Analance Advanced Analytics 2018-04-23T02:36:47+00:00
AAA - Analance Advanced Analytics

Analance Advanced Analytics

Data Science and Machine Learning Made Easy

With the redesigned Analance Advanced Analytics (AAA) module, empower your users to execute data science projects using sophisticated techniques and tools with ease. Successfully deliver insights and predictions in minutes, not hours. Deploy fast, scale on demand and experience a lower total cost of ownership by leveraging the expertise and skills of our internal team whenever you need them.

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You don’t have to be a Data Scientist

Zero Coding. Point and Click. Pre-built Algorithms.

AAA takes all users through an end-to-end workflow at each stage to bring the focus onto addressing real, prioritized issues and business challenges. Whether you have a business user, business analyst or a data scientist on the team, the platform is easy to master. There is no need for coding. Simply point, click and run out-of-the-box algorithms seamlessly and start visualizing the insights.

Analyze Data like a Pro

Step-by-step workflow to make AAA truly self-serve.

Add a model and generate your output for very large data sets in minutes, not hours.

Pick and choose out-of-the-box Machine Learning algorithms from prediction, classification, clustering, survival analysis, forecasting, text classification, text clustering and association rules.

Import and prepare your data. Select the type of imputations, outliers and normalization (algorithm specific) to cleanse and normalize data.

Feature engineering. Use the built-in feature reduction method to identify variables from the data that has a relationship to only consider those for analysis using tree based, lasso regression and principle component analysis.

Sample and split data. Run model on the entire data set or select the first n records, last n record, random or stratified for sampling and set your train ratio.

Pick algorithms and enter input parameters. Select one or more ML algorithms available for the chosen analysis before running the model. Fine tune the model on accuracy, sensitivity and specificity to improve confidence on data output from every model.

View the model results. View the results based on the algorithm chosen and executed. Every output sheet will give you insights into your data you have never seen before. Based on your data score, take the trained model to a new data and make predictions about unseen behavior to identify unknown opportunities. When given new data sets, the model automatically adapts, eliminating the need for human intervention.

  1. Pick and choose out-of-the-box ML models, including prediction, classification, clustering, survival analysis, forecasting, text classification, text clustering and association rules.
  2. Import your data to prepare data. Select the imputation, lower outlier, upper outlier and normalization (algorithm specific) to reduce the spectrum and optimize the data.
  3. Feature engineering. Use the built-in feature reduction method to identify variables from the data that have a relationship. Isolate variables for analysis using tree based, lasso regression and principle component analysis.
  4. Sample and split data. Run models on the entire data set or select the first n records, last n record, random or stratified for sampling. Set your train ratio.
  5. Pick algorithms and enter input parameters. Select one ML algorithm or all available algorithms before running the model. Fine tune the model on accuracy, sensitivity and specificity to improve confidence on data output from every model. You can even import custom algorithms.
  6. View the model results. Get up to 39 output sheets based on the algorithm chosen. Every output sheet gives new insights into your data. Based on data score, take the trained model to new data and make predictions about unseen behavior to identify previously unknown opportunities. When given new data sets, the model automatically adapts, eliminating the need for human intervention.

Integration with R and Python

Leverage best-of-breed languages, tools and libraries

AAA integrates with best-of-breed statistical analytics languages like R and Python with a suite of pre-built, zero-coding machine learning algorithms to jump start analysis. The process is completely seamless. Additional machine learning algorithms can be added to target specific business challenges.

Why Analance Advanced Analytics?

More Advanced Analytics

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