This dataset contains a variety of laptop specifications and the price of each device in Euros, the goal is to create a machine learning model to train the model to predict laptop prices.

The dataset is allowed to be modified during the analysis.

Details of the columns:

| | Columns | Description | Type | |--------------------|--------------------------------------|---------------------| | 1 | Company | Laptop manufacturer. | Categorical | | 2 | Product | Brand and Model. | Categorical | | 3 | TypeName | Type (Notebook, Ultrabook, Gaming, etc.)| Categorical | | 4 | Inches | Screen Size. | Numerical (float)| | 5 | ScreenResolution | Screen Resolution. | Categorical | | 6 | CPU_Company | Central Processing Unit (CPU) manufacturer. | Categorical | | 7 | CPU_Type | Central Processing Unit (CPU) type. | Categorical | | 8 | CPU_Frequency | Central Processing Unit (CPU) Frequency (GHz).| Numerical (float) | | 9 | RAM (GB) | Laptop RAM. | Numerical (int) | | 10 | Memory | Hard Disk / SSD Memory. | Categorical | | 11 | GPU_Company | Graphics Processing Units (GPU) manufacturer. | Categorical | | 12 | GPU_Type | Graphics Processing Units (GPU) type. | Categorical | | 13 | OpSys | Operating System. | Categorical | | 14 | Weight (kg) | Laptop Weight (kg). | Numerical (float) | | 15 | Price (Euro) | Laptop price (Euro). | Numerical (float) |

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q1 2025 about sales, median, housing, and USA.

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Essential for: - Product Development - Pricing Strategy - Market Analysis - Competitor Benchmarking - Total Cost of Ownership (TCO)

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Use our Stock prices dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

Gain valuable insights into the automotive market with our comprehensive Car Prices Dataset. Designed for businesses, analysts, and researchers, this dataset provides real-time and historical car pricing data to support market analysis, pricing strategies, and trend forecasting.

Dataset Features

Vehicle Listings: Access detailed car listings, including make, model, year, trim, and specifications. Ideal for tracking market trends and pricing fluctuations. Pricing Data: Get real-time and historical car prices from multiple sources, including dealerships, marketplaces, and private sellers. Market Trends & Valuations: Analyze price changes over time, compare vehicle depreciation rates, and identify emerging pricing trends. Dealer & Seller Information: Extract seller details, including dealership names, locations, and contact information for lead generation and competitive analysis.

Customizable Subsets for Specific Needs Our Car Prices Dataset is fully customizable, allowing you to filter data based on vehicle type, location, price range, and other key attributes. Whether you need a broad dataset for market research or a focused subset for competitive analysis, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Pricing Strategy: Track vehicle price trends, compare competitor pricing, and optimize pricing strategies for dealerships and resellers. Automotive Valuation & Depreciation Studies: Analyze historical pricing data to assess vehicle depreciation rates and predict future values. Competitive Intelligence: Monitor competitor pricing, dealership inventory, and promotional offers to stay ahead in the market. Lead Generation & Sales Optimization: Identify potential buyers and sellers, track demand for specific vehicle models, and enhance sales strategies. AI & Predictive Analytics: Leverage structured car pricing data for AI-driven forecasting, automated pricing models, and trend prediction.

Whether you're tracking car prices, analyzing market trends, or optimizing sales strategies, our Car Prices Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

How much do fruits and vegetables cost? ERS estimated average prices for 153 commonly consumed fresh and processed fruits and vegetables.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q1 2025 about sales, housing, and USA.

Eggs US rose to 2.76 USD/Dozen on July 14, 2025, up 2.53% from the previous day. Over the past month, Eggs US's price has risen 3.05%, and is up 18.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Eggs US.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

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Revolutionize Your Pricing Strategy with Our Data. Discover hidden insights into historical pricing trends, market fluctuations, and seasonal patterns. Our unmatched Car Price Data empowers you to predict future pricing, eliminate guesswork, and make data-driven decisions. Benchmark against industry leaders to optimize profit margins and gain a competitive advantage.

Covering: France, UK, Italy, Poland, Netherlands, Spain, Belgium, Germany, Austria, Czechia, Portugal, Romania, Switzerland, Bulgaria, Croatia, Denmark, Hungary, Norway, Slovenia, Sweden, and Ireland.

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This dataset was generated for analyzing the economic impacts of subway networks on housing prices in metropolitan areas. The provision of transit networks and accompanying improvement in accessibility induce various impacts and we focused on the economic impacts realized through housing prices. As a proxy of housing price, we consider the price of condominiums, the dominant housing type in South Korea. Although our focus is transit accessibility and housing prices, the presented dataset is applicable to other studies. In particular, it provides a wide range of variables closely related to housing price, including housing properties, local amenities, local demographic characteristics, and control variables for the seasonality. Many of these variables were scientifically generated by our research team. Various distance variables were constructed in a geographic information system environment based on public data and they are useful not only for exploring environmental impacts on housing prices, but also for other statistical analyses in regard to real estate and social science research. The four metropolitan areas covered by the data—Busan, Daegu, Daejeon, and Gwangju—are independent of the transit systems of Greater Seoul, providing accurate information on the metropolitan structure separate from the capital city.

