Latvia - Households without access to internet at home, because the equipment costs are too high was 24.76% in December of 2019, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Latvia - Households without access to internet at home, because the equipment costs are too high - last updated from the EUROSTAT on November of 2025. Historically, Latvia - Households without access to internet at home, because the equipment costs are too high reached a record high of 59.63% in December of 2011 and a record low of 24.76% in December of 2019.

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.

This dataset contains the predicted prices of the asset WE'RE GONNA WIN SO MUCH 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.

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

Luxembourg - Households without access to internet at home, because the equipment costs are too high was 10.84% in December of 2019, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Luxembourg - Households without access to internet at home, because the equipment costs are too high - last updated from the EUROSTAT on November of 2025. Historically, Luxembourg - Households without access to internet at home, because the equipment costs are too high reached a record high of 11.00% in December of 2015 and a record low of 2.68% in December of 2013.

Procurement prices of natural gas in Spain have been on a mostly stable trend throughout 2024, oscillating around ** euros per megawatt-hours. Prior to this, natural gas procurement prices in the Mediterranean country experienced a great increase between 2021 and 2022. In the latter year, the average natural gas procurement price amounted to roughly ** euros per megawatt-hours, peaking at ***** euros per megawatt-hours in September. By contrast, Spain's average procurement price of natural gas in 2020 was around ***** euros per megawatt-hours. Why are gas prices so high? One main reason behind natural gas prices soaring in the last couple of years is the post-pandemic economic recovery. As coronavirus restrictions were lifted and many industrial and commercial sectors resumed activity simultaneously, there was a sudden demand for energy. This led to a global energy supply shortage, which was further aggravated by Russia’s invasion of Ukraine in February 2022. The natural gas sector in Spain Spain has a negligible production volume of natural gas that has been on a downward trend over the past years. Meanwhile, the import volume into the Mediterranean country has seen a mostly growing tendency. Spain’s main trading partner is Algeria, which accounts for nearly one third of the overall import volume. Altogether, natural gas constitutes an important source of energy in Spain, representing over ** percent of the primary energy consumption, and coming only second to oil.

The median home sales price is the middle value of the prices for which homes are sold (both market and private transactions) within a calendar year. The median value is used as opposed to the average so that both extremely high and extremely low prices do not distort the prices for which homes are sold. This measure does not take into account the assessed value of a property.Source: First American Real Estate Solutions (FARES) and RBIntel (2022-forward)Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2022, 2023

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.

Context

I have previously shared a classification based dataset to classify the gender which is liked by those who are new to machine learning as it give a pretty good accuracy, which encouraged me to create a regression dataset to predict continues values. I have tried many real world datasets for regression problems which are predicting with lower accuracy and high error rate. As a beginner, I have struggled and worried why and how the dataset performs poorly. This is another main reason why I created this dataset. Although this is a made up dataset, I have considered all the features when deciding the price of the property. If you are a beginner, you would love to try this as the results are stunning..

Content

Since this is a populated data, I will straightaway explain the features and the label. FEATURES 1. land_size_sqm - This the total size of the land in square meters. 2. house_size_sqm - This is the area in which house is located within the land. This is measured in square meters. 3. no_of_rooms - This indicates the number of rooms available in the house. 4. no_of_bathrooms - This shows the number of total bathrooms made in the house. 5. large_living_room - This indicates whether the house includes a larger living room or not. The assumption is that all the houses contain a living room. This feature attempts to classify whether it's large or small where '1' means large and '0' means small. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 6. parking_space - This indicates whether there is a parking space or not. '1' represents the parking available while '0' represents no parking space available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 7. front_garden - This shows whether there is a garden available in front of the house. '1' means the garden available and '0' means no garden available. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 8. swimming_pool - This shows the availability of the swimming pool at the house. 1 represents the availability of the swimming pool while 0 represents the non availability of the same. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 9. distance_to_school_km - This shows the distance from the house to the nearest school in Kilometers. 10. wall_fence - This shows whether there is a wall fence or not. '1' mean there is wall fence and '0' means no wall fence. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 11. **house_age_or_renovated **- This is either the age of the house in years or the period from the date of renovation. 12. water_front - this indicates whether the house is located in front of the water or not. 1 means waterfront and 0 means its not located near the water. However in the categorical dataset, 1 and 0 are represented with 'yes' and 'No' respectively. 13. distance_to_supermarket_km - what is the distance to the nearest supermarket in kilometers.

LABEL property_value - This is the price of the property

Following features are only available in the "house price dataset original v2 cleaned" and "house price dataset original v2 with categorical features" data only. 14. crime_rate - its in float and falls between 0 and 7. lesser the better 15. room_size - As the name suggests, it explains the size of the room. 0 is being 'small', 1 is being 'medium', 2 is 'large' and 3 is being 'Extra large'. However in the categorical dataset, these values are categorical and self explanatory.

