# Category Archives: Data Science

## Matplotlib Bar Chart Python / Pandas Examples

Are you looking to learn how to create bar charts / bar plots / bar graph using the combination of Matplotlib and Pandas in Python? Bar charts are one of the most commonly used visualizations in data analysis, enabling us to present categorical data in a visually appealing and intuitive manner. Whether you’re a beginner data scientist or an intermediate-level practitioner seeking to enhance your visualization skills, this blog will provide you with practical examples and hands-on guidance to create compelling bar charts / bar plots using Matplotlib libraries in Python. You will also learn how to leverage the data manipulation capabilities of Pandas to prepare the data for visualization, …

## One-hot Encoding Concepts & Python Examples

Have you ever encountered categorical variables in your data analysis or machine learning projects? These variables represent discrete qualities or characteristics, such as colors, genders, or types of products. While numerical variables can be directly used as inputs for machine learning algorithms, categorical variables require a different approach. One common technique used to convert categorical variables into a numerical representation is called one-hot encoding, also known as dummy encoding. When working with machine learning algorithms, categorical variables need to be transformed into a numerical representation to be effectively used as inputs. This is where one-hot encoding comes to rescue. In this post, you will learn about One-hot Encoding concepts and …

## Difference between Parametric vs Non-Parametric Models

When working with machine learning models, data scientists often come across a fundamental question: What sets parametric and non-parametric models apart? This is also one of the most frequent questions asked in the interviews. Machine learning models can be parametric or non-parametric. Parametric models are those that require the specification of some parameters before they can be used to make predictions, while non-parametric models do not rely on any specific parameter settings and therefore often produce more accurate results. These two distinct approaches play a crucial role in predictive modeling, each offering unique advantages and considerations. This blog post discusses parametric vs non-parametric machine learning models with examples along with …

## Ridge Regression Concepts & Python example

Ridge regression is a type of linear regression that penalizes ridge coefficients. This technique can be used to reduce the effects of multicollinearity in ridge regression, which may result from high correlations among predictors or between predictors and independent variables. In this tutorial, we will explain ridge regression with a Python example. What is Ridge Regression? Ridge regression is a powerful technique in machine learning that addresses the issue of overfitting in linear models. In linear regression, we aim to model the relationship between a response variable and one or more predictor variables. However, when there are multiple variables that are highly correlated, the model can become too complex and …

## Machine Learning NPTEL Online Courses List 2023

Machine learning is a rapidly evolving field that has gained immense popularity in recent years. As technology continues to advance, the demand for professionals with expertise in machine learning continues to soar. If you’re someone who is interested in diving deep into the world of machine learning or looking to enhance your existing knowledge, the NPTel courses are an excellent avenue to explore. The National Programme on Technology Enhanced Learning (NPTel) is a joint initiative by the Indian Institutes of Technology (IITs) and the Indian Institute of Science (IISc). It offers a wide range of online courses across various disciplines, including computer science and engineering. In this blog, we will …

## Recommender Systems in Machine Learning: Examples

Recommender systems are used in machine learning to predict the ratings or preferences of items for a given user. They are commonly used in e-commerce applications to suggest items that a user may be interested in. One common example of a recommender system is Netflix. Netflix uses a recommender system to suggest movies and TV shows that a user may want to watch. The algorithm looks at past ratings and preferences to make suggestions. In this blog post, you will learn about recommender systems and some of the different types of recommender systems with the help of examples. Recommender systems make use of machine learning to predict the ratings or …

## Binomial Distribution Explained with Examples

Have you ever wondered how to predict the number of successes in a series of independent trials? Or perhaps you’ve been curious about the probability of achieving a specific outcome in a sequence of yes-or-no questions. If so, we are essentially talking about the binomial distribution. It’s important for data scientists to understand this concept as binomials are used often in business applications. The binomial distribution is a discrete probability distribution that applies to binomial experiments (experiments with binary outcomes). It’s the number of successes in a specific number of trials. Sighting a simple yet real-life example, the binomial distribution may be imagined as the probability distribution of a number …

## Difference between Data Science & Data Analytics

What’s the difference between data science and data analytics? Many people use these terms interchangeably, but there is a big distinction between the two fields. Data science is more focused on understanding and deriving insights from data while leveraging statistical and machine learning methods, while data analytics is an overarching term used to solve problems using analytical techniques while leveraging data. Both the terms are in a way related. In this blog post, we’ll explore the differences between data science and data analytics in greater detail, with examples of each. The following are key topics in relation to the difference between data science and data analytics: Different forms/purposes Different techniques …