This research data file contains the necessary software and the dataset for estimating the missing prices of house units. This approach combines several machine learning techniques (linear regression, support vector regression, the k-nearest neighbors and a multi-layer perceptron neural network) with several dimensionality reduction techniques (non-negative factorization, recursive feature elimination and feature selection with a variance threshold). It includes the input dataset formed with the available house prices in two neighborhoods of Teruel city (Spain) in November 13, 2017 from Idealista website. These two neighborhoods are the center of the city and “Ensanche”.

This dataset supports the research of the authors in the improvement of the setup of agent-based simulations about real-estate market. The work about this dataset has been submitted for consideration for publication to a scientific journal.

The open source python code is composed of all the files with the “.py” extension. The main program can be executed from the “main.py” file. The “boxplotErrors.eps” is a chart generated from the execution of the code, and compares the results of the different combinations of machine learning techniques and dimensionality reduction methods.

The dataset is in the “data” folder. The input raw data of the house prices are in the “dataRaw.csv” file. These were shuffled into the “dataShuffled.csv” file. We used cross-validation to obtain the estimations of house prices. The outputted estimations alongside the real values are stored in different files of the “data” folder, in which each filename is composed by the machine learning technique abbreviation and the dimensionality reduction method abbreviation.

This dataset contains Food Prices data for India, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

This dataset provides supply chain health commodity shipment and pricing data. Specifically, the data set identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, the data set provides the commodity pricing and associated supply chain expenses necessary to move the commodities to countries for use. The dataset has similar fields to the Global Fund's Price, Quality and Reporting (PQR) data. PEPFAR and the Global Fund represent the two largest procurers of HIV health commodities. This dataset, when analyzed in conjunction with the PQR data, provides a more complete picture of global spending on specific health commodities. The data are particularly valuable for understanding ranges and trends in pricing as well as volumes delivered by country. The US Government believes this data will help stakeholders make better, data-driven decisions. Care should be taken to consider contextual factors when using the database. Conclusions related to costs associated with moving specific line items or products to specific countries and lead times by product/country will not be accurate.

Analysis of ‘Hotel Prices - Beginner Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sveneschlbeck/hotel-prices-beginner-dataset on 28 January 2022.

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

Context

This dataset addresses Data Science students and/or Beginners who want to dive into Regression or Clustering without the need to pre-clean the data first.

Content

This dataset consists of a pre-cleaned .csv table that has been translated from German to English.

There are four columns in this dataset:

• Profit (How much money does this hotel make in a year)
• Price in Millions (€)
• Square Meter (Hotel Area)
• City

Here, "Hotel Prices" does not refer to the cost of spending a night at those hotels but the price for buying them. This would be an interesting chart for someone who wants to buy a hotel and needs to judge whether he/she is overpaying or getting a great deal depending on similar objects in other comparable cities.

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

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

Median house prices for California districts derived from the 1990 census.

About Dataset

Context This is the dataset used in the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. 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 to 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.

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. Be warned the data aren't cleaned so there are some preprocessing steps required! The columns are as follows, their names are pretty self-explanatory: - longitude - latitude - housing_median_age - total_rooms - total_bedrooms - population - households - median_income - median_house_value - ocean_proximity

Acknowledgements This data was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.

and I encountered it in 'Hands-On Machine learning with Scikit-Learn and TensorFlow' by Aurélien Géron. Aurélien Géron wrote: This dataset is a modified version of the California Housing dataset available from: Luís Torgo's page (University of Porto)

Inspiration See my kernel on machine learning basics in R using this dataset, or venture over to the following link for a python based introductory tutorial: https://github.com/ageron/handson-ml/tree/master/datasets/housing

This dataset contains information about car listings scraped from the Yad2 website in Israel. The data was collected on June 4th, 2024. It includes various attributes of the cars, such as their age, horsepower, fuel type, and more, which can be used for predicting car prices.

Dataset Features:

• brand: The brand of the car.
• model: The model of the car.
• car_name: The name of the car.
• price: The listed price of the car (NIS).
• year: The year the car was manufactured.
• hand_num: The number of previous owners.
• horse_power: The horsepower of the car.
• 4x4: Indicates whether the car is a 4x4 vehicle.
• fuel_type: The type of fuel the car uses (e.g., Petrol, Diesel).
• engine_volume: The volume of the car's engine.
• valid_test: Indicates whether the car has a valid test.
• magnesium_wheels: Indicates whether the car has magnesium wheels.
• distance_control: Indicates whether the car has distance control.
• economical: Indicates whether the car is economical.
• adaptive_cruise_control: Indicates whether the car has adaptive cruise control.
• cruise_control: Indicates whether the car has cruise control.
• brand_normalized: Normalized brand name.
• brand_group: Grouping of brands.
• log_price: The logarithm of the car's price.
• cluster: Cluster label assigned based on K-Means clustering of price and horsepower.
• cluster_label: Descriptive label for the cluster.
• car_age: The age of the car.
• electric_or_not: Indicates whether the car is electric.

Source: The data was scraped from the Yad2 website, a popular online marketplace in Israel.

Usage: This dataset can be used for various purposes, including:

• Predicting car prices based on their attributes.
• Analyzing trends in car prices and features.
• Clustering cars based on their characteristics.

Acknowledgements: Please acknowledge the Yad2 website as the source of the data if you use this dataset in your work.

This statistic shows the average price of cellular data per gigabyte in the United States from 2018 to 2023. In 2018, the average price of cellular data was estimated to amount to 4.64 U.S. dollars per GB.

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), 2016The 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 website: https://www.eia.gov/petroleum/gasdiesel/xls/pswrgvwall.xls