## Pandas Dataframe loc, iloc & brackets examples

Pandas is a powerful data analysis tool in Python that can be used for tasks such as data cleaning, exploratory data analysis, feature engineering, and predictive modeling. In this article, we will focus on how to use Pandas’ loc and iloc functions on Dataframe, as well as brackets with Dataframe, with examples. As a data scientist or data analyst, it is very important to understand how these functions work and when to use them. In this post, we will work with the following Pandas data frame. Use loc and iloc functions to get Rows of Dataframe The loc function is used to get a particular row in a Dataframe by …

## Pandas: How to Create a Dataframe – Examples

One of the most popular modules for working with data in Python is the Pandas library. Pandas provides data structures and operations for working with structured data. A key concept in Pandas is the Dataframe. Learning how to create and use dataframes is an important skill for anyone including data analysts and data scientists working with data in Python. In this post, you will learn about how to create a Pandas dataframe with some sample data. What is Pandas Dataframe? A Pandas dataframe is a two-dimensional data structure, like a table in a spreadsheet, with columns of data and rows of data. Dataframe is analogous to a table in SQL …

## Central Limit Theorem: Concepts & Examples

The central limit theorem is one of the most important concepts in statistics. This theorem states that, given a large enough sample size, the distribution of sample averages will be approximately normal. This is a huge deal because it means that we can use the normal distribution to make predictions about populations based on samples. In this article, we’ll explore the central limit theorem in more detail and look at some examples of how it works. As data scientists, it is important to understand the central limit theorem so that we can apply it to real-world situations. What is the central limit theorem and why is it important? The central …

## Probability concepts, formulas & real-world examples

Probability is a branch of mathematics that deals with the likelihood of an event occurring. It is important to understand probability concepts if you want to get good at data science and machine learning. In this blog post, we will discuss the basic concepts of probability and provide examples to help you understand it better. We will also introduce some common formulas associated with probability. So, let’s get started! What is probability and what are the different types? Probability is a concept in mathematics that measures the likelihood of an event occurring. It is typically expressed as a number between 0 and 1, with 0 indicating that an event is …

## Statistics – Random Variables, Types & Python Examples

Random variables are one of the most important concepts in statistics. In this blog post, we will discuss what they are, their different types, and how they are related to the probability distribution. We will also provide examples so that you can better understand this concept. As a data scientist, it is of utmost importance that you have a strong understanding of random variables and how to work with them. What is a random variable and what are some examples? A random variable is a variable that can take on random values. The key difference between a variable and a random variable is that the value of the random variable …

## Histogram Plots using Matplotlib & Pandas: Python

Histograms are a graphical representation of the distribution of data. In Python, there are several ways to create histograms. One popular method is to use the Matplotlib library. In this tutorial, we will show you how to create different types histogram plots in Python using Matplotlib. As data scientists, it is important to learn how to create visualizations to communicate our findings. Histograms are one way to do this effectively. What are Histogram plot? Histogram plots are a way of representing the distribution of data. A histogram is made up of bars, with each bar representing a certain range of data values. The height of the bar indicates how many …

## How to Create Pandas Dataframe from Numpy Array

Pandas is a library for data analysis in Python. It offers a wide range of features, including working with missing data, handling time series data, and reading and writing data in different formats. Pandas also provides an efficient way to manipulate and calculate data. One of its key features is the Pandas DataFrame, which is a two-dimensional array with labeled rows and columns. A DataFrame is a table-like structure that contains columns and rows of data. Creating a Pandas DataFrame from a NumPy array is simple. In this post, you will get a code sample for creating a Pandas Dataframe using a Numpy array with Python programming. Step 1: Load …

## Ensemble Methods in Machine Learning: Examples

Machine learning models are often trained with a variety of different methods in order to create a more accurate prediction. Ensemble methods are one way to do this, and involve combining the predictions of several different models in order to get a more accurate result. When different models make predictions together, it can help create a more accurate result. Data scientists should care about this because it can help them create models that are more accurate. In this article, we will look at some of the common ensemble methods used in machine learning. Data scientists should care about this because it can help them create models that are more accurate. …

