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What is CodeX?

CodeX is a medium publication that aims at providing top-notch content based on technology & coding.

We expect writers who submit their articles to CodeX must fall under the category of computer science, innovative technology, programming, coding & computer science concepts, personal experience in tech or coding, tech in major fields (like Healthcare, education, and so on).

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CODEX

Familiarize the fundamentals of NumPy with practical implementation

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NumPy

NumPy is a python package that is primarily used for solving mathematical problems and high-level computations. It provides programmers with a wide variety of tools and built-in functions to create their own custom and complex functions to solve specific problems.

In this article, let’s familiarize the basics of NumPy by building some mathematical functions.

Function: 1 | Sigmoid

A Sigmoid function is a mathematical function that has a characteristic S-shaped curve. The Sigmoid function is normally used to refer specifically to the logistic function, also called the logistic sigmoid function.

All sigmoid functions have the property that they map the entire number line into a small range such as between 0 and 1, or -1 and 1, so one use of a sigmoid function is to convert a real value into one that can be interpreted as a probability. …


Source

It’s much easier than what you think it is

In recent days, there has been a lot of improvements in this modern technological world like autonomous cars, facial recognition systems, chatbots to name a few. These cool inventions are made possible only with the help of Deep Learning. Read this article to know what deep learning is and its supporting factors that made deep learning so popular.

Deep Learning

Deep learning is a subset of Machine Learning (ML) that gives us certain predictions for the given set of inputs by learning from examples or previous data points. That’s what the whole concept is about. Technically speaking, deep learning is the higher form of Machine Learning that replicates the working of the human brain to perform complex tasks. Deep learning is much powerful than Machine Learning in that it is capable of giving accurate predictions for both structured (data that has columns and rows, for example, housing data, etc.) and unstructured data (examples are audio patterns, images, etc.). …


Learn to construct your own Python functions to solve geometrical problems without importing any packages

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Introduction

Coordinate Geometry also is known as Analytical Geometry or Cartesian Geometry, used to analyze or study geometry through the means of coordinates and vertices. This concept is held in much geometrical mathematics for example to find the area of geometrical shapes using coordinates, finding the midpoints, dividing a line segment into m:n ratio, and so on. Each of these concepts has its own formulas and methodology for solving the problems. What if we can solve these using python?

Python, being a general-purpose programming language, is highly powerful and efficient in solving mathematical tasks or problems. Even though there are several scientific packages like NumPy and SciPy, defining our own mathematical functions and parameters on top of python would be more flexible. So, what are we going to solve? …


Learn to build and visualize K-means models to solve clustering problems

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K-Means Clustering

The K-Means clustering beams at partitioning the ‘n’ number of observations into a mentioned number of ‘k’ clusters (produces sphere-like clusters). The K-Means is an unsupervised learning algorithm and one of the simplest algorithm used for clustering tasks. The K-Means divides the data into non-overlapping subsets without any cluster-internal structure. The values which are within a cluster are very similar to each other but, the values across different clusters vary enormously. K-Means clustering works really well with medium and large-sized data.

Despite the algorithm’s simplicity, K-Means is still powerful for clustering cases in data science. In this article, we are going to tackle a clustering problem which is customer segmentation (dividing customers into groups based on similar characteristics) using the K-means algorithm. …


Using XGBoost, Random forest, KNN, Logistic regression, SVM, and Decision tree to solve classification problems

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Case

Assume that you are employed to help a credit card company to detect potential fraud cases so that the customers are ensured that they won’t be charged for the items they did not purchase. You are given a dataset containing the transactions between people, the information that they are fraud or not, and you are asked to differentiate between them. This is the case we are going to deal with. Our ultimate intent is to tackle this situation by building classification models to classify and distinguish fraud transactions.

Why Classification? Classification is the process of predicting discrete variables (binary, Yes/no, etc.). Given the case, it will be more optimistic to deploy a classification model rather than any others.


Using Ridge, Bayesian, Lasso, Elastic Net, and OLS regression model for prediction

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Introduction

Estimating the sale prices of houses is one of the basic projects to have on your Data Science CV. By finishing this article, you will be able to predict continuous variables using various types of linear regression algorithm.

Why linear regression? Linear regression is an algorithm used to predict values that are continuous in nature. It became more popular because it is the best algorithm to start with if you are a newbie to ML.

To predict the sale prices we are going to use the following linear regression algorithms: Ordinal Least Square (OLS) algorithm, Ridge regression algorithm, Lasso regression algorithm, Bayesian regression algorithm, and lastly Elastic Net regression algorithm. …


Learn to build Support Vector Machine models for classification problems with python

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Support Vector Machine

SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, the characteristics of new data can be used to predict the group to which a new record should belong.

Advantages

  • SVM is a very helpful method if we don’t have much idea about the data. It can be used for data such as image, text, audio, etc. …


Professional financial charts with less-code using Plotly in Python

Photo by Chris Liverani on Unsplash

Disclaimer: This article is purely for educational purposes and should not be considered as a piece of advice for making investments.

Plotly for Visualization

One of the most popular packages used for interactive visualizations is Plotly. Plotly is built on top of python and enables data scientists to produce professional and great-looking plots with less-code. It became popular because of its extensive category of plots which can be produced in no-time. The categories of plots include basic charts, statistical charts, ML and AI charts, scientific charts, and financial charts.

In this article, I’m going to walk you through the process of creating interactive and professional financial charts in Plotly with python. We will also explore yahoo’s API for pulling historical stock data which we will be using for visualizations. …


A practical introduction to Logistic Regression for classification and predictions in Python

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Logistic Regression

Logistic Regression is an algorithm that can be used for regression as well as classification tasks but it is widely used for classification tasks.’

‘Logistic Regression is used to predict categorical variables with the help of dependent variables. Consider there are two classes and a new data point is to be checked which class it would belong to. Then algorithms compute probability values that range from 0 and 1. For example, whether it will rain today or not.

Python for Logistic Regression

Python is the most powerful and comes in handy for data scientists to perform simple or complex machine learning algorithms. It has an extensive archive of powerful packages for machine learning to help data scientists automate their way of coding. In this article, we will be building and evaluating our logistic regression model using python’s scikit-learn package. And, the case we are going to solve is whether a telecommunication company's customers are willing to stay in there or not. …

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