Abigail  Cassin

Abigail Cassin

1595002140

Hands-on with Feature Engineering Techniques: Advanced Methods

This post is a part of a series about feature engineering techniques for machine learning with Python.

You can check out the rest of the articles:

Welcome to the last of this series on feature engineering! In today’s article, we’ll explore some advanced feature engineering techniques across different tasks. Specifically, we’ll look at advanced categorical encoding, advanced outlier detection, automated feature engineering and more.

#heartbeat #machine-learning #python #artificial-intelligence #programming

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Hands-on with Feature Engineering Techniques: Advanced Methods

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Vern  Greenholt

Vern Greenholt

1598245080

Feature Engineering: What is Feature Engineering?

According to a survey in Forbes, data scientists spend 80% of their time on data preparation. This shows the importance of feature engineering in data science. Here are some valuable quotes about Feature Engineering and its importance:

Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering — Prof. Andrew Ng.

The features you use influence more than everything else the result. No algorithm alone, to my knowledge, can supplement the information gain given by correct feature engineering — Luca Massaron

What is Feature Engineering?

Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in an improved model accuracy on unseen data.

Basically, all machine learning algorithms use some input data to create outputs. This input data comprises features, which are usually in the form of structured columns. Algorithms require features with some specific characteristic to work properly

Having and engineering good features will allow us to most accurately represent the underlying structure of the data and therefore create the best model. Features can be engineered by decomposing or splitting features, from external data sources, or aggregating or combining features to create new features.

#data-science #feature-engineering #feature-selection #data analysis

Ananya Gupta

1604922206

Top 7 Advantages Of Advanced Excel Learning

Advanced Excel Certification offers numerous job opportunities that have come up. Lately, companies search for a talented personality who holds great knowledge in excel. However, simply basic knowledge isn’t sufficient. If you would like to be a part of a well-renowned company then you want to have the excel certification matching industrial standards.

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Know More About Advanced Excel?

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Sival Alethea

Sival Alethea

1624291630

Data Structures Easy to Advanced Course - Full Tutorial from a Google Engineer

Learn and master the most common data structures in this full course from Google engineer William Fiset. This course teaches data structures to beginners using high quality animations to represent the data structures visually.

You will learn how to code various data structures together with simple to follow step-by-step instructions. Every data structure presented will be accompanied by some working source code (in Java) to solidify your understanding.
⭐️ Course Contents ⭐️
⌨️ (0:00:00) Abstract data types
⌨️ (0:04:28) Introduction to Big-O
⌨️ (0:17:00) Dynamic and Static Arrays
⌨️ (0:27:40) Dynamic Array Code
⌨️ (0:35:03) Linked Lists Introduction
⌨️ (0:49:16) Doubly Linked List Code
⌨️ (0:58:26) Stack Introduction
⌨️ (1:09:40) Stack Implementation
⌨️ (1:12:49) Stack Code
⌨️ (1:15:58) Queue Introduction
⌨️ (1:22:03) Queue Implementation
⌨️ (1:27:26) Queue Code
⌨️ (1:31:32) Priority Queue Introduction
⌨️ (1:44:16) Priority Queue Min Heaps and Max Heaps
⌨️ (1:49:55) Priority Queue Inserting Elements
⌨️ (1:59:27) Priority Queue Removing Elements
⌨️ (2:13:00) Priority Queue Code
⌨️ (2:28:26) Union Find Introduction
⌨️ (2:33:57) Union Find Kruskal’s Algorithm
⌨️ (2:40:04) Union Find - Union and Find Operations
⌨️ (2:50:30) Union Find Path Compression
⌨️ (2:56:37) Union Find Code
⌨️ (3:03:54) Binary Search Tree Introduction
⌨️ (3:15:57) Binary Search Tree Insertion
⌨️ (3:21:20) Binary Search Tree Removal
⌨️ (3:34:47) Binary Search Tree Traversals
⌨️ (3:46:17) Binary Search Tree Code
⌨️ (3:59:26) Hash table hash function
⌨️ (4:16:25) Hash table separate chaining
⌨️ (4:24:10) Hash table separate chaining source code
⌨️ (4:35:44) Hash table open addressing
⌨️ (4:46:36) Hash table linear probing
⌨️ (5:00:21) Hash table quadratic probing
⌨️ (5:09:32) Hash table double hashing
⌨️ (5:23:56) Hash table open addressing removing
⌨️ (5:31:02) Hash table open addressing code
⌨️ (5:45:36) Fenwick Tree range queries
⌨️ (5:58:46) Fenwick Tree point updates
⌨️ (6:03:09) Fenwick Tree construction
⌨️ (6:09:21) Fenwick tree source code
⌨️ (6:14:47) Suffix Array introduction
⌨️ (6:17:54) Longest Common Prefix (LCP) array
⌨️ (6:21:07) Suffix array finding unique substrings
⌨️ (6:25:36) Longest common substring problem suffix array
⌨️ (6:37:04) Longest common substring problem suffix array part 2
⌨️ (6:43:41) Longest Repeated Substring suffix array
⌨️ (6:48:13) Balanced binary search tree rotations
⌨️ (6:56:43) AVL tree insertion
⌨️ (7:05:42) AVL tree removals
⌨️ (7:14:12) AVL tree source code
⌨️ (7:30:49) Indexed Priority Queue | Data Structure
⌨️ (7:55:10) Indexed Priority Queue | Data Structure | Source Code

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=RBSGKlAvoiM&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3
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#data structures #data structures easy to advanced course #google engineer #william fiset #data structures easy to advanced course - full tutorial from a google engineer #advanced course

Feature Engineering & Feature Selection

WarningThere is no magical formula or Holy Grail here, though a new world might open the door for you.


📈Python for finance series

  1. Identifying Outliers
  2. Identifying Outliers — Part Two
  3. Identifying Outliers — Part Three
  4. Stylized Facts
  5. Feature Engineering & Feature Selection
  6. Data Transformation

Following up the previous posts in these series, this time we are going to explore a real Technical Analysis (TA) in the financial market. For a very long time, I have been fascinated by the inner logic of TA called Volume Spread Analysis (VSA). I have found no articles on applying modern Machine learning on this time proving long-lasting technique. Here I am trying to throw out a minnow to catch a whale. If I could make some noise in this field, it was worth the time I spent on this article.

Especially, after I read David H. Weis’s Trades About to Happen, in his book he described:

“Instead of analyzing an array of indicators or algorithms, you should be able to listen to what any market says about itself.”¹

To closely listen to the market, as also well said from this quote below, just as it may not be possible to predict the future, it is also hard to neglect things about to happen. The key is to capture what is about to happen and follow the flow.

Image for post

But how to perceive things about to happen, a statement made long ago by Richard Wyckoff gives some clues:

“Successful tape reading [chart reading] is a study of Force. It requires ability to judge which side has the greatest pulling power and one must have the courage to go with that side. There are critical points which occur in each swing just as in the life of a business or of an individual. At these junctures it seems as though a feather’s weight on either side would determine the immediate trend. Any one who can spot these points has much to win and little to lose.”²

#feature-engineering #feature-selection #trading #python #machine-learning