predictive analytics-nirmal.potx
TRANSCRIPT
Predictive Analytics: From Data to Foresight
Summer School 2015
Nirmal Fernando
Associate Technical Lead, WSO2 Inc.
Predictive Analytics
Extract information from existing data sets to determine patterns and predict future outcomes and trends.
It does not tell you what will happen in the future.
It forecasts what might happen in the future with an acceptable level of reliability.
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source: http://insidebigdata.com/2014/08/25/salespredict-marketo-partner-using-predictive-analytics/
Where is it used?
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Ticket to success
o Guiding front-line decisions and
actions via transmuting data into
predictive visions and intuitions.
o Customer requirements and steps to
increase profitability and retention.
o Boosting productivity of people, assets and processes
o Eliminating threats and frauds before they can hamper the
image and reputation of the company.
o Assessing the social media impact of your products in the
market.
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o Matured
o Less expensive
o More approachable
o Easy to make use of
Unlocking the potential of “Big Data”
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Machine Learning
Machine learning is the science of getting computers to act without being explicitly
programmed.
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Categorization
o Supervised Learning
o Unsupervised Learning
o Reinforcement Learning
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Categorization
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o Supervised Learning
o Unsupervised Learning
o Reinforcement Learning
Supervised Learning
Machine learning task of inferring a function from labeled training data.
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source: http://www.astroml.org/sklearn_tutorial/general_concepts.html
Supervised Learning - Classification
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Supervised Learning - Regression
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Supervised Learning - Algorithms
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Regression
o Linear Regression
o Lasso Regression
o Ridge Regression
Classification
o Logistic Regression
o Support Vector Machine
o Decision Tree
o Random Forest
o Naive Bayes
Unsupervised Learning
Machine learning task of inferring a function from unlabeled training data. The algorithm tries to find similarities among the objects in question.
13source: http://www.astroml.org/sklearn_tutorial/general_concepts.html
Unsupervised Learning - Clustering
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o Manage and explore your data o Analyze the data using machine learning algorithmso Build machine learning modelso Compare and manage generated machine learning modelso predict using the built models
Powered by Apache Spark and Apache Spark MLlib.
Key words,
o ML Project: a logical grouping of set of machine learning analyses you would perform on a selected dataset.
o ML Analysis: holds a pre-processed feature set, a selected machine learning algorithm and its calibrated set of hyper-parameters.
WSO2 Machine Learner
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Manage your datasets...
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Explore your data..
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Manage ML projects...
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Generate and manage ML models
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Finding the best model
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Other features...
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o Fast and scalable machine learning
o Every operation exposed using a REST API
o Easy to use User Interface
o Use generated models in WSO2 ESB and WSO2 CEP for
prediction
Future,
o Deep learning algorithms
o NLP techniques
o Data pre-processing techniques
Let’s try to solve a real world problem
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Problem description
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source: https://www.kaggle.com/c/otto-group-product-classification-challenge
Dataset
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o 93 features
o for 200,000+ products
o id - an anonymous id unique to a product
o feat_1, feat_2, ..., feat_93 - the various features of a product
o target - the class of a product. There are 9 most important
product categories (like fashion, electronics, etc.)
Objective
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Build a predictive model
which is capable of
categorizing a given
product into
one of the categories.
Towards the solution..
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Summary
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o Discussed predictive analytics
o Learnt what machine learning is
o Got to know widely-used machine learning techniques
o Glanced at WSO2 Machine Learner product features
o Solved a real-world machine-learning problem using WSO2
Machine Learner
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