How to get odd and even numbers from a list in Python

Hits: 16  How to get odd and even numbers from a list in Python In this coding example, we learn how to find odd and even numbers from a list using Python program. Code Example:   Outcomes:   Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio Projects …

Python Example – The most simplest python program to create a QR code

Hits: 8 The most simplest python program to create a QR code In this coding example,  we showed how to generate a QR code using Python package called pyqrcode. It is a very simple example.     Result   Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio …

Linear, Lasso, and Ridge Regression with R

Hits: 13 Linear, Lasso, and Ridge Regression with R   Introduction Machine learning is used by many organizations to identify and solve business problems. The two types of supervised machine learning algorithms are classification and regression. This guide will focus on regression models that predict a continuous outcome. You’ll learn how to implement linear and regularized regression models …

Splitting and Combining Data with R

Hits: 8 Splitting and Combining Data with R   Introduction In real-world data science projects, it is often necessary to divide data into two or more subsets or to combine multiple sets into one. This is an integral part of the data wrangling process for predictive modeling. In this guide, you will learn techniques for …

Reshaping Data with R

Hits: 4 Reshaping Data with R   Introduction In predictive modeling, it is often necessary to reshape the data to make it ready for conducting analysis or building models. The process of transforming the data into a clear, simple, and desirable form is an integral component of data science. The most common reshaping process is …

Coping with Missing, Invalid and Duplicate Data in R

Hits: 3 Coping with Missing, Invalid and Duplicate Data in R   Introduction A vital component of data science is cleaning the data and getting it ready for predictive modeling. The most common problem related to data cleaning is coping with missing data, invalid records and duplicate values. In this guide, you will learn about …

Visualization of Text Data Using Word Cloud in R

Hits: 2 Visualization of Text Data Using Word Cloud in R   Introduction Visualization plays an important role in exploratory data analysis and feature engineering. However, visualizing text data can be tricky because it is unstructured. Word Cloud provides an excellent option to visualize the text data in the form of tags, or words, where …

Machine Learning with Text Data Using R

Hits: 2 Machine Learning with Text Data Using R   Introduction The domain of analytics that addresses how computers understand text is called Natural Language Processing (NLP). NLP has multiple applications like sentiment analysis, chatbots, AI agents, social media analytics, as well as text classification. In this guide, you will learn how to build a …

Hypothesis Testing – Interpreting Data with Statistical Models

Hits: 0 Hypothesis Testing – Interpreting Data with Statistical Models   Introduction Building predictive models, or carrying out data science research, depends on formulating a hypothesis and drawing conclusions using statistical tests. In this guide, you will learn about how to perform these tests using the statistical programming language, ‘R’. The most widely used inferential …

Time Series Forecasting Using R

Hits: 1 Time Series Forecasting Using R   Introduction In this guide, you will learn how to implement the following time series forecasting techniques using the statistical programming language ‘R’: 1. Naive Method 2. Simple Exponential Smoothing 3. Holt’s Trend Method 4. ARIMA 5. TBATS We will begin by exploring the data. Problem Statement Unemployment …

Interpreting Data Using Statistical Models with R

Hits: 2 Interpreting Data Using Statistical Models with R   Introduction Statistical models are useful not only in machine learning, but also in interpreting data and understanding the relationships between the variables. In this guide, the reader will learn how to fit and analyze statistical models on the quantitative (linear regression) and qualitative (logistic regression) …

Data Science in R: Interpreting Data Using Descriptive Statistics with R

Hits: 0 Interpreting Data Using Descriptive Statistics with R   Introduction Descriptive Statistics is the foundation block of summarizing data. It is divided into the measures of central tendency and the measures of dispersion. Measures of central tendency include mean, median, and the mode, while the measures of variability include standard deviation, variance, and the …

Understanding ROC Curves with Python

Hits: 5  Understanding ROC Curves with Python In the current age where Data Science / AI is booming, it is important to understand how Machine Learning is used in the industry to solve complex business problems. In order to select which Machine Learning model should be used in production, a selection metric is chosen upon …