Logsum in the Logit Model

2023-08-04

Deploying Streamlit to Cloud Run

This article explains the steps to deploy Streamlit to Cloud Run. Terraform is used to create the necessary infrastructure resources on Cloud Run and deployment process is automated via GitHub Actions.

2023-06-10

Black Formatter in VSCode

This article introduces the Black Code Formatter in VSCode, including installation and usage instructions.

VSCode

2023-05-13

Loading GTFS Realtime in Python

This article introduces a method for loading GTFS Realtime data in Python.

Transportation

2023-04-01

Retry with Tenacity in Python

This article introduces the Tenacity library, enabling seamless handling of temporary failures and retries in Python. Explore the installation process, basic usage, customization options, and exception handling capabilities of Tenacity, with examples demonstrating how to effectively apply these features in various scenarios.

2023-03-31

Python Version Management with Pyenv

This article provides a guide to using Pyenv, an open-source Python version management tool that simplifies the installation and switching of Python versions on a per-project basis. Discover how to install Pyenv on various operating systems, grasp essential commands, and learn how to integrate Pyenv with virtual environments. Dive into managing multiple Python versions using global, local, and shell scopes, and improve your projects' reliability and maintainability.

2023-03-31

Virtual Environments in Python

This article offers a guide on virtual environments (venvs) in Python, explaining their importance and key benefits such as isolation, simplified dependency management, and reproducibility. Learn how to install venv, create and manage virtual environments, and effectively use pip for package management within your isolated project spaces.

2023-03-31

contextlib.contextmanager

This article offers a guide to using Python's contextlib.contextmanager, a powerful tool for creating custom context managers through a simple function and a decorator. Discover how the contextmanager decorator streamlines the process of creating custom context managers, explore real-life examples and applications, and learn the importance of error handling and combining context managers with try-except blocks for improved reliability and maintainability in your code.

2023-03-31

contextlib.ExitStack

This article delves into Python's ExitStack, a versatile context manager from the contextlib module. It demonstrates how ExitStack can simplify file management tasks, manage multiple context managers, and handle dynamic context management. Additionally, the article showcases how ExitStack gracefully handles exceptions when working with multiple context managers, ensuring proper resource cleanup.

2023-03-31

Concurrency in Python

This article explores concurrency in Python, focusing on techniques to execute multiple tasks simultaneously for better performance. It discusses the threading, multiprocessing, and concurrent.futures modules, covering thread creation, synchronization, and communication. It also addresses using processes for parallelism, inter-process communication, and high-level interfaces for executing callables asynchronously. Finally, the article provides guidance on choosing the appropriate concurrency model based on task requirements and performance considerations.

2023-03-31

Manage temporary files and directories with tempfile

This article explores the Python tempfile module, which allows you to create and manage temporary files and directories securely and efficiently. The article covers the creation of temporary files using tempfile.TemporaryFile(), temporary directories with tempfile.TemporaryDirectory(), named temporary files through tempfile.NamedTemporaryFile(), and spooled temporary files via tempfile.SpooledTemporaryFile().

2023-03-31

defaultdict in Python

This article explores the defaultdict, a specialized dictionary in Python's collections module that simplifies handling missing keys. It delves into understanding default factories, using built-in functions as default factories, and creating custom default factories. The article demonstrates how to create and import defaultdicts, and discusses common use cases such as counting elements, grouping elements, creating nested defaultdicts, and combining defaultdicts. Finally, the article compares defaultdict to the built-in dict class, highlighting key differences and performance implications.

2023-03-31

Namedtuples in Python

This article delves into the world of namedtuples in Python, exploring their usage, methods, and real-life applications. Discover the advantages of namedtuples over regular classes, learn how to create and manipulate them, and understand how to use them effectively in a variety of scenarios.

2023-03-31

OrderedDict in Python

This article delves into the essentials of Python's OrderedDict, a dictionary subclass that maintains element order. Explore how to create, access, update, delete, reverse, sort, and merge OrderedDicts, and discover practical examples in parsing configuration files, working with JSON objects, and implementing LRU caches.

