SEDGE Documentation
Welcome to the complete beginners guide to SEDGE! This document will guide you on a step by step basis to the complete documentation of SEDGE.
Table of Contents:
- Introduction
- Concepts
- Functionality
- Application access
- Basics
- Data analytics
- Data sources
- DB Schemas and Access
- ETL Process
- File upload
- File collaboration
- Data preview
- Profile Dataset
- Statistics
- Statistical Test
- Transformations
- Absolute
- Accrint
- Arc cosine - Acos
- Add months
- Append
- Ascii
- Arcsin
- Atan - Arc tangent
- Atan2
- Base64
- Bigint
- Bin
- Bit-length
- Bround
- Char
- Char_length
- Coalesce
- Concat
- Concat_ws
- Conv
- Cos
- Cosh
- Cot
- Crc32
- Cube Root
- Ceiling
- Copy
- Current_date
- Datediff
- Datediff_hrs
- Datediff_mins
- Datediff_secs
- Date_add
- Date_value
- Current_timestamp
- Date_isin
- Date_sub
- Date_trunc
- Day
- Dayofmonth
- Dayofweek
- Dayofyear
- Degrees
- Delete
- Double
- Effect
- Exponential
- Expm1
- Factorial
- Find
- Float
- Floor
- Format_number
- From unixtime
- From-utc-timestamp
- FV
- Geo Dist Kms
- Greatest
- Hexadecimal
- Holiday_count
- Hour
- Hypot
- If Else And
- Ifnull
- Initcap
- Int
- Insert
- Instr
- Inverse sign
- Is_holiday
- Is_Text
- Is_weekend
- Isnan
- Isnull
- Is not null
- Is Numeric
- Last_day
- Least
- Left
- Length
- Levenshtein
- Ln
- Locate
- Log10
- Log1p
- Log2
- Lower
- Lpad
- Ltrim
- Logarithm
- Max
- Md5
- Mean
- Median
- Milisec_todate
- Min
- Minute
- Mod
- Month
- Months_between
- Multiple Replacement
- Negate_bool
- Negative
- Next_day
- Normalize
- Now
- NPER
- Nullif
- Nvl2
- Octet_length
- Offday_count
- One Replacement
- Pmod
- PMT
- Positive
- Power
- Proper
- Quarter
- Radians
- Rand
- Randn
- Reciprocal
- Regexp_replace
- Regexp_extract
- Remove
- Repeat
- Replace
- Reverse
- Right
- Rint
- Round
- Row_index
- Rtrim
- Rpad
- Running total
- Second
- Sha1
- Sha2
- Shiftleft
- Shiftright
- Shiftrightunsigned
- Sign
- Sin
- Sinh
- Soundex
- Square Root
- Step Delete
- Standardize
- Stddev
- String
- Substring
- Substring_index
- Sum
- Tan
- Tanh
- To_date
- To_timestamp
- To_unix_timestamp
- To_utc_timestamp
- Trim
- Typeof
- Unbase64
- Unhex
- Unix_timestamp
- Uuid
- Unit converter
- Temperature units
- Quantity units
- Volume units
- Distance units
- Length units
- Upper
- Variance
- Weekday
- Weekend_count
- Weekofyear
- Workday_count
- XXhash64
- Year
- Year_to_date
- Visual Analytics
- Cluster Analysis
- Graphical Analysis
- Advanced Analysis
- Predictive Analytics
- Algorithms
- Linear regression
- Logistic regression
- Decision Tree
- Random Forest
- Gradient Boosting Machines
- eXtreme Gradient Boosting
- Naive Bayes
- K-Nearest Neighbors
- Support Vector Machine
- Multilayer Perceptron
- Light GBM
- Variable of Importance
- Metrics - Classification report
- Metrics- Confusion Matrix
- Metrics- Gain Charts
- Metrics- Lift Chart
- Metrics- K-S Chart
- AUC- Area under the ROC Curve
- Model comparison
- Actual vs predicted
- Shapely importance
- Dash Pro
- Tab Renaming and Addition
- Widget Types and Graphs
- Table
- Pivot
- Bar
- Stacked Bar
- Grouped Bar
- Column
- Stacked Column
- Grouped column
- Pie
- Donut
- Semi Circle
- Sun burst
- Water Fall
- Network
- Line
- Area
- Gantt
- Scatter
- Dumbbell Plot
