About this dataset

Context

The dataset illustrates the median household income in Mobile, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Mobile decreased by $1,596 (3.19%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 4 years and declined for 7 years.

https://i.neilsberg.com/ch/mobile-al-median-household-income-trend.jpeg" alt="Mobile, AL median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mobile median household income. You can refer the same here

This synthetic but realistic dataset contains 90+ customer reviews for 6 smartphone models (from Apple, Samsung, and Google), along with:

Product specifications (Price, Screen Size, Battery, Camera, RAM, Storage, 5G, Water Resistance) Customer reviews (Star Ratings, Review Text, Verified Purchase Status) Sales data (Units Sold per Model) Potential Use Cases: ✅ Feature importance analysis (Which specs drive ratings?) ✅ Sentiment analysis (NLP on reviews) ✅ Pricing strategy optimization ✅ Market research (Comparing Apple vs. Samsung vs. Google)

Smartphone Customer Satisfaction Survey Objective: Understand how product features influence purchasing decisions and satisfaction.

Section 1: Demographic & Purchase Behavior Which smartphone brand did you purchase?

☐ Apple ☐ Samsung ☐ Google Maps to brand column. Which model did you purchase?

Apple: ☐ iPhone 14 | ☐ iPhone 15 Samsung: ☐ Galaxy S22 | ☐ Galaxy S23 Google: ☐ Pixel 7 | ☐ Pixel 8 Maps to model_name column. Where did you purchase the phone?

☐ Online (e.g., Amazon, Brand Website) ☐ Physical Store Justifies verified_purchase (assumed online = verified). Section 2: Product Feature Ratings How would you rate the following features? (1 = Poor, 5 = Excellent)

Battery Life: ⭐⭐⭐⭐⭐ Camera Quality: ⭐⭐⭐⭐⭐ Screen Size: ⭐⭐⭐⭐⭐ Performance (RAM/Processor): ⭐⭐⭐⭐⭐ Aggregates into star_rating (average of these). Which feature is MOST important to you?

☐ Battery Life ☐ Camera Quality ☐ Screen Size ☐ Performance ☐ Price Explains review_text keywords (e.g., "battery" mentions). Section 3: Price & Satisfaction How do you feel about the price of your phone?

☐ Very Affordable ☐ Fairly Priced ☐ Slightly Expensive ☐ Too Expensive Maps to price vs. star_rating correlation. Would you recommend this phone to others?

☐ Definitely Yes ☐ Probably Yes ☐ Neutral ☐ Probably No ☐ Definitely No

Linked to star_rating (5 = Definitely Yes).

Column Details (Metadata)

Column Name (Type) Description "Example"**

model_id (Integer) Unique ID for each phone model 1 (iPhone 14)

brand (String) Manufacturer (Apple, Samsung, Google) "Apple"

model_name (String) Name of the phone model "iPhone 15"

price (Integer) Price in USD 999

screen_size (Float) Screen size in inches 6.1

battery (Integer) Battery capacity in mAh 4000

camera_main (String) Main camera resolution (MP) "48MP"

ram (Integer) RAM in GB 8

storage (Integer) Storage in GB 128

has_5g (Boolean) Whether the phone supports 5G TRUE

water_resistant (String) Water resistance rating (IP68 or None) "IP68"

units_sold (Integer) Estimated units sold (for market analysis) 15000

review_id (Integer) Unique ID for each review 1

user_name (String) Randomly generated reviewer name "John"

star_rating (Integer) Rating from 1 (worst) to 5 (best) 5

verified_purchase (Boolean) Whether the reviewer bought the product TRUE

review_date (Date) Date of the review (YYYY-MM-DD) "2023-05-10"

review_text (String) Simulated review text based on features & rating "The 48MP camera is amazing!"

Suggested Analysis Ideas to inspire data analysis: A. Feature Impact on Ratings Regression: star_rating ~ battery + camera_main + price Key drivers: Does battery life affect ratings more than camera quality?

B. Sentiment Analysis (NLP) Use tidytext (R) or NLTK (Python) to extract most-loved/hated features. Example: r library(tidytext) reviews_tidy <- final_data %>% unnest_tokens(word, review_text) reviews_tidy %>% count(word, sort = TRUE) %>% filter(n > 5)

C. Brand Comparison Apple vs. Samsung vs. Google: Which brand has higher average ratings? Price sensitivity: Do cheaper phones (e.g., Pixel) get better value ratings?

D. Sales vs. Features Correlation: units_sold ~ price + brand Premium segment analysis: Do iPhones sell more despite higher prices?

License

CC0

Original Data Source: Smartphone Feature Optimization (Marketing Mix)

Context

The Dow Jones Industrial Average is one of the most followed stock market indexes by investors, financial professionals and the media.

Content

It measures the daily price movements of 30 large American companies on the Nasdaq and the New York Stock Exchange. The Dow Jones Industrial Average is widely viewed as a proxy for general market conditions and even the economy of the United States.

This Dow Jones Industrial Average dataset is downloaded from https://www.investing.com/indices/us-30-historical-data, including 2767 closing records from January 4th 2009 to December 31st 2019.

This is a version of the gas prices dataset used in the following paper: Data Polygamy: The Many-Many Relationships among Urban Spatio-Temporal Data Sets, F. Chirigati, H. Doraiswamy, T. Damoulas, and J. Freire. In Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data (SIGMOD), 2016 The dataset includes records of the average gasoline price in dollars per gallon for New York, from 2000 to 2014. The original data is available at the U.S. Energy Information Administration.