2023-03-31

TypeVar in Python

This article dives into the world of TypeVar, a powerful feature in Python's typing module that enables the creation of generic functions and classes while maintaining type safety. Discover how to define TypeVar with constraints and boundaries, utilize TypeVar for flexible type constraints, and explore practical examples of TypeVar usage in generic functions, such as finding an item's index in a list and merging two dictionaries.

2023-03-31

Protocol in Python

This article introduces Protocol in Python, a powerful feature that enables more flexible and expressive type hinting. Learn how to create custom protocols and understand their differences from abstract base classes. The article also demonstrates the practical use of Protocols in a scikit-learn example.

2023-03-13

each_item Flag in Pydantic Validators

This article delves into the each_item flag in Pydantic validators, explaining its functionality and demonstrating its implementation with examples.

2023-03-12

Pre-Validation with Pydantic

This article delves into the power of pre-validation in Pydantic, explaining how to use the pre=True flag in the @validator decorator for data preprocessing before standard validation. Discover examples and techniques to create custom pre-validation functions for more robust and reliable code.

2023-03-12

Converting Between Pydantic Models and Dictionaries

This article explains how to convert Pydantic models to dictionaries and vice versa.

2023-03-12

Sklearn Algorithm Cheat Sheet

This article presents a useful cheat sheet provided by Sklearn for selecting the appropriate machine learning model or algorithm based on your data type and problem.

2023-03-11

Command-Line Arguments with argparse in Python

This article explains the basics of the argparse module in Python, which allows programmers to easily create command-line interfaces for their Python programs. It covers creating a parser object, adding arguments, parsing command-line arguments, and running a Python script. The article also discusses advanced usage options, such as specifying argument types, setting default values, specifying argument help messages, grouping arguments, and controlling the output of help messages. Additionally, it covers how to handle errors and exceptions that can occur when parsing command-line arguments, including handling invalid argument values, missing arguments, and conflicting arguments.

2023-03-11

Subprocess in Python

This article provides an in-depth guide to Python's subprocess module, which is used to manage subprocesses, execute external commands, and interact with other applications. Learn about the run() function for running external commands, how to handle errors and exceptions, and capture command output. Additionally, explore advanced subprocess management using the Popen class, methods for controlling processes, and techniques for setting timeouts and terminating processes.

2023-03-11

How to Validate That Either One of Two Optional Fields Is Not None in Pydantic

This article demonstrates the use of Pydantic to validate that at least one of two optional fields in a data model is not None. The article covers creating an Example model with two optional fields, using the validator decorator to define custom validation logic, and testing the validation with different field values.

2023-03-10

args and kwargs in Python

This article discusses *args and **kwargs in Python, which are commonly used to pass a variable number of arguments to a function. The *args syntax is used to pass a variable number of non-keyword arguments to a function, while the **kwargs syntax is used to pass a variable number of keyword arguments. By using *args and **kwargs, you can create flexible functions that can handle different numbers of arguments or keyword arguments. The article includes examples that demonstrate how to use *args and kwargs in Python, and how to pass a dictionary to a function that uses kwargs.

2023-03-10

Python Docstring Style

Docstrings in Python are string literals used to provide documentation about the purpose, behavior, and usage of the code that follows it. The contents of a docstring are typically written in plain English and can be a single-line or multi-line string. Python's docstring convention is defined in PEP 257, which provides guidelines for how to write and format docstrings. Docstrings are essential for readability, maintenance, and code generation. There are several formats for writing docstrings in Python, including reStructuredText (reST) format, Google-style format and NumPy format.

2023-03-10

PYTHONPATH Environment Variable

This article explains what the PYTHONPATH environment variable is in Python and how it works. PYTHONPATH is a list of directories that the Python interpreter searches when looking for Python modules and packages to import. Setting PYTHONPATH is useful when working with custom modules that you have created and want to import in your code. The article also provides troubleshooting tips for common PYTHONPATH issues, such as checking the directory structure, ensuring module names are spelled correctly, and using virtual environments.

2023-03-10

Handling Undeclared Variables in Jinja

This article delves into Jinja2's jinja2.meta.find_undeclared_variables function, which helps developers identify undeclared variables in their templates, preventing potential issues. Explore examples on how to utilize this function and create a template setter class to ensure a smooth templating experience.