- Tree Map
- Heat Map Legend
- Grouped and Sorted
- Multiple Axis
- Radar Line
- Zoomable Bubble
- Time Line
- Population Pyramid
- Box Plot
- Text
- Bar with Line/Scatter
- Layered Column
- Bullet
- Circos
- Candle stick
- Map with Bubble
- Word Cloud
- Summary
- Card
- KPI Card
- Custom Pivot
- Sparkline
- Filter(s) Functionality
- Icon Definitions
- My Charts
- Published Dashboard
- Color continuation in visualisation
- Time series
- Optical Character Recognition
- Third Party acknowledgements
- Release notes
- SEDGE 8.0 Release notes
- Version 8.5.0 - 3 Jan 2025
- Version 8.4.2 (Hotfix) - 13 Dec 2024
- Version 8.4.2 - 10 Dec 2024
- Version 8.4.1 - 12 Nov 2024
- Version 8.4.0 - 12 Nov 2024
- Version 8.3.0 - 26 Sep 2024
- Version 8.2.0 - 27 Aug 2024
- Version 8.1.0 - 26 July 2024
- Version 8.0.2 - 18 July 2024
- Version 8.0.1 - 10 July 2024
- Version 8.0.0 - 19 Jun 2024
- SEDGE 7.0 Release notes
- Version 7.8.1 - 03 Apr 2024
- Version 7.8.0 - 13 Mar 2024
- Version 7.7.1 - 25 Jan 2024
- Version 7.7.0 - 14 Dec 2023
- Version 7.6.2 - 18 Aug 2023
- Version 7.6.1 - 25 July 2023
- Version 7.6.0 - 14 July 2023
- Version 7.5.1 - 20 June 2023
- Version 7.5.0 - 09 June 2023
- Version 7.4.0 - 21 Apr 2023
- Version 7.3.0 - 31 Mar 2023
- Version 7.2.0 - 07 Mar 2023
- Version 7.1.1 - 03 Feb 2023
- Version 7.1.0 - 16 Jan 2023
- Version 7.0.1 - 08 Dec 2022
- Version 7.0.0 - 30 Nov 2022
- SEDGE 6.0 Release notes
- SEDGE 5.0 Release notes
- SEDGE 4.0 Release notes
- SEDGE 3.0 Release notes
- SEDGE 2.0 Release notes
- SEDGE 8.0 Release notes
- Tables and charts
Introduction
![SEDGE Logo](_images/SEDGE.png)
SEDGE Logo
SEDGE is a machine learning, deep learning, and Artificial Intelligence (AI) online collaborative data science platform, created to explore data of your business, delivering simple, smart, and rapid decision making for complex analytics. SEDGE is an ideal tool for solving real-world business problems in a simple and intuitive way. With SEDGE, you will be able to run your predictions with great accuracy, make experienced decisions based on the forecasted results, and that add value to your business. Be ahead of your competitors and let the power of artificial intelligence help you with taking more accurate and science-based decisions.
Challenges
Users on a daily basis work with varied datasets, such as large relational data, or unstructured data involving text documents, or vision-related data, and build models for these data. The data sizes may run into Gigabytes, involving large dimensions and can involve a blend of text and vision data. SEDGE can help in simplifying the complexity around data analysis and model building and provide insights in a simple and intuitive manner.
The Process
Prediction of future trends or results is a key feature of the software, you can upload your data from CSV (comma separated values) files or from a database, afterward, you will preview the data, make a manual clean-up of the data in order to fit into the model which would be required for the purpose of your prediction. In the next step you can do the statistics, also erase some columns or other features that are not relevant for your prediction, and in the last stage visualize the data in various graphs and charts