YouTube Video Resolution Downgrade Classification
Feature engineering, selection, visualization, model development, feature explainability.
Mail
LinkedIn
Github
Google Scholar
Medium
Kaggle
Experienced Data Scientist with a Ph.D. in Data Science, blending 6+ years of research expertise with 4+ years in business. Proven track record in analytics, programming, and modeling. Published 13 papers, executed successful projects, and showcased skills through blogging and GitHub. Dedicated to ongoing learning and staying abreast of industry trends.
Skills
- Python
- MySQL
- Data Mining
- Data Analysis
- Data Visualization
- Machine Learning
Posts by Category
- Data Processing 11
- Machine Learning 9
- Data Visualization 7
- Feature Engineering 6
- Feature Selection 4
- Time Series 4
- Exploratory Data Analysis 3
- Clustering 3
- Deployment 3
- Software Development 2
- Web Development 2
- API 2
- MLOps 1
Data Processing
Comparing the Speed and Filesize of to_csv(), np.save(), to_hdf(), and to_pickle() Functions
Achieve ×66 speedup read time, ×25 write time, and ×0.39 filesize on your daily I/O operations.
Topic Modeling on my Watched Movies
Merge IMDb with Wikipedia movie data and build, evaluate and visualize topics using LDA.
Outlier Detection — Theory, Visualizations, and Code
Five Algorithms to rule them all, Five Algorithms to find them, Five Algorithms to bring them all and in the darkness bind them.
Feature Engineering with Dates
Given a single feature that contains date and time, I created a function that generates 23 features.
Battle of the Cities
Let’s suppose that you want to relocate. You don’t know where, yet. You have enough money in order to relocate and then search for a job in your new city. Yo...
EDA is Fun!
A very popular Kaggle kernel (110.000+ views, 1400+ upvotes, 200+ comments) that won me the 1st prize, as the most informative and upvoted kernel for the pop...
Normalization and Standardization
Why to use normalization and standardization? Which are their differences? When to use them and when to avoid?
Pandas Tutorial
Are you starting with Data Science? Pandas is the first thing you will need. Learn how to create, add, remove, rename, read, select, filter, sort, group, man...
Encoding Categorical Features
Why to encode categorical features? 6 ways to encode different kinds of data.
Emotion Detection on Movie Reviews
The objective of this project is the Emotion Analysis of sentences that are comming from movie reviews, using Machine Learning. An attempt will be made to co...
Machine Learning
MLOps Lifecycle with MLFlow, Airflow, Amazon S3, PostgreSQL
Full Machine Learning Lifecycle using Airflow, MLflow, and AWS S3.
Ensemble Feature Selection for Machine Learning
Investigate if ensemble feature selection methods are superior of individual in machine learning classification problems.
YouTube Video Resolution Downgrade Classification
Feature engineering, selection, visualization, model development, feature explainability.
Feature Selection for Machine Learning: 3 Categories and 12 Methods
Learn basic theory about the 3 types of feature selection in machine learning namely filters, wrappers, and embedders.
Forecasting Wars: Classical Forecasting Methods vs Machine Learning
6 Classical Forecasting Methods are Compared with 3 Machine Learning Algorithms using code in Python on sales data from kaggle.
Clustering Theory
Clustering is the most common form of unsupervised learning. It is the process that involves the grouping of data points into classes of similar objects.
Battle of the Cities
Let’s suppose that you want to relocate. You don’t know where, yet. You have enough money in order to relocate and then search for a job in your new city. Yo...
EDA is Fun!
A very popular Kaggle kernel (110.000+ views, 1400+ upvotes, 200+ comments) that won me the 1st prize, as the most informative and upvoted kernel for the pop...
Emotion Detection on Movie Reviews
The objective of this project is the Emotion Analysis of sentences that are comming from movie reviews, using Machine Learning. An attempt will be made to co...
Data Visualization
Topic Modeling on my Watched Movies
Merge IMDb with Wikipedia movie data and build, evaluate and visualize topics using LDA.
Statify
An app where anyone can join with Spotify credentials and see a lot of cool statistics about his/her profile.
Outlier Detection — Theory, Visualizations, and Code
Five Algorithms to rule them all, Five Algorithms to find them, Five Algorithms to bring them all and in the darkness bind them.
Time Series Analysis with Theory, Plots, and Code Part 2
Stationarity. ADF stationarity Test. Autocorrelation, Lag Scatter plot. Moving Average, Double and Triple Exponential Smoothing.
Time Series Analysis with Theory, Plots, and Code Part 1
What is a time series and which are the steps in a forecasting task? How to plot trends and seasonality? How to decompose?
Time Series Analysis
For those of you that want to dive into the world of time series, this is the perfect place to start! Including visualizations for each important time series...
Visualizations with Seaborn
Code examples for histogram, kde, box, count, pie, scatter, join, reg, hex, line bar, violin, boxen, strip, correlation plots.
Feature Engineering
MLOps Lifecycle with MLFlow, Airflow, Amazon S3, PostgreSQL
Full Machine Learning Lifecycle using Airflow, MLflow, and AWS S3.
YouTube Video Resolution Downgrade Classification
Feature engineering, selection, visualization, model development, feature explainability.
Forecasting Wars: Classical Forecasting Methods vs Machine Learning
6 Classical Forecasting Methods are Compared with 3 Machine Learning Algorithms using code in Python on sales data from kaggle.
Feature Engineering with Dates
Given a single feature that contains date and time, I created a function that generates 23 features.
EDA is Fun!
A very popular Kaggle kernel (110.000+ views, 1400+ upvotes, 200+ comments) that won me the 1st prize, as the most informative and upvoted kernel for the pop...
Emotion Detection on Movie Reviews
The objective of this project is the Emotion Analysis of sentences that are comming from movie reviews, using Machine Learning. An attempt will be made to co...
Feature Selection
Ensemble Feature Selection for Machine Learning
Investigate if ensemble feature selection methods are superior of individual in machine learning classification problems.
YouTube Video Resolution Downgrade Classification
Feature engineering, selection, visualization, model development, feature explainability.
Feature Selection for Machine Learning: 3 Categories and 12 Methods
Learn basic theory about the 3 types of feature selection in machine learning namely filters, wrappers, and embedders.
Emotion Detection on Movie Reviews
The objective of this project is the Emotion Analysis of sentences that are comming from movie reviews, using Machine Learning. An attempt will be made to co...
Time Series
Forecasting Wars: Classical Forecasting Methods vs Machine Learning
6 Classical Forecasting Methods are Compared with 3 Machine Learning Algorithms using code in Python on sales data from kaggle.
Time Series Analysis with Theory, Plots, and Code Part 2
Stationarity. ADF stationarity Test. Autocorrelation, Lag Scatter plot. Moving Average, Double and Triple Exponential Smoothing.
Time Series Analysis with Theory, Plots, and Code Part 1
What is a time series and which are the steps in a forecasting task? How to plot trends and seasonality? How to decompose?
Time Series Analysis
For those of you that want to dive into the world of time series, this is the perfect place to start! Including visualizations for each important time series...
Exploratory Data Analysis
YouTube Video Resolution Downgrade Classification
Feature engineering, selection, visualization, model development, feature explainability.
Time Series Analysis
For those of you that want to dive into the world of time series, this is the perfect place to start! Including visualizations for each important time series...
EDA is Fun!
A very popular Kaggle kernel