Jinja

2023-03-10

Jinja Templating Engine in Python

Jinja is a popular templating engine for Python which is used to generate dynamic web pages, HTML, XML, and other markup languages. It provides an easy and efficient way to separate the presentation layer from the business logic layer. This article covers the setup of Jinja in Python and explains its syntax for variables and loops, including loop-specific variables such as index, revindex, first, last, and length. Additionally, it demonstrates how to use template inheritance, macros, filters, and loop control in Jinja.

Jinja

2023-03-10

Logging in Python

This article covers basic and advanced logging concepts, such as creating a logger object, logging messages, setting logging levels, formatting log messages, using handlers to output logs, and rotating log files.

2023-03-10

Loguru

This article provides a comprehensive guide to Loguru, a powerful logging library for Python that simplifies the logging process and makes it easier for developers to track the behavior of their applications.

2023-03-10

Pathlib Module for Simplifying File System Operations

This article is an introduction to pathlib, a module in Python's standard library that simplifies file system operations. The article provides an overview of pathlib and compares it with the older os and os.path modules. It explains how to create and work with paths, access individual parts of a path, and perform common file system operations. The article also covers some of the most commonly used methods of the Path object in pathlib and provides examples of how to use them, including exists(), is_file(), is_dir(), name, parent, absolute(), glob(), and cwd(). Finally, the article explains how pathlib provides a way to convert between absolute paths and relative paths using the resolve() and relative_to() methods of the Path object.

2023-03-10

Regular Expressions with Python's re Module

The re module is a built-in library in Python that provides support for regular expressions. Regular expressions, also known as regex or regexp, are sequences of characters that define a search pattern. This article introduces the re module and explores its various functions and methods for working with regular expressions. The article also provides example code and output for each method and demonstrates how regular expressions can be used to perform complex text-processing tasks.

2023-03-10

Progress Bars with TQDM in Python

This article provides an introduction to TQDM, a popular Python library that enables developers to add progress bars to their code. The article explains how to install TQDM using pip or conda, and covers basic usage examples such as using TQDM with iterables and loops, customizing the appearance of the progress bar, and using nested progress bars. Additionally, the article covers advanced usage examples such as using TQDM with Pandas, Multiprocessing, Jupyter Notebooks, and AsyncIO.

2023-03-10

Inheriting Pydantic Models

This article explores the power of inheritance in Pydantic models. Learn how to create a base model and extend it with additional fields, add validators to enforce custom rules, and override fields or methods. Discover practical examples of Pydantic inheritance in user authentication, product catalogs, and blog post models to streamline your code and improve maintainability.

2023-03-10

Pydantic for Data Validation

Pydantic is a Python library that simplifies data validation and settings management, providing a simple way to define data models, validate input data, and manage application settings while maintaining strict typing and error handling. Pydantic works seamlessly with web frameworks like FastAPI and Starlette, but it can be used in any Python application. This article describes the steps to install Pydantic, create Pydantic models, and use them for data validation, both for input data and output data. Pydantic offers a range of built-in validators, such as MinValue, MaxValue, and regex, and can validate input data by creating an instance of the Pydantic model and validating the input data against it. Pydantic can also be used to validate output data by converting the list of user data dictionaries to a list of User instances and validating the output data.

2023-03-10

Scikit-learn Pipeline for Machine Learning

Scikit-learn Pipeline is a framework that streamlines the data preprocessing and model building stages of machine learning. It allows users to chain together multiple data processing and feature extraction techniques into a single pipeline, facilitating testing and experimentation while avoiding data leakage. Using Scikit-learn Pipeline saves time and resources, improves code readability, and improves the performance of machine learning models. Building a Scikit-learn Pipeline involves preprocessing data using Scikit-learn transformers, creating a Pipeline object, fitting and transforming data with the Pipeline, and tuning hyperparameters using GridSearchCV.

2023-03-10

Functional Programming

Functional programming is a programming paradigm that emphasizes the use of pure functions, immutable data, and higher-order functions. In this article, I explore the key concepts of functional programming, such as pure functions, immutable data, first-class and higher-order functions, recursion, and lazy evaluation.