Acknowledgements

I spent around 3 hours creating this dataset. Enjoy..

Inspiration

Share your notebooks to see which algorithm predicts the house price precisely.

Spain - Households without access to internet at home, because the access costs are too high (telephone, etc.) was 26.15% in December of 2019, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Spain - Households without access to internet at home, because the access costs are too high (telephone, etc.) - last updated from the EUROSTAT on November of 2025. Historically, Spain - Households without access to internet at home, because the access costs are too high (telephone, etc.) reached a record high of 29.90% in December of 2017 and a record low of 19.98% in December of 2008.

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.

Housing prices and number of transactions by dwelling type. House sales not at full market value are excluded. Ownership of this dataset remains with the Communities and Local Government (CLG). Information can only be reproduced if the source is fully acknowledged. The Land Registry (LR) and CLG have provided these datasets drawn from the Land Register. Information on outliers, that is transactions involving a very low or very high price, is included so that users can take their impact into account when using the data. Available for Middle Layer Super Output Area (MSOA). NOTE: This data has not been updated since 2009. See more on the ONS NESS website.

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.

Israel Rental Prices: Avg: Ashdod: 2.5 to 3 rooms data was reported at 3,791.500 ILS in Dec 2024. This records a decrease from the previous number of 3,791.900 ILS for Sep 2024. Israel Rental Prices: Avg: Ashdod: 2.5 to 3 rooms data is updated quarterly, averaging 3,354.600 ILS from Mar 2017 (Median) to Dec 2024, with 32 observations. The data reached an all-time high of 3,791.900 ILS in Sep 2024 and a record low of 2,911.856 ILS in Mar 2017. Israel Rental Prices: Avg: Ashdod: 2.5 to 3 rooms data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.EB009: Average Rental Price: Dwellings.

The average home in the U.S. sold for several percent below its asking price in December 2022, as a result of the housing market slowing. Just a few months before that, In the second quarter of 2022, the so-called sale-to-list price ratio went above ***. This reflected the high housing demand and the need of prospective home buyers to bid above the asking price. Housing demand - as measured in pending home sales - went up, as mortgage rates were historically low and plummeted once rates were increased.

1) Data Introduction • The Twitter Stock Prices Dataset contains stock price data for Twitter from November 2013 to October 2022. This dataset is a time series dataset that provides daily stock trading information. • The key attributes include the stock's opening price (Open), highest price (High), lowest price (Low), closing price (Close), adjusted closing price (Adj Close), and volume (Volume).

2) Data Utilization (1) Characteristics of the Twitter Stock Prices Data • This dataset is a time series, offering daily stock price fluctuations and allows tracking of price changes over time. • It includes 7 main attributes related to stock trading, allowing for analysis of price movements (open, high, low, close) and volume, to better understand Twitter’s stock price dynamics. • This data helps analyze market trends, price volatility patterns, and price fluctuation analysis, providing insights into the dynamics of the stock market.

(2) Applications of the Twitter Stock Prices Data • Predictive Modeling: This dataset can be used to develop stock price prediction models, including predicting price increases/decreases or forecasting future stock prices using machine learning models. • Business Insights: Investment experts can use this dataset to evaluate Twitter’s stock performance, and it provides useful information for optimizing investment strategies in response to market changes. This dataset can be used for trend forecasting and investor analysis. • Trend Analysis: By analyzing stock upward/downward trends, this dataset can help evaluate the company's market performance and develop trend-based investment strategies.

Switzerland Fuel Price: Value: 9001 to 14000 Litre data was reported at 97.040 CHF/100 l in Apr 2025. This records an increase from the previous number of 96.490 CHF/100 l for Mar 2025. Switzerland Fuel Price: Value: 9001 to 14000 Litre data is updated monthly, averaging 79.790 CHF/100 l from Jan 2000 (Median) to Apr 2025, with 304 observations. The data reached an all-time high of 156.150 CHF/100 l in Aug 2022 and a record low of 36.080 CHF/100 l in Feb 2002. Switzerland Fuel Price: Value: 9001 to 14000 Litre data remains active status in CEIC and is reported by Swiss Federal Statistical Office. The data is categorized under Global Database’s Switzerland – Table CH.P002: Fuel Prices. [COVID-19-IMPACT]

Germany DE: Price to Rent Ratio: sa data was reported at 127.280 2015=100 in 2024. This records a decrease from the previous number of 132.141 2015=100 for 2023. Germany DE: Price to Rent Ratio: sa data is updated yearly, averaging 124.182 2015=100 from Dec 1970 (Median) to 2024, with 55 observations. The data reached an all-time high of 159.163 2015=100 in 1981 and a record low of 89.430 2015=100 in 2010. Germany DE: Price to Rent Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.AHPI: House Price Index: Seasonally Adjusted: OECD Member: Annual. Nominal house prices divided by rent price indices