All Articles

K-Means in Machine Learning

Many clustering algorithms are available in Scikit-Learn and elsewhere, but perhaps the simplest to understand is an algorithm known as k-means clustering, which is implemented in sklearn.cluster.KMeans. Introduction The k-means algorithm searches for a pre-determined number…

Employee Turnover Prediction

This article features the implementation of an employee turnover analysis that is built using Python’s Scikit-Learn library. In this article, I will use Logistic Regression and Random Forest Machine Learning algorithms. At the end of…

Customer Segmentation

If you want to find out who are your best customers, using an old technique RFM matrix principle is still the best in the business. RFM means – Recency, Frequency and Monetary. RFM is basically…

Time Series Forecasting

Time Series Forecasting

Many Business activities are seasonal in nature, where most of the business are dependent on a particular time of festival and holidays. Every business uses sales promotion techniques to increase the demand for their products…

TensorFlow

TensorFlow Tutorial

TensorFlow is a powerful library for numerical computation, particularly well suited and fine-tuned for large–scale Machine Learning ( but you could use it for anything else that requires heavy calculations). The Google Brain team developed…

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning (RL) is one of the most exciting fields of machine learning today. and also one the oldest. It has been around since the 1950s, producing many exciting applications over the years, particularly in…

Merging Datasets

Merging Datasets

Merging Datasets is one of the most high-performance features, which is provided by pandas in Python. In this article, I will show how we can merge datasets in Python with the help of examples and…

Model Selection

Model Selection Technique

Evaluating a model is simple enough to use a test set. But suppose you are hesitating in model selection between two types of models (say, a linear model and a polynomial model); how can you…

Handling Missing Data in data Science

Missing Data Handling

There is a lot of difference between the data you get to practice data science skills and the data you get in the real world. Honestly speaking, many datasets you will get in the process…

Training and Test sets

Training and Test Sets

This article is about description for those who need to know what is the actual difference between the dataset split between the Training and Test sets in Machine Learning while training and classifying models. What…

Manifold Learning in Machine Learning

Manifold Learning

Rotating, re-orienting, or stretching the piece of paper in three-dimensional space doesn’t change the flat geometry of the article: such operations are akin to linear embeddings. If you bend, curl, or crumple the paper, it…

PCA in Machine Learning

PCA in Machine Learning

In this article, you will explore what is perhaps one of the most broadly used of unsupervised algorithms, principal component analysis (PCA). PCA is fundamentally a dimensionality reduction algorithm, but it can also be useful…

best data science books

Best Data Science Books

Below are some of the famous Data Science books that will help beginners explore more about Data Science and the experienced practitioners to gain more deep knowledge. I found these books really useful and highly…

Decision trees in machine learning

Decision Trees in Machine Learning

Decision Trees are versatile Machine Learning algorithms that can perform both classification and regression tasks, and even multi-output tasks. They are powerful algorithms, capable of fitting complex datasets. Decision trees are also the fundamental components…

support vector machines

Support Vector Machines (SVM) in Machine Learning

Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. In this article, I will develop the intuition behind support vector machines and their use in…

Naive Bayes Classification in Machine Learning

In Machine Learning Naive Bayes models are a group of high-speed and simple classification algorithms that are often suitable for very high-dimensional datasets. Because they are so fast and have so few tunable parameters, they…

Workflow of machine learning projects

Workflow of Machine Learning Projects

This Workflow can guide you through your Machine Learning Projects. There are eight main steps: Frame the problem and look at the big picture. Get the data. Explore the data to get insights. Prepare the…

Understanding a Neural Network

What is a Neural Network Neural Network is a computational algorithm that is used in creating deep learning models for predictions and classifications. It is based on self-learning and training, rather than being explicitly programmed.…

feature engineering

Feature Engineering in Machine Learning

In the real world, data rarely comes in perfect form. With this in mind, one of the more critical steps in using machine learning in practice is Feature Engineering, that is, taking whatever information you have…