Software Development

2023-03-05

How to Make a Custom BERT Model

This article explains how to create your own BERT model for natural language processing (NLP) tasks, using PyTorch and Hugging Face Transformers library.

2023-03-05

How to Incorporate Tabular Data with BERT

This article introduces how to incorporate tabular data (numerical and categorical values) into a BERT model and train it using the Hugging Face Trainer. Step-by-step PyTorch code with explanations for each step will be provided.

2023-03-05

Understanding the Last Hidden State in BERT Model

The last hidden state in BERT is an important component of the model that captures the contextual information of the input text. This article explores the significance of the last hidden state in BERT and how it is calculated.

2023-03-05

Understanding Logits in BERT

Logits are a crucial part of the BERT algorithm, which powers many NLP applications. This article explains what logits are and how they work in BERT.

2023-03-03

Integrating Black with GitHub Actions

This article explians how to integrate Black, a Python code formatter, with GitHub Actions.

GitHub

GitHub Actions

2023-03-03

Setting Up Python 3.10 in GitHub Actions

This article shows you the process of setting up Python 3.10 in GitHub Actions while addressing and resolving version compatibility issues caused by YAML's handling of floating-point numbers.

GitHub

GitHub Actions

2023-02-27

Adding Specific Version of Library in Poetry

This article explains the process of adding a specific version of a library in Poetry, a Python package manager.

2023-02-25

Demystifying the call Method in Python

This article delves into the powerful and flexible call method in Python, which allows instances of a class to be called as if they were functions.

2023-02-25

__post_init__ in Dataclasses

This article dives into the use of the __post_init__ method in Python Dataclasses, discussing its significance, basic implementation, and applications in data validation, transformation, and handling optional parameters for class attribute customization.

2023-02-24

What is init.py

This article explains what init.py is in Python.

2023-02-24

Decorators in Python

This article describes how to use decorators to modify the behavior of functions and methods in Python.

2023-02-24

Walrus Operator in Python

The Walrus Operator is a new feature in Python 3.8 that assigns a value to a variable within an expression. It improves code readability, efficiency and flexibility. It simplifies code by making it more concise and eliminating the need for temporary variables in some cases. Examples of how to use it include file handling, data validation, and API requests.

2023-02-24

UUID

UUID (Universally Unique Identifier) is a 128-bit unique identifier used in computer systems to identify resources. This article will explore about UUID, including its purpose, structure, and how it's used in software development.

Software Development

2023-02-24

Black

This article introduces Black, the Uncompromising Code Formatter for Python.

2023-02-24

Understanding Abstract Class in Python

In Python, abstract classes provide a way to define a base class that cannot be instantiated on its own, but can be subclassed to create concrete classes. This article will explain what abstract classes are, how to create them in Python, and how they can be used in your code.

2023-02-24

Python Classes

This article explains about Python classes, one of the most powerful features of the language.

2023-02-24

Dataclasses in Python

Dataclasses are a powerful tool in Python for simplifying the process of creating classes with attributes. This article covers information about dataclasses, including their syntax, features.

2023-02-24

Understanding Property in Python

Property is a built-in feature in Python that allows you to manage attributes and methods of a class. This article explains how to use property in Python.

2023-02-24

Classmethod and Staticmethod in Python

This article covers how to use each type of method and provides examples to help you understand when to use them.

2023-02-24

Poetry

Poetry is a comprehensive package manager for Python that simplifies the management of dependencies and package installation. This tool helps streamline the workflow of managing Python projects and makes it easy for developers to manage their packages effectively. With Poetry, developers can focus on building high-quality applications instead of worrying about package management.

2023-02-24

Type Annotation in Python

Type annotations in Python help developers catch errors early, improve code readability, and make it easier to collaborate on larger projects. This article introduces basic usage of type annotations.

2023-02-17

RNN

This article explains about RNN.

