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Advertising Sales Prediction Data
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About
This dataset captures sales revenue generated in relation to advertisement costs across multiple channels, specifically radio, television (TV), and newspapers. Its primary purpose is to facilitate an understanding of the impact of advertising budgets on overall sales. It is ideal for building and evaluating regression models to predict sales.
Columns
- ID: An identifier column, ranging from 1 to 200. It has 200 valid entries with a mean of 101 and a standard deviation of 57.7.
- TV Ad Budget (£): Represents the budget allocated for TV advertisements, expressed in thousands of pounds. Values range from 0.70 to 296.40, with a mean of 147 and a standard deviation of 85.6 for its 200 valid entries.
- Radio Ad Budget (£): Represents the budget allocated for Radio advertisements, expressed in thousands of pounds. Values range from 0.00 to 49.60, with a mean of 23.3 and a standard deviation of 14.8 for its 200 valid entries.
- Newspaper Ad Budget (£): Represents the budget allocated for Newspaper advertisements, expressed in thousands of pounds. Values range from 0.30 to 114.00, with a mean of 30.6 and a standard deviation of 21.7 for its 200 valid entries.
- Sales (£): Represents the sales revenue, expressed in millions of pounds. Values range from 1.60 to 27.00, with a mean of 14 and a standard deviation of 5.2 for its 200 valid entries.
All listed columns have 200 valid entries, with no mismatched or missing data points.
Distribution
The dataset is typically provided in a CSV format and has a file size of 4.8 kB. It contains 200 records, and the data is clean with no missing or mismatched entries across any of the columns.
Usage
This dataset is well-suited for several applications, including:
- Building Linear Regression models to predict sales based on advertising spend across various channels.
- Developing single-feature or multi-feature regression models to understand individual and combined advertising impacts.
- Evaluating model performance using metrics such as R2 score and Root Mean Squared Error (RMSE).
- Analysing the correlation between advertising expenditure on different media and sales revenue.
Coverage
The provided source material does not specify the geographic scope, time range, or demographic scope of the data.
License
CC0: Public Domain
Who Can Use It
This dataset is valuable for:
- Data scientists and analysts performing regression analysis and predictive modelling.
- Marketing professionals seeking to optimise their advertising budgets and understand channel effectiveness.
- Students and researchers engaged in studies of sales forecasting and advertising impact.
- Businesses aiming to make data-driven decisions regarding marketing investments.
Dataset Name Suggestions
- Advertising Sales Prediction Data
- Marketing Spend and Sales Revenue
- Multi-channel Advertising Effectiveness
- Sales Forecasting Dataset
- Ad Budget Impact Analysis
Attributes
Original Data Source:Advertising Sales Prediction Data
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