time-series, trend, seasonality, pandas

Data Science

How to Remove Trend & Seasonality from Time-Series Data in Python?

Tutorial provides a brief guide to detect stationarity (absence of trend and seasonality) in time series data. After checking for stationarity, the tutorial explains various ways to remove trends and seasonality from time series to make them stationary.

Sunny Solanki

Aug 27, 2022 ~30 mins

configuration-files-parser

Python

configparser - Simple Guide to Create and Parse Configuration Files in Python

A simple guide on how to use Python library "configparser" to create, read, parse, and modify config files (Generally ending with .conf, .ini, etc) in Python. Apart from basics, tutorial covers how to add/remove sections, create configuration from Python dictionary or strings and specify section details as formatted Python strings. All topics are covered with simple and easy-to-understand examples.

Sunny Solanki

Aug 26, 2022 ~30 mins

events, scheduler, sched

Python

sched - How to Schedule Events in Python?

A simple guide on how to use Python module "sched" to schedule events. It let us run a function/callable in future. We can run a task at a specified time in future or after a specified time interval has passed. Apart from scheduling tasks, we have covered how to cancel tasks, assign priorities, create a scheduler with different time units, etc.

Sunny Solanki

Aug 25, 2022 ~15 mins

metrics, visualizations

Machine Learning

Scikit-Plot: Visualize ML Model Performance Evaluation Metrics

Tutorial explains how to use Python library scikit-plot to create data visualizations of various ML metrics. It is designed on top of matplotlib and provides charts for most commonly used ML metrics like confusion matrix, ROC AUC curve, Precision-Recall Curve, Elbow Method, Silhouette Analysis, Feature Importance, PCA Projection, etc.

Sunny Solanki

Aug 24, 2022 ~30 mins

xgboost

Machine Learning

XGBoost - An In-Depth Guide [Python API]

An in-depth guide on how to use Python ML library XGBoost which provides an implementation of gradient boosting on decision trees algorithm. Tutorial covers majority of features of library with simple and easy-to-understand examples. Apart from training models & making predictions, topics like cross-validation, saving & loading models, early stopping training to prevent overfitting, creating custom loss function & evaluation metrics, etc are covered in detail.

Sunny Solanki

Aug 21, 2022 ~60 mins

catboost

Machine Learning

CatBoost - An In-Depth Guide [Python API]

An in-depth guide on how to use Python ML library catboost which provides an implementation of gradient boosting on decision trees algorithm. Tutorial covers majority of features of library with simple and easy-to-understand examples. Apart from training models & making predictions, topics like hyperparameters tuning, cross-validation, saving & loading models, plotting training loss/metric values, early stopping training to prevent overfitting, creating custom loss function & evaluation metrics, etc are covered in detail.

Sunny Solanki

Aug 20, 2022 ~60 mins

lightgbm, boosting-decision-trees

Machine Learning

LightGBM - An In-Depth Guide [Python API]

An in-depth guide on how to use Python ML library LightGBM which provides an implementation of gradient boosting on decision trees algorithm. Tutorial covers majority of features of library with simple and easy-to-understand examples. Apart from training models & making predictions, topics like cross-validation, saving & loading models, plotting features importances, early stopping training to prevent overfitting, creating custom loss function & evaluation metrics, etc are covered in detail.

Sunny Solanki

Aug 19, 2022 ~30 mins

scikit-learn, incremental-learning

Machine Learning

Scikit-Learn - Incremental Learning for Large Datasets

Tutorial explains how to use scikit-learn models/estimators with large datasets that do not fit into main memory of the computer. Majority of sklearn estimators can work with datasets that fit into main memory only. But there are few that can work with data in batches. All these models provide "partial_fit()" method that can be called more than once to update model weights. We have explained these kinds of models for solving ML tasks regression, classification, clustering, dimensionality reduction, and preprocessing.

Sunny Solanki

Aug 18, 2022 ~30 mins

python, parallel-processing

Python

joblib - Parallel Processing in Python

A detailed guide on how to use Python library joblib for parallel computing in Python. Tutorial explains how to submit tasks to joblib pool and then retrieve results. It even explains how to use various parallel computing backend like loky, threading, multiprocessing, dask, etc.

Sunny Solanki

Aug 17, 2022 ~30 mins

logging-configuration

Python

logging.config - Simple Guide to Configure Loggers from Dictionary and Config Files in Python

An in-depth guide to configure loggers from dictionary and config files. Python module "logging.config" provides us with various methods to create loggers from configuration details available through dictionary and config files. The tutorial explains methods (dictConfig() & fileConfig()) of module with simple examples.

Sunny Solanki