NLP

2023-02-17

torch.stack in PyTorch

This article provides an overview of the torch.stack function in PyTorch and its syntax and parameters. It also covers the usage of the dim parameter in torch.stack with examples of how to change the parameter. Additionally, the article compares torch.stack with other PyTorch functions, such as torch.cat and torch.chunk, and discusses their differences with examples. Finally, the article addresses performance and memory considerations when using torch.stack in deep learning models, including the use of the in-place operation and the out parameter to reduce memory usage.

PyTorch

2023-02-17

pytest's conftest.py

This article explains about pytest's conftest.py.

2023-02-17

pytest

This article explains about pytest.

2023-02-17

unittest mock

This article explains about mock in Python unittest.

2023-02-17

unittest patch

This article explains about patch in Python unittest.

2023-02-17

unittest

This article explains about unittest in Python.

2023-02-11

Creating Multi-Page Apps with Streamlit

This article explains Streamlit's Multi-page feature, introduced in version 1.10.0.

Streamlit

2023-02-10

Streamlit

This article introduces Streamlit, a Python framework that makes it easy to develop data science and machine learning applications without the need for knowledge in HTML or CSS, allowing data visualization to be done effortlessly. It covers how to install Streamlit, execute Streamlit apps, use text and widgets, create sidebars, display data tables, visualize data with graphs, and integrate geographic data using maps.

Streamlit

2023-02-04

Hugging Face Trainer Class for Efficient Transformer Training

This article provides a guide to the Hugging Face Trainer class, covering its components, customization options, and practical use cases. Discover how the Trainer class simplifies training and fine-tuning transformer models, and explore examples for creating custom training loops and dynamically instantiating new models.

2023-02-03

Word Embeddings

This article explains about word embeddings.

NLP

2023-02-03

Hugging Face Datasets

This article explains about Hugging Face Datasets.

2023-02-03

Hugging Face Transformers：Fine-tune

This article describes the fine tuning of Hugging Face Transformers.

2023-02-03

Hugging Face Transformers：Model

This article describes Hugging Face Transformers Model.

2023-02-03

Hugging Face Transformers：Overview

This article explains about nn overview of Hugging Face Transformers.

2023-02-03

Hugging Face Transformers：Pipeline

This article describes the Pipeline of Hugging Face Transformers.

2023-02-03

Hugging Face Transformers：Tokenizer

This article describes Hugging Face Transformers Tokenizer.

2023-01-27

Text Classification with DistilBERT

This article performs text classification with DistilBERT.

2023-01-10

os.path.join()

This article explains the os.path.join() method in Python, allowing you to effortlessly combine paths regardless of the operating system.

2022-12-28

Gamma regression

This article explains about Gamma regression.

2022-12-23

AIC

This article explains about the AIC.

2022-12-23

Generalized linear model

This article explains about the generalized linear model.

2022-12-23

Logistic regression

This article explains about logistic regression.

2022-12-23

Statistical model

This article explains about a statistical model.

2022-12-18

Replace Method in Pandas DataFrame

This article explains the replace method in Pandas DataFrame.

2022-12-17

How to Rename Columns in Pandas DataFrame

This article introduces two primary methods for renaming columns in a DataFrame.

2022-12-16

Skewness and kurtosis of probability distribution

This article explains about skewness and kurtosis of probability distribution.

2022-12-16

Chi-square distribution

This article explains about the chi-squared distribution.

2022-12-16

F-distribution

This article explains about the F-distribution.

2022-12-16

Gumbel Distribution

The Gumbel distribution is an extreme value distribution used to analyze maximum or minimum values of independent random variables. It has key applications in hydrology, engineering, finance, and machine learning. With its mathematical foundations in probability density function (PDF), cumulative distribution function (CDF), moments, and characteristic functions, the Gumbel distribution allows for precise estimation of location and scale parameters. This article delves into these applications and provides a Python code example for drawing a Gumbel distribution.

2022-12-16

t-distribution

This article explains about the t-distribution.

2022-12-15

Pandas DataFrame Normalization

This article explains how to conduct data normalization in Pandas DataFrame using Scikit-learn.

2022-12-09

Dirichlet distribution

This article explains about Dirichlet distribution.

2022-12-09

Categorical distribution

This article explains about the categorical distribution.

2022-12-09

Multinomial distribution

This article explains about the multinomial distribution.

