Category Archives: Python

Pandas CSV to Dataframe Python Example

Read CSV Files to Pandas Dataframe using Python

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 …

Continue reading

Posted in Data Science, Python. Tagged with , .

Outlier Detection Techniques in Python: Examples

Outlier detection Python Machine Learning

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 …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .

Python Tesseract PDF & OCR Example

python tesseract pdf ocr example

Have you ever needed to extract text from an image or a PDF file? If so, you’re in luck! Python has an amazing library called Tesseract that can perform Optical Character Recognition (OCR) to extract text from images and PDFs. In this blog, I will share sample Python code using with you can use Tesseract to extract text from images and PDFs. As a data scientist, it can be very helpful and useful to be able to extract text from images or PDFs, especially when working with large amounts of data found in receipts, invoices, etc. Tesseract is an OCR engine widely used in the industry, known for its accuracy …

Continue reading

Posted in Data Science, Python. Tagged with , .

Python: Convert JSON to CSV Example

Convert JSON to CSV using Python Code

Have you ever wondered how to convert JSON data to CSV using Python? JSON (JavaScript Object Notation) is a popular data format used to exchange data between servers and web applications. However, sometimes it’s necessary to convert this data into another format, such as CSV (Comma Separated Values). CSV is a simple text format that is commonly used to store and exchange tabular data. In this blog post, a sample Python code is provided for converting JSON to CSV using Python. The code showcases the Python code that uses the json and csv modules to read and write data. But before going forward with the code, let’s take a look …

Continue reading

Posted in Python.

Seaborn: Multiple Line Plots with Markers, Legend

Seaborn multiple line plots using markers, legends

Do you want to learn how to create visually stunning and informative line plots that will captivate your audience by providing most apt information? Do you have the requirement of creating multiple line plots in the same figure representing sales of different products across different months in a year? Are you looking for a takeaway Python code with Seaborn library for creating line plots? If yes, you are in the right place. In this blog post, we’ll explore how to create multiple line plots with Seaborn, a powerful data visualization library built on top of Matplotlib. I will also show how to add markers to the line plots to make …

Continue reading

Posted in Data Science, Data Visualization, Python.

Sklearn Algorithms Cheat Sheet with Examples

sklearn algorithms cheat sheet

The Sklearn library, short for Scikit-learn, is one of the most popular and widely-used libraries for machine learning in Python. It offers a comprehensive set of tools for data analysis, preprocessing, model selection, and evaluation. As a beginner data scientist, it can be overwhelming to navigate the various algorithms and functions within Sklearn. This is where the Sklearn Algorithms Cheat Sheet comes in handy. This cheat sheet provides a quick reference guide for beginners to easily understand and select the appropriate algorithm for their specific task. In this cheat sheet, I have compiled a list of common supervised and unsupervised learning algorithms, along with their Sklearn classes and example use …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Sklearn Neural Network Example – MLPRegressor

Sklearn Neural Network MLPRegressor Regression Model

Are you interested in using neural networks to solve complex regression problems, but not sure where to start? Sklearn’s MLPRegressor can help you get started with building neural network models for regression tasks. While the packages from Keras, Tensorflow or PyTorch are powerful and widely used in deep learning, Sklearn’s MLPRegressor is still an excellent choice for building neural network models for regression tasks when you are starting on. Recall that Python Sklearn library is one of the most popular machine learning libraries, and it provides a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. In this blog post, we will be focusing on training a …

Continue reading

Posted in Data Science, Deep Learning, Machine Learning, Python. Tagged with , , .

KMeans Silhouette Score Python Example

If you’re building machine learning models for solving different prediction problems, you’ve probably heard of clustering. Clustering is a popular unsupervised learning technique used to group data points with similar features into distinct clusters. One of the most widely used clustering algorithms is KMeans, which is popular due to its simplicity and efficiency. However, one major challenge in clustering is determining the optimal number of clusters that should be used to group the data points. This is where the Silhouette Score comes into play, as it helps us measure the quality of clustering and determine the optimal number of clusters. Silhouette score helps us get further clarity for the following …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Python – Draw Confusion Matrix using Matplotlib

