Building a Data Driven Product with Machine Learning

Kadriye Doğan, Yalçın Yenigün

26.01.2017

Agenda

Agenda1. What is Data-Driven Product?

a) Introductionb) Examples

2. Machine Learninga) Term Definitionsb) A Visual Examplec) Supervised Learningd) Unsupervised Learninge) Cross Validationf) Feature Extraction

3. Case Study

What isData Driven Product?

Data Driven Product• Data driven is the future!!!• It’s the ‘right’ way of doing things!!!..etc.

• What is “data-driven” ??• Is Facebook a data-driven product??• Is Uber a data-driven product??

• We can say that “all” of these are data-driven products• All of them works with data.• But they are really data-driven products??

Data Driven Product• Experimentation:

• Data-Driven: Making design decisions based on behavioral evidence from users.

• Example: Picking a green button for your website because conversion metrics are significantly improved over the purple button

Data Driven Product• Machine Learning : Building systems that

learn from behavioral data generated by users

• Examples:• Recommendation• Personalized Ranking• People-you-may-know• Products-you-may-like

Data Driven Product• Databases or APIs

• They just use the data• To them their system is also data-driven.• But they are NOT data-driven.• They don’t use behavioral data generated

by users.

Examples• A mobile app that gives information about public transport around you.

• Pulls data from transport operator or APIs, merges and gives you.• Nothing really data-driven.

• Data-driven version of this app:• Learn what part of the transport network relevant to you.• Predict when cycling is better when walking is better.• Predict waiting times.• Predict delays of transports.

Examples• A website that provides blogging services to users

• Write posts, subscribe other posts.. etc.

• Data-driven version of this blog:• Recommend who to follow based on your previous likes• Auto-tag your content to allow people quickly find it• Create relevance-sorted feed of posts.

Machine Learning

Term Definitions• Machine Learning: “Field of study that gives computers

the ability to learn without being explicitly programmed” Arthur Samuel

• Arthur Samuel: A pioneer in the field of computer gaming and artificial intelligence. He coined the term "machine learning" in 1959.

• Feature: In machine learning and pattern recognition, a feature is individual measurable property of a phenomenon being observed.

Term Definitions• Data Sampling: Data sampling is

a statistical analysis technique used to select, manipulate and analyze a representative subset of data points in order to identify patterns in the larger data set being examined.

Term Definitions• Training Set: A training set is a set of data used to discover

potentially predictive relationships.

• ML Model: You can use the ML model to get predictions on new data for which you do not know the target.

• Cross Validation: A model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set.

Term Definitions

Confusion Matrix

Confusion Matrix• Accuracy: Ratio of correctly predicted observations.

(TP + TN) / (TP + TN + FP + FN)

• Precision: Ratio of correct positive observations. TP / (TP + FP)

• Recall: Ratio of correctly predicted positive events.

TP / (TP + FN)

Visual Example

Visual Example

Supervised Learning

Supervised Learning• Input data is called training data and has

a known label or result such as spam/not-spam or a stock price at a time.

• Example problems are classification and regression.

• Example algorithms include Logistic Regression and the Back Propagation Neural Network.

Supervised Learning Example

Supervised Learning Example

Supervised Learning• Supervised Learning: Right

answers given

• Regression: Predict continuous valued output

• Classification: Discrete valued output

Supervised Learning – Classification Example

Supervised Learning – Classification Example

Supervised Learning – Classification Example

http://localhost:8888/notebooks/dev/workspaces/iyzico/scipy_2015_sklearn_tutorial/notebooks/02.1%20Supervised%20Learning%20-%20Classification.ipynb

Linear Regression with One Variable

Linear Regression with One Variable

Cost Function

Cost Function

Cost Function

Supervised Learning – Regression Example

http://localhost:8888/notebooks/dev/workspaces/iyzico/scipy_2015_sklearn_tutorial/notebooks/02.2%20Supervised%20Learning%20-%20Regression.ipynb

Unsupervised Learning

Unsupervised Learning• Input data is not labeled and does not

have a known result.

• Example problems are clustering, dimensionality reduction and association rule learning.

• Example algorithms include: the Apriori algorithm and k-Means.

Unsupervised Learning Examples

Unsupervised Learning – Transformation Example

http://localhost:8888/notebooks/dev/workspaces/iyzico/scipy_2015_sklearn_tutorial/notebooks/02.3%20Unsupervised%20Learning%20-%20Transformations%20and%20Dimensionality%20Reduction.ipynb

Unsupervised Learning – Clustering Example

http://localhost:8888/notebooks/dev/workspaces/iyzico/scipy_2015_sklearn_tutorial/notebooks/02.4%20Unsupervised%20Learning%20-%20Clustering.ipynb

Cross Validation

Cross Validation• A model validation technique

for assessing how the results of a statistical analysis will generalize to an independent data set.

Cross Validation Example

http://localhost:8888/notebooks/dev/workspaces/iyzico/scipy_2015_sklearn_tutorial/notebooks/04.1%20Cross%20Validation.ipynb

Feature Extraction

Feature Extraction• Feature extraction starts from an initial set of measured data and builds

derived values (features) intended to be informative and non-redundant.

• Feature extraction involves reducing the amount of resources required to describe a large set of data.

Text Feature Extraction Example

http://localhost:8888/notebooks/dev/workspaces/iyzico/scipy_2015_sklearn_tutorial/notebooks/03.4%20Methods%20-%20Text%20Feature%20Extraction.ipynb

Case Study

Handwriting Digits

http://localhost:8888/notebooks/dev/workspaces/iyzico/scipy_2015_sklearn_tutorial/notebooks/03.1%20Case%20Study%20-%20Supervised%20Classification%20of%20Handwritten%20Digits.ipynb

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