Machine LearningData WranglingVisualizationData Sources
Point and Click
Exploratory’s Analytics View makes it easier to use a wide range of the most advanced open source AI / Machine Learning Algorithms to discover hidden patterns and trends in your data effectively.
Regression Analysis
Use Regression Analysis to understand how the other attributes or variables are influencing the outcome of your interest.
Logistic Regression
It’s a variation of Regression Analysis. Use Logistic Regression when the outcome of your interest has two categories like TRUE/FALSE, Yes/No, etc. and you want to know how the other attributes or variables are influencing it.
Variable Importance
Use one of the most popular Machine Learning algorithms among data scientists - Random Forest - to understand which variables are more influential on the outcome of your interest and how. The outcome can be numeric, binary, or multiple categories (classes).
Survival Analysis
If you want to do Cohort Analysis right, use Survival Analysis, which employs a robust statistical algorithms 'Kaplan-Meier'. You can quickly find which cohorts have the problems in retaining customers, keeping employees, or keeping product qualities, for example, and understand the life time values of your customers.
Survival Impact Analysis
Use Survival Impact Analysis (Cox Regression) to understand what makes your customer churn or become loyal customers, what contributes product failures and when.
A/B Testing
Use Bayesian A/B Testing to evaluate and understand the result of your A/B Testing in a way you can explain to others with confidence.
Time Series Forecasting
With a cutting edge forecasting algorithm called Prophet built by a team at Facebook, you can quickly forecast the future for your time sensitive data even without a proper knowledge or training in time series forecasting.
Anomaly Detection
Use Anomaly Detection to not only detect unusual activities like financial fraud, suspicious web site access, machine faults, etc., but also find what’s trending on your web sites, referrals, customer activities, etc.
Use PCA (Principal Component Analysis) to understand the relationships among the variables and detect hidden characteristics about them.
Use Correlation Analysis to understand which variables are correlated or not. Insight from this analysis help you build better prediction models.
Similarity / Clustering
Use Similarity Analysis to understand the similarities between categories and discover hidden characteristics of your customers, employees, products, etc.
Build, Predict, and Evaluate
Exploratory’s Machine Learning framework provides a simple and consistent experience for building, predicting, and evaluating the models from hundreds of the machine learning algorithms available in R.
Ensemble - Boosting / Bagging
Ensemble models such as Boosted Trees, Random Forest, etc. helps not only improve the prediction performance but also helps you find which attributes of the data have more impacts on the outcome you want to predict. You can quickly access to these state of art algorithms Data Scientists love without coding in Exploratory.
Open and Extensible
With Exploratory’s open machine learning framework you can bring your own favorite algorithms and run them natively, which means you can build, predict, and evaluate the models with point and clicks, just like any other out-of-the-box machine learning models.