Classification models are a fundamental part of machine learning and are used extensively in various industries. Evaluating the performance of these models is critical in determining their effectiveness and identifying areas for improvement. One of the most common tools used for evaluating classification models is the confusion matrix. It provides a visual representation of the model’s performance by displaying the number of true positives, false positives, true negatives, and false negatives. In this post, we will explore how to create and visualize confusion matrices in Python using Matplotlib. We will walk through the process step-by-step and provide examples that demonstrate the use of Matplotlib in creating clear and concise confusion …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Support Vector Machine (SVM) Python Example

support vector machine - SVM

Support Vector Machines (SVMs) are a powerful and versatile machine learning algorithm that has gained widespread popularity among data scientists in recent years. SVMs are widely used for classification, regression, and outlier detection (one-class SVM), and have proven to be highly effective in solving complex problems in various fields, including computer vision (image classification, object detection, etc.), natural language processing (sentiment analysis, text classification, etc.), and bioinformatics (gene expression analysis, protein classification, disease diagnosis, etc.). In this post, you will learn about the concepts of Support Vector Machine (SVM)  with the help of  Python code example for building a machine learning classification model. We will work with Python Sklearn package for building the …

Continue reading

Posted in AI, Data Science, Machine Learning, Python. Tagged with , , .

Hidden Markov Models: Concepts, Examples

hidden markov model

Hidden Markov models (HMMs) are a type of statistical modeling that has been used for several years. They have been applied in different fields such as medicine, computer science, and data science. The Hidden Markov model (HMM) is the foundation of many modern-day data science algorithms. It has been used in data science to make efficient use of observations for successful predictions or decision-making processes. This blog post will cover hidden Markov models with real-world examples and important concepts related to hidden Markov models. What are Markov Models? Markov models are named after Andrey Markov, who first developed them in the early 1900s. Markov models are a type of probabilistic …

Continue reading

Posted in Data Science, Python. Tagged with .

Python Pickle Example: What, Why, How

python pickle file example

Have you ever heard of the term “Python Pickle“? If not, don’t feel bad—it can be a confusing concept. However, it is a powerful tool that all data scientists, Python programmers, and web application developers should understand. In this article, we’ll break down what exactly pickling is, why it’s so important, and how to use it in your projects. What is Python Pickle? In its simplest form, pickling is the process of converting any object into a byte stream (a sequence of bytes). This byte stream can then be transmitted over a network or stored in a file for later use. It’s like putting the object into an envelope and …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .

Machine Learning Programming Languages List

machine learning programming languages

If you’re interested in pursuing a career in machine learning, you’ll need to have a firm grasp of at least one programming language. But with so many languages to choose from, which one should you learn? Here are three of the most popular machine learning programming languages, along with a brief overview of each. Python Python is a programming language with many features that make it well suited for machine learning. It has a large and active community of developers who have contributed a wide variety of libraries and tools. Python’s syntax is relatively simple and easy to learn, making it a good choice for people who are new to …

Continue reading

Posted in Machine Learning, Programming, Python, R. Tagged with , .

How to Prepare for a Python Interview

Python interview questions and answers

Python has become the most popular programming language in the world and is one of the most in-demand languages by employers. It is a widely used high-level interpreted programming language. Its design philosophy emphasizes code readability and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java. The language provides constructs intended to enable clear programs on both a small and large scale. As such, if you’re a Python programmer, you’re likely to face stiff competition when applying for jobs. In order to increase your chances of landing an interview, it’s important to be well prepared. In …

Continue reading

Posted in Career Planning, Python. Tagged with , .

K-Nearest Neighbors (KNN) Python Examples

If you’re working with data analytics projects including building machine learning (ML) models, you’ve probably heard of the K-nearest neighbors (KNN) algorithm. But what is it, exactly? And more importantly, how can you use it in your own AI / ML projects? In this post, we’ll take a closer look at the KNN algorithm and walk through a simple Python example. You will learn about the K-nearest neighbors algorithm with Python Sklearn examples. K-nearest neighbors algorithm is used for solving both classification and regression machine learning problems. Stay tuned!  Introduction to K-Nearest Neighbors (K-NN) Algorithm K-nearest neighbors is a supervised machine learning algorithm for classification and regression. In both cases, the input consists …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Stacking Classifier Sklearn Python Example

Stacking classifier python example

In this blog post, we will be going over a very simple example of how to train a stacking classifier machine learning model in Python using the Sklearn library and learn the concepts of stacking classifier. A stacking classifier is an ensemble learning method that combines multiple classification models to create one “super” model. This can often lead to improved performance, since the combined model can learn from the strengths of each individual model. What are Stacking Classifiers? Stacking is a machine learning ensemble technique that combines multiple models to form a single powerful model. The individual models are trained on different subsets of the data using some type of …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .