in Apr 2009.

This dataset contains the predicted prices of the asset Pepe Goes Higher over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Highstreet price data for 2025-11-28 including currency, value, high, low, open, close, and percentage difference.

Key information about House Prices Growth

• Canada house prices dropped 1.8% YoY in Oct 2025, following a decrease of 1.8% YoY in the previous month.
• YoY growth data is updated monthly, available from Jan 1982 to Oct 2025, with an average growth rate of 5.1%.
• House price data reached an all-time high of 16.5% in Mar 1989 and a record low of -9.7% in Apr 1991.

Learn about the factors contributing to the current maize price situation, including supply and demand, weather conditions, and government policies. Discover how rising prices are impacting farmers, traders, and consumers worldwide, and why it's important to stay informed about the volatile maize market.