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Erdogan Taskesen

Data Scientist in Statistical Machine Learning

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Blogs

Overview

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  1. Taskesen E, How to Find the Best Theoretical Distribution for Your Data. Medium, Feb. 2023

  2. Taskesen E, D3Blocks: The Python Library to Create Interactive and Standalone D3js Charts. Medium, Sep. 2022

  3. Taskesen E, A Step-by-Step Guide in detecting causal relationships using Bayesian Structure Learning in Python. Medium, Sep. 2021

2023

  1. Taskesen E, From Clusters To Insights; The Next Step. Medium, May. 2023 Learn how to quantitatively detect which features drive the formation of the clusters.

  2. Taskesen E, [From Data to Clusters: When is Your Clustering Good Enough?*](https://towardsdatascience.com/from-data-to-clusters-when-is-your-clustering-good-enough-5895440a978a). Medium, April. 2023 *Hidden gems can be found using clustering approaches but you need the right clustering method and evaluation approach to make sensible clusters. Learn how to find them in four steps.

  3. Taskesen E, Outlier Detection Using Principal Component Analysis and Hotelling’s T2 and SPE/DmodX Methods. Medium, March. 2023 Learn how to detect outliers using PCA.

  4. Taskesen E, Outlier Detection Using Distribution Fitting in Univariate Datasets. Medium, Feb. 2023 Learn how to detect outliers using Probability Density Functions for fast and lightweight models and explainable results.

  5. Taskesen E, How to Find the Best Theoretical Distribution for Your Data. Medium, Feb. 2023 Knowing the underlying data distribution is an essential step for data modeling and has many applications, such as anomaly detection, synthetic data creation, and data compression.

2022

  1. Taskesen E, The Starters Guide to Release your Python Package in PyPi. Medium, Dec. 2022 A step-by-step guide to effectively release your Python package in the Python Package Index (PyPI) to pip install it

  2. Taskesen E, Get the Most Out of Your Scatterplot by Making It Interactive Using D3js and Python. Medium, Nov. 2022 Scatterplots are extremely useful for visualizing relationships between two sets of numerical variables. It is even more insightful when it is interactive with zooming and brushing capabilities.

  3. Taskesen E, Hands-on Guide to Create beautiful Sankey Charts in d3js with Python. Medium, Oct. 2022 The Sankey chart is a great way to discover the most prominent contributions just by looking at how individual items flow across states.

  4. Taskesen E, How to Create Storytelling Moving Bubbles Charts in d3js with Python. Medium, Sep. 2022 The MovingBubble chart is one of those mindblowing charts to look at. It is a great way to conceptually better understand how individual items are distributed across states and move across time. Learn how to create them with Python and your own data set.

  5. Taskesen E, A Hands-on Guide To Create Explainable Gradient Boosting Classification models using Bayesian Hyperparameter Optimization. Medium, Sep. 2022 Boosted decision tree algorithms, such as XGBoost, CatBoost, and LightBoost are popular methods for the classification task. Learn how to split the data, optimize hyperparameters, prevent overtraining, select the best-performing model, and create explainable results.

  6. Taskesen E, D3Blocks: The Python Library to Create Interactive and Standalone D3js Charts. Medium, Sep. 2022 Create interactive, stand-alone, and visually attractive charts that are built on the graphics of d3 javascript (d3js) but configurable with Python.

  7. Taskesen E, A Guide to Find the Best Boosting Model using Bayesian Hyperparameter Tuning but without Overfitting. Medium, Aug. 2022 With boosted decision tree algorithms, such as XGBoost, CatBoost, and LightBoost you may outperform other models but overfitting is a real danger. Learn how to split the data, optimize hyperparameters, and find the best-performing model without overtraining it using the HGBoost library.

  8. Taskesen E, Quantitative comparisons between t-SNE, UMAP, PCA, and Other Mappings. Medium, May. 2022 Low dimensional projections are useful to understand the relationships between samples better but how similar is one map to another?

  9. Taskesen E, What are PCA loadings and how to effectively use Biplots?. Medium, April. 2022 A practical guide for getting the most out of Principal Component Analysis.

  10. Taskesen E, Creating beautiful stand-alone interactive D3 charts with Python. Medium, Feb. 2022 With application to D3 force-directed network graphs.

2021

  1. Taskesen E, Detection of Duplicate Images Using Image Hash Functions. Medium, Jan. 2021 Automate the search for (near-)identical photos with the Python library undouble.

  2. Taskesen E, A step-by-step guide for clustering images. Medium, Dec. 2021 For the detection and exploration of image clusters. Learn how to carefully pre-process images, utilize well-known feature extraction approaches, and evaluate the goodness of the clustering. A theoretical background followed by a hands-on tutorial.

  3. Taskesen E, A step-by-step guide in designing knowledge-driven models using Bayesian theorem. Medium, Sep. 2021 In case you don’t have data but there is expert knowledge. A starter’s guide to convert knowledge into computer-aided models.

  4. Taskesen E, A Step-by-Step Guide in detecting causal relationships using Bayesian Structure Learning in Python. Medium, Sep. 2021 The starter’s guide to effectively determine causalities across variables.

  5. Taskesen E, Explore and understand your data with a network of significant associations. Medium, Aug. 2020 Explore to understand your data can make the difference between an unsuccessful project or finishing successfully!