Displacement risk indicator classifying census tracts according to apartment rent prices in census tracts. We classify apartment rent along two dimensions:The median rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in median rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Median rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing.Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development

Get statistical data on weekly cattle prices in Ontario.

Data includes:

• Ontario large and medium frame feeder and fed cattle prices
• market price
• feed cost
• feeder steer margin

Statistical data are compiled to serve as a source of agriculture and food statistics for the province of Ontario. Data are prepared primarily by Statistics and Economics staff of the Ministry of Agriculture, Food and Rural Affairs, in co-operation with the Agriculture Division of Statistics Canada and various government departments and farm marketing boards.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Excluding apartments, measured in € Figure changed on the 27/6/16 as revised data received from the Local authority

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Licensed under: Creative Commons Attribution 4.0

Analysis of ‘California Housing Prices Data (5 new features!)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fedesoriano/california-housing-prices-data-extra-features on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Similar Datasets:

Boston House Prices: LINK

Context

This is the dataset is a modified version of the California Housing Data used in the paper Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being too toyish and too cumbersome.

The data contains information from the 1990 California census. So although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning.

Modifications with respect to the original data

This dataset includes 5 extra features defined by me: "Distance to coast", "Distance to Los Angeles", "Distance to San Diego", "Distance to San Jose", and "Distance to San Francisco". These extra features try to account for the distance to the nearest coast and the distance to the centre of the largest cities in California.

The distances were calculated using the Haversine formula with the Longitude and Latitude:

https://wikimedia.org/api/rest_v1/media/math/render/svg/a65dbbde43ff45bacd2505fcf32b44fc7dcd8cc0" alt="">

where:

• phi_1 and phi_2 are the Latitudes of point 1 and point 2, respectively
• lambda_1 and lambda_2 are the Longitudes of point 1 and point 2, respectively
• r is the radius of the Earth (6371km)

Content

The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. The columns are as follows, their names are pretty self-explanatory:

1) Median House Value: Median house value for households within a block (measured in US Dollars) [$] 2) Median Income: Median income for households within a block of houses (measured in tens of thousands of US Dollars) [10k$] 3) Median Age: Median age of a house within a block; a lower number is a newer building [years] 4) Total Rooms: Total number of rooms within a block 5) Total Bedrooms: Total number of bedrooms within a block 6) Population: Total number of people residing within a block 7) Households: Total number of households, a group of people residing within a home unit, for a block 8) Latitude: A measure of how far north a house is; a higher value is farther north [°] 9) Longitude: A measure of how far west a house is; a higher value is farther west [°] 10) Distance to coast: Distance to the nearest coast point [m] 11) Distance to Los Angeles: Distance to the centre of Los Angeles [m] 12) Distance to San Diego: Distance to the centre of San Diego [m] 13) Distance to San Jose: Distance to the centre of San Jose [m] 14) Distance to San Francisco: Distance to the centre of San Francisco [m]

Source

This data was entirely modified and cleaned by me. The original data (without the distance features) was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.

The original dataset can be found under the following link: https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html

--- Original source retains full ownership of the source dataset ---

1) Data Introduction • The Restaurant Dataset includes key restaurant-related attributes such as location, average cost, ratings, and the type of dish (target variable) provided with service information for various restaurants worldwide.

2) Data Utilization (1) Restaurant Dataset has characteristics that: • This dataset provides a variety of information, including the restaurant's name, location (country, city, address, latitude and longitude), average cost of meals, calls, table reservations and online delivery, price point, ratings, and vote counts. (2) Restaurant Dataset can be used to: • Cooking Classification Model Development: Using characteristics such as location, price, service, and rating of a restaurant, we can build a machine learning-based cooking type prediction model. • Establish location and marketing strategies: By analyzing regional popular dishes, ratings, and price point data, you can use them to select new restaurant locations and establish customized marketing strategies.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Excluding apartments, measured in € Figure changed on the 27/6/16 as revised data received from the Local authority

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Measured in €

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Measured in €

Gasoline fell to 2.08 USD/Gal on June 27, 2025, down 1.15% from the previous day. Over the past month, Gasoline's price has fallen 0.73%, and is down 17.13% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on June of 2025.

Concept: Average cost of credit operations that make up the portfolio of loans, financing and leasing operations of financial institutions belonging to the National Financial System. It includes the totality of outstanding operations classified as current assets, regardless of the date of the credit lending. Source: Central Bank of Brazil � Statistics Department 27657-average-cost-of-outstanding-loans---nonearmarked---non-financial-corporations---overdraft 27657-average-cost-of-outstanding-loans---nonearmarked---non-financial-corporations---overdraft

Coupes and convertibles were the most expensive used car types in the United States as of February 2023, priced on average at around 49,800 and 46,700 U.S. dollars respectively. In contrast, used wagons and hatchbacks were more affordable, at an average of 20,000 and 24,200 U.S. dollars. The overall used vehicle average list price had been steadily rising between mid-year 2020 and mid-year 2022, but dipped in June 2023.

This dataset includes data on China's egg market transaction prices from 2014-2021 and consists of 2 parts: (1) text data including the national egg market retail price statistics table (weekly), the national egg market wholesale price statistics table (daily), the national egg market wholesale price change information, the national and 12 provinces (autonomous regions and municipalities directly under the central government) monthly average wholesale prices and information on the rate of change; (2) picture data sets include monthly average wholesale prices and weekly retail prices and their rate of change line graphs for the national egg market from 2014-2021.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Flambeau town. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Flambeau town. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Flambeau town, the median household income stands at $71,250 for householders within the 25 to 44 years age group, followed by $71,154 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $55,714.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Flambeau town median household income by age. You can refer the same here