2022-12-04

PuLP

This article introduces PuLP, a popular Python library for linear programming and mixed-integer linear programming problems.

Optimization Problem

2022-12-04

Python-MIP

This article introduces Python-MIP, a Python library designed for solving complex optimization problems.

Optimization Problem

2022-12-01

Beta distribution

This article explains about beta distribution

2022-12-01

Exponential distribution

This article explains about the exponential distribution.

2022-12-01

Gamma distribution

This article explains about the gamma distribution.

2022-12-01

Normal distribution

This article explains about the normal distribution.

2022-12-01

Bernoulli distribution

This article explains about the Bernoulli distribution.

2022-12-01

Binomial distribution

This article explains about the binomial distribution.

2022-12-01

Geometric distribution

This article explains about the geometric distribution.

2022-12-01

Poisson distribution

This article explains about the Poisson distribution.

2022-11-24

Support Vector Regression

This article explains Support Vector Regression (SVR), a powerful and versatile machine learning algorithm for predicting continuous target variables.

2022-11-23

Polynomial Regression

This article covers Polynomial Regression, an extension of Linear Regression that models complex nonlinear relationships between variables.

2022-11-22

K-Nearest Neighbors (KNN) Regression

This article covers KNN Regression, a non-parametric supervised learning algorithm for regression tasks.

2022-11-22

Ridge Regression

This article explains Ridge Regression, a regularization technique used in Linear Regression models to address the issue of multicollinearity. It describes the mathematical foundation of Ridge Regression, including the cost function and L2 penalty term.

2022-11-21

Lasso Regression

This article covers the fundamentals of Lasso Regression, including its need for regularization and mathematical foundations.

2022-11-20

Linear Regression

This article covers the basics of linear regression, including its definition, assumptions, and types.

2022-11-20

What is regression analysis

This article explains about regression analysis.

Tabular

2022-11-18

Converting Pandas DataFrame to Dict

This article explores how to convert a Pandas DataFrame to a dictionary using the to_dict() method.

2022-11-17

Time Series Data with Pandas

This article explains how to handling and analyzing time series data using Pandas in Python. Dive into working with dates and times, time series resampling techniques, and the power of rolling window functions with practical examples and code snippets.

2022-11-16

Data Filtering Techniques in Pandas

This article explores various techniques for filtering data in Pandas, a crucial operation in data analysis.

2022-11-15

Merging, Concatenating, and Joining DataFrames in Pandas

This article delves into the essential techniques for combining DataFrames in the pandas library - merging, concatenating, and joining.

2022-11-15

Techniques for Enhancing Pandas Performance and Efficiency

This article explains various techniques for optimizing Pandas workflows, covering efficient data loading, memory management, vectorization, and parallel processing.

2022-11-14

Multi-Level Indexing in Pandas

This article delves into the concept of multi-level indexing in Pandas, demonstrating its usefulness in organizing complex, multi-dimensional data sets.

2022-11-13

Plotting Histograms with Pandas

This article introduces how to plot histograms using Pandas.

2022-11-13

Importing and Exporting Data with Pandas

This article provides a guide on importing and exporting data in various formats using Pandas. Discover techniques for handling CSV, Excel, JSON, SQL, web APIs, and more, along with exporting your data to different file formats.

2022-11-12

Aggregating and Grouping Data with pandas

This article explains how to perform data aggregation and grouping using Pandas in Python. It covers GroupBy objects and their creation, selection of columns and rows, and iteration over groups. The article also explores built-in and custom aggregate functions, and how to apply multiple aggregate functions at once.

2022-11-11

Indexing and Slicing in Pandas DataFrames

This article offers a guide to indexing and slicing techniques in Pandas DataFrames, covering label-based and position-based indexing, Boolean arrays, hierarchical indexing, and row and column slicing.

2022-11-10

Pandas Dataframe to Markdown

This article offers a tutorial on converting a Pandas dataframe to Markdown format using the built-in to_markdown() function. The article includes creating a sample dataframe, exporting it to Markdown, and utilizing various options available within the to_markdown() function to customize the appearance of the exported table.

