DATA ANALYTICS
Analyzing raw data in order to unearth actionable insights about that data.
DATA UPLOAD:
FILE/DATABASE
Users can upload various types of data files including CSV, Tab Separated (TSV), Semicolon, Space, JSON, Parquet, Avro, S3 and HDFS files.
Additionally, tables can be loaded into SEDGE from databases such as MySQL, MariaDB, Oracle, Postgres, SQL Server, Cassandra, SQLite, Presto, Redshift, Redis.
SQL query builder can be used to connect multiple tables and to normalize data that is then loaded into SEDGE.
DATA
PROFILING
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Information on detailed statistics
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Different sampling algorithms can be used to sample a large dataset.
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Visualize the Target variable (a field that is of interest to the user), which may be a categorical, numerical, data, or boolean type
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The profiling page provides information about the dataset, such as the numbers of numerical variables, categorical variables, dates and text variables as well as the amount of missing data %. The page also indicates some warnings about the quality of the data such as variables with more than 90% missing values and variables without any variance.
STATISTICS, FEATURE CREATION & FEATURE IMPORTANCE
The variables from the data that are important and have the potential to influence the target variable are automatically sorted based on importance value.
Large numbers of capabilities are then available to the users in order to achieve the desired level of data transformation and data cleansing. Functions such as changing data types, strings, numerical , mathematical and date operations are also available.
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Whilst in the Statistics section (page) users can view the description and statistics associated with each data type such as:
Mean, Median, Missing Row Count, Missing Row %, Distinct Counts, Minimum, Maximum, 1st Quartile, 3rd Quartile, Inner Fence, Outer Fence, Kurtosis, Skewness, Outlier Data
DECISION TREE ANALYSIS
Decision tree analysis provides a tree-shaped diagram that illustrates the statistical probability of an outcome, which helps the user to understand the decision-making process. Each branch represents a possible outcome.
Since the target variable is predicted by inferring simple decision rules from the prior data, the method is very easy to comprehend. The significant advantage of this method is that the user can consider all possible outcomes of the decision and decide which is best for the business problem and conclude.
AUTO ML
SEDGE provides Automated Machine Learning capabilities to help users without doing endless inquiries on data preparation, feature selection, hyperparameters tuning, model comparison, and model selection. The auto-ML algorithm takes all the above factors into account and develops the best model for accurate predictions
CONCLUSION
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Machine Learning is playing a leading role in generating insights and in this role SEDGE unearths the facts from algorithms for a meaningful execution of various decisions and goals predetermined by an Enterprise.
SEDGE is redefining and revolutionizing the world of software and analytics, and brings the power of the future into today.