## Learning Curves Python Sklearn Example

In this post, you will learn about how to use learning curves using Python code (Sklearn) example to determine machine learning model bias-variance. Knowing how to use learning curves will help you assess/diagnose whether the model is suffering from high bias (underfitting) or high variance (overfitting) and whether increasing training data samples could help solve the bias or variance problem. You may want to check some of the following posts in order to get a better understanding of bias-variance and underfitting-overfitting. Bias-variance concepts and interview questions Overfitting/Underfitting concepts and interview questions What are learning curves & why they are important? Learning curve in machine learning is used to assess how models will …

## Machine Learning Sklearn Pipeline – Python Example

In this post, you will learning about concepts about machine learning (ML) pipeline and how to build ML pipeline using Python Sklearn Pipeline (sklearn.pipeline) package. Getting to know how to use Sklearn.pipeline effectively for training/testing machine learning models will help automate various different activities such as feature scaling, feature selection / extraction and training/testing the models. It is recommended for data scientists (Python) to get a good understanding of Sklearn.pipeline. Introduction to Machine Learning Pipeline & Sklearn.pipeline Machine Learning (ML) pipeline, theoretically, represents different steps including data transformation and prediction through which data passes. The outcome of the pipeline is the trained model which can be used for making the predictions. …

## Sequence Models Quiz 1 – Test Your Understanding

Sequence modeling is extremely important for data scientists as it can be used in a variety of real-world applications. Sequence modeling is used in speech recognition, image recognition, machine translation, and text summarization. These are all important applications that data scientists must be familiar with. As a data scientist, it is important to have a good understanding of sequence modeling and how it can be used to solve real-world problems. In this blog, we’ll be looking at a quiz around sequence models, more specifically the different types of sequence models. This will help us understand how sequence models work and can be used in an interview situation. Before getting into …

## Credit Risk Modeling & Machine Learning Use Cases

Credit risk modeling is a process of estimating the probability that a borrower will default on their loan. This is done by analyzing historical data about borrowers’ credit behavior. Credit risk models are used by banks and financial institutions to make better decisions about who to lend money to, how much to extend, and when to pull back. Banks and financial institutions are under constant pressure to improve their business outcomes. One way they are doing this is by using machine learning to better predict credit risk. By understanding the factors that contribute to a borrower’s likelihood of default, banks can make more informed decisions about who to lend money …

## Data Lineage Concepts, Examples & Tools

Data lineage can be a complex and confusing topic. It’s hard to know where your data comes from, how it’s been changed, and what the impact of those changes has been. Data lineage tools make tracing data easy and straightforward. By understanding your data’s history you can more effectively troubleshoot issues, optimize processes, and make better decisions. In this blog, you will learn about data lineage concepts, examples, and tools. As a data professional, you must have a strong understanding of data lineage. What is Data Lineage and why is it important? Data lineage is a term used in data management to describe the path that data takes from its …

## Data Analyst Technical & Soft Skills

Do you want to become a data analyst? It’s a great career choice! Data analysts are in high demand these days. Companies rely on data analysts to help them make better decisions by turning data into insights. In order to be successful, data analysts need a mix of technical skills and soft skills. Technical skills include expertise in analyzing data. Soft skills include communication and problem-solving skills. Data analysts must be able to take data and turn it into insights that help their company make better decisions. They also need to be able to effectively communicate those insights to people who may not have a technical background. In this blog …

## Performance metrics for Time-series Forecasting models

Time-series forecasting is a specific type of forecasting / predictive modeling that uses historical data to predict future trends in a particular time series. There are several different metrics that can be used to measure the accuracy and efficacy of a time-series forecasting model, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and others. By understanding these performance metrics, you can better assess the effectiveness of your time-series forecasting model and make necessary adjustments as needed. In this blog, you will learn about the different time-series forecasting model performance metrics and how to use them for model evaluation. Check out a related post – Different types of time-series …

## Sample Dataset for Regression & Classification: Python

A lot of beginners in the field of data science / machine learning are intimidated by the prospect of doing data analysis and building regression (linear) & classification models in Python. But with an ability to create sample dataset using Python packages, you can practice your skills and build your confidence over a period of time. The technique demonstrated in this blog post to create and visualize / plot the sample dataset includes datasets that can be used for regression models such as linear regression and classification models such as logistic regression, random forest, SVM etc. You can use this technique to explore different methods for solving the same problem. …