2022-11-10

Pandas Overview

This article provides an overview of Pandas, a Python library for data analysis and manipulation. It covers the key features of Pandas, including its data structures, tools for cleaning and transforming data, and integration with other Python libraries.

2022-10-20

Support Vector Machine (SVM)

This article covers the Support Vector Machine (SVM) algorithm, including its basic concepts and terminology, the mathematics behind it, and its implementation with the Iris dataset.

2022-10-16

How to use AWS Cognito

This article explains how to use AWS Cognito.

2022-10-03

Processing Multipart Form Data with AWS Chalice

This article provides a step-by-step guide on how to receive and process multipart/form-data using AWS Chalice. It covers creating a route for form submission, using the CGI library to parse multipart data, extracting form data and files, and handling errors and validation.

AWS

AWS Chalice

2022-10-02

AWS Chalice

This article describes AWS Chalice.

AWS

AWS Chalice

2022-10-02

Hierarchical Clustering

This article covers the basics of Hierarchical Clustering, a family of unsupervised machine learning algorithms that build a hierarchy of clusters. It includes an overview of agglomerative and divisive approaches, as well as their respective bisection and linkage methods.

2022-10-02

K-Means Clustering

This article discusses K-Means Clustering, a popular unsupervised machine learning technique. It covers the K-Means Algorithm's objective function and steps, choosing the right number of clusters (K) using the Elbow Method, Silhouette Method, and Gap Statistic, and implementing K-Means in Python with the Iris dataset.

2022-08-10

requests

This article explains the requests module in Python for making HTTP requests.

2022-08-04

Feature importance in Decision Tree

This article explores the concept of feature importance in decision trees and its various methods such as Gini impurity, information gain, and gain ratio. It discusses how these methods aid in selecting the most significant variables from a dataset and simplifying complex data. The article also demonstrates how to visualize feature importance in both regression and classification cases using Python.

2022-08-03

Gradient Boosting Decision Trees (GBDT)

This article demystifies Gradient Boosting Decision Trees (GBDT), a powerful ensemble learning method, by diving into its algorithm, comparing it to Random Forests, and providing Python implementation examples.

2022-08-02

Random Forests with the Titanic Dataset

This article guides you through implementing a random forest classifier on the Titanic dataset. Discover how to prepare the dataset, build the model using scikit-learn, and evaluate its performance. Additionally, learn to visualize feature importance to identify significant predictors of survival.

2022-07-10

Underscore Positions in Python

This article explains the different use-cases of underscores in Python.

2022-07-06

Configuring CORS in FastAPI

This article explains how to configure CORS middleware in FastAPI, from basic configurations suitable for testing to advanced settings for production environments, ensuring secure interaction with different services.

2022-07-06

Customizing Default 422 Error in FastAPI

This article explains how to customize the default 422 error in FastAPI.

2022-07-05

API documentation in FastAPI

This article explains the built-in support of FastAPI for automatic API documentation using Swagger UI and ReDoc.

2022-07-04

FastAPI

This article introduces an overview of FastAPI, a high-performance web framework for building APIs with Python. It explains FastAPI's core features, installation process, creating a FastAPI application, and how FastAPI handles data through request bodies, query parameters, and path parameters.

2022-07-02

Permutation Importance

This article covers the concept of Permutation Importance and its methodology for calculating feature importance in machine learning models.

2022-04-02

Enum Operations

This article explains how to extract keys and values from an Enum, convert an Enum to a dictionary, and retrieve keys from values.

2022-04-02

Extracting List of Keys from TypedDict Class

This article explains how to utilize the annotations attribute in Python's TypedDict class to extract a list of keys.

2022-04-02

TypedDict in Python

This article provides an overview of TypedDict in Python, a feature introduced in Python 3.8 for specifying type hints for dictionaries.

2022-04-01

Literal Types in Python

This article introduces typing.Literal in Python, introduced in Python 3.8, to specify types constrained to specific values.

2022-03-30

Enum in Python

This article explains the enumerations, or enums, in Python.

Descriptive Statistics

Differential Equation

Dimensionality Reduction

Discrete Choice Model

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Hypothesis Testing

Inferential Statistics