## Hold-out Method for Training Machine Learning Models

The hold-out method for training the machine learning models is a technique that involves splitting the data into different sets: one set for training, and other sets for validation and testing. The hold-out method is used to check how well a machine learning model will perform on the new data. In this post, you will learn about the hold-out method used during the process of training the machine learning model. Do check out my post on what is machine learning? concepts & examples for a detailed understanding of different aspects related to the basics of machine learning. Also, check out a related post on what is data science? When evaluating …

## How to Choose Right Statistical Tests: Examples

Whether you are a researcher, data analyst, or data scientist, selecting the appropriate statistical test is crucial for accurate and reliable data analysis. With numerous tests available, it can be overwhelming to determine the right one for your research question and data type. In this blog, the aim is to simplify the process, providing you with a systematic approach to choosing the right statistical test. This blog will be particularly helpful for those who are new to statistical analysis and are unsure which test to use for their specific needs. You will learn a clear and structured method for selecting the appropriate statistical test. By considering factors such as data …

## One-way ANOVA test: Concepts, Formula & Examples

The one-way analysis of variance (ANOVA) test is a statistical procedure commonly used to compare the means values on a specific variable between three or more groups. The significance of the difference between the means of two samples can be judged through either t-test or z-test depending upon different criteria, but it becomes tricky when there is a need to simultaneously evaluate the significance of the difference amongst three or more sample means. This is where one-way ANOVA test comes to rescue. The ANOVA technique enables us to perform this simultaneous test and as such is considered to be an important tool of analysis. As data scientists, it is of …

## Neyman-Pearson Lemma: Hypothesis Test, Examples

Have you ever faced a crucial decision where you needed to rely on data to guide your choice? Whether it’s determining the effectiveness of a new medical treatment or assessing the quality of a manufacturing process, hypothesis testing becomes essential. That’s where the Neyman-Pearson Lemma steps in, offering a powerful framework for making informed decisions based on statistical evidence. The Neyman-Pearson Lemma holds immense importance when it comes to solving problems that demand decision making or conclusions to a higher accuracy. By understanding this concept, we learn to navigate the complexities of hypothesis testing, ensuring we make the best choices with greater confidence. In this blog post, we will explore …

## Pandas CSV to Dataframe Python Example

Converting CSV files to DataFrames is a common task in data analysis. In this blog, we’ll explore a Python code example using the Pandas library to efficiently convert CSV files to DataFrames. This approach offers flexibility, speed, and convenience, making it a valuable technique for handling large datasets. Read CSV into Pandas Dataframe The following is the code which can be used to read the CSV file from local drive: In case, you want to read CSV file from the URL, the following will be the code. As a matter of fact, nothing changes except for the fact that you pass the URL to read_csv function. The following are some …

## Occam’s Razor in Machine Learning: Examples

“Everything should be made as simple as possible, but not simpler.” – Albert Einstein Consider this: According to a recent study by IDC, data scientists spend approximately 80% of their time cleaning and preparing data for analysis, leaving only 20% of their time for the actual tasks of analysis, modeling, and interpretation. Does this sound familiar to you? Are you frustrated by the amount of time you spend on complex data wrangling and model tuning, only to find that your machine learning model doesn’t generalize well to new data? As data scientists, we often find ourselves in a predicament. We strive for the highest accuracy and predictive power in our …

## Outlier Detection Techniques in Python: Examples

In the realm of data science, mastering outlier detection techniques is paramount for ensuring data integrity and robust machine learning model performance. Outliers are the data points which deviate significantly from the norm. The outliers data points can greatly impact the accuracy and reliability of statistical analyses and machine learning models. In this blog, we will explore a variety of outlier detection techniques using Python. The methods covered will include statistical approaches like the z-score method and the interquartile range (IQR) method, as well as visualization techniques like box plots and scatter plots. Whether you are a data science enthusiast or a seasoned professional, it is important to grasp these …

## Boston Housing Dataset Linear Regression: Predicting House Prices

Predicting house prices accurately is crucial in the real estate industry. However, it can be challenging to determine the factors that significantly impact house prices. Without a clear understanding of these factors, accurate predictions are difficult to achieve. The Boston Housing Dataset addresses this problem by providing a comprehensive set of variables that influence house prices in the Boston area. However, effectively utilizing this dataset and building robust predictive models require appropriate techniques and evaluation methods. In this blog, we will provide an overview of the Boston Housing Dataset and explore linear regression, LASSO, and Ridge regression as potential models for predicting house prices. Each model has its unique properties …

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