Mapeai is a integrated platform that allows everyone to explore, clean, and transform data to disclose its story. Manage and prepare data to Machine Learning deployment.
Once you upload your dataset, you're ready to transform it into useful information. Mapeai is a powerful tool capable of cleaning, normalizing, and revealing real data contextualization.
Mapeai spots null values and outliers right away. No need of sifting into your dataset, sorting or filtering columns.
The optimization feature tells you what you can do with your data, like how to transform a text column into an int16 or a datetime column just removing some misplaced or invalid values.
Mapeai will then carry out all the transformations for you. The information gets already contextualized for you within an easy UI and with minimal effort.
Data is key when working with Data Science and Machine Learning.
Visualizing your data properly enables you to quickly identify its structure, contents and issues, which surely would demand a lot of investigation time of your data analysts team.
Mapeai highlights null values and outliers so that the investigation and cleaning process become much more seamless.
Having the perfect data for using in analytics is not easy. To make sure that all your data is uniform and ready to be used in charts, dashboards, or in Machine Learning models is not always straightforward and requires a lot of manipulation.
Data Transformation makes it easy with our automatically data recognition.
Reorder, sort, exclude, filter, rename columns and then replace null values and outliers by suggestions like mean, median, mode, or just remove the records completely.
Check the detailed descriptive statistics of your data and know exactly how your dataset was built and what to expect from it.
Machine Learning models require data to be as neat as possible, with all columns translated into numeric values.
Mapeai will evaluate how cast your data into numbers, considering all the adjustments needed.
Identify the correct decimal point in numbers and normalize them. Likewise, remove leading and trailing characters with no special meaning, like spaces and letters, next to numbers.
Your data will be given a score, along with a detailed description of all problems found. Just compare to the score of the optimized data, side by side.