- Agile Data Science Tutorial
- Agile Data Science - Home
- Agile Data Science - Introduction
- Methodology Concepts
- Agile Data Science - Process
- Agile Tools & Installation
- Data Processing in Agile
- SQL versus NoSQL
- NoSQL & Dataflow programming
- Collecting & Displaying Records
- Data Visualization
- Data Enrichment
- Working with Reports
- Role of Predictions
- Extracting features with PySpark
- Building a Regression Model
- Deploying a predictive system
- Agile Data Science - SparkML
- Fixing Prediction Problem
- Improving Prediction Performance
- Creating better scene with agile & data science
- Implementation of Agile
- Agile Data Science Useful Resources
- Agile Data Science - Quick Guide
- Agile Data Science - Resources
- Agile Data Science - Discussion
Agile Data Science - SparkML
Machine learning library also called the “SparkML” or “MLLib” consists of common learning algorithms, including classification, regression, clustering and collaborative filtering.
Why learn SparkML for Agile?
Spark is becoming the de-facto platform for building machine learning algorithms and applications. The developers work on Spark for implementing machine algorithms in a scalable and concise manner in the Spark framework. We will learn the concepts of Machine learning, its utilities and algorithms with this framework. Agile always opts for a framework, which delivers short and quick results.
ML Algorithms include common learning algorithms such as classification, regression, clustering and collaborative filtering.
It includes feature extraction, transformation, dimension reduction and selection.
Pipelines provide tools for constructing, evaluating and tuning machine-learning pipelines.
Following are a few popular algorithms −
A recommendation system is a subclass of information filtering system that seeks prediction of “rating” and “preference” that a user suggests to a given item.
Recommendation system includes various filtering systems, which are used as follows −
It includes building a model based on the past behavior as well as similar decisions made by other users. This specific filtering model is used to predict items that a user is interested to take in.
Content based Filtering
It includes the filtering of discrete characteristics of an item in order to recommend and add new items with similar properties.
In our subsequent chapters, we will focus on the use of recommendation system for solving a specific problem and improving the prediction performance from the agile methodology point of view.
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