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Overview
Our service enhances your dataset's quality using descriptive statistics to gain insights and ensure robustness. We use measures like mean, median, standard deviation, etc., to identify central tendencies and variability. We perform plausibility filtering to check for logical consistency and outlier filtering to remove data points that could skew predictions. This careful data preparation refines your dataset, boosting the accuracy and reliability of your model training. Tailored to your needs, these methods optimise your dataset for better model performance.
More about the service
Discover more about our service, including how it can benefit you, the delivery process, and the options for customisation tailored to your specific needs!
This service helps by refining and enhancing customer datasets through advanced data analysis techniques. Before the service, customers may have datasets with inconsistencies, outliers, or limited insights into variability and trends. After the service, the datasets are cleansed, consistent, and enriched with descriptive statistics such as central tendencies and variability measures. These improvements make the datasets more robust and reliable, ensuring better model training and analytics outcomes.
The service is delivered remotely and includes several stages of data analysis, such as descriptive statistics computation, plausibility checks, and outlier filtering. Depending on the dataset size and complexity, the service execution takes a few days to weeks. Customers receive a detailed report that includes an analysis summary, identified data patterns, and recommendations for improving model performance. Customers must provide their datasets and specify their analysis objectives to ensure tailored results.
Customisation options include focusing on specific statistical measures, applying advanced outlier detection techniques, or tailoring the analysis to unique dataset characteristics as well as specific training approaches. The service can also adapt to specific industry or project needs, such as incorporating domain-specific validation rules. Limitations depend on the quality and structure of the provided datasets. Customers should communicate any specific requirements or constraints during the initial planning phase.
Location
Remote
Type of Sector
Arable farming
Horticulture
Livestock farming
Tree Crops
Viticulture
Type of service
Data analysis
Accepted type of products
Data
