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(A) Data Science in Practice with Python - Sample 2

In this post I'll explain what is a recommender system, how work it and show you some code examples. In my previous post I did a quick introduction:

Sample 2 - Recommender System

WHAT IS A RECOMMENDER SYSTEM? A model that filters information to present users with a curated subset of options they’re likely to find appealing.
HOW DOES IT WORK? Generally via a collaborative approach (considering user’s previous behavior) or content based approach (based on discrete assigned characteristics).

Now I'll get into in some concepts very important about recommender systems.

Recommender System in Details:

We can say that the goal of a recommender system is to make product or service recommendations to people. Of course, these recommendations should be for products or services they’re more likely to want buy or consume.

Recommender systems are active information filtering systems which personalize the information coming to a user based on his interests, relevance of the information etc.…
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(A) Data Science in Practice with Python

The top trending in Twitter or other social network is the term “data science”. But ...
What’s the data science? How do real companies use data science to make products, services and operations better? How does it work? What does the data science lifecycle look like?  This is the buzzword at the moment. A lot of people ask me about it. Are many questions. I’ll try answer all of these questions through of some samples.

Sample 1 - Regression

WHAT IS A REGRESSION? This is the better definition what I found [Source: Wikipedia] - Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning.
HOW DOES IT WORK? Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variable…

(A) Tucson Best Buy Analysis

“Data! Data! Data!” he cried impatiently.  “I can’t make bricks without clay.” —Arthur Conan Doyle
The Ascendance of Data

We live in a world that’s drowning in data. Websites track every user’s every click. Your smartphone is building up a record of your location and speed every second of every day. “Quantified selfers” wear pedometers-on-steroids that are ever recording their heart rates, movement habits, diet, and sleep patterns. Smart cars collect driving habits, smart homes collect living habits, and smart marketers collect purchasing habits. The Internet itself represents a huge graph of knowledge that contains (among other things) an enormous cross-referenced encyclopedia; domain-specific databases about movies, music, sports results, pinball machines, memes, and cocktails; and too many government statistics (some of them nearly true!) from too many governments to wrap your head around.
Buried in these data are answers to countless questions that no one’s ever thought to ask. In…

(T) NumPy in Detail

In my post previous there was some examples contained matrices or other data structures of higher dimensionality—just one-dimensional vectors. To understand how NumPy treats objects with dimensions greater than one, we need to develop at least a superficial understanding for the way NumPy is implemented. It is misleading to think of NumPy as a “matrix package for Python” (although it’s commonly used as such). I find it more helpful to think of NumPy as a wrapper and access layer for underlying C buffers. These buffers are contiguous blocks of C memory, which—by their nature—are one-dimensional data structures. All elements in those data structures must be of the same size, and we can specify almost any native C type (including C structs) as the type of the individual elements. The default type corresponds to a C double and that is what we use in the examples that follow, but keep in mind that other choices are possible. All operations that apply to the data overall are performed in C…

(T) NumPy in Action

The NumPy module provides effecient and convenient handling of large numerical arrays in Python. This module is used by many other libraries and projects and in this sense is a "base" technology. Let's look at some quick examples.
NumPy objects are of type ndarray. There are different ways of creating then. We can create an ndarray by:
Converting a Python listUsing a library function that returns a populated vectorReading data from a file directly into a NumPy object The listing that follows shows five different ways to create NumPy objects. First we create one by converting a Python list. Then we show two different factory routines that generate equally spaced grid points. These routines differ in how they interpret the provided boundary values: one routine includes both boundary values, and the other includes one and excludes the other. Next we create a vector filled with zeros and set each element in a loop. Finally, we read data from a text file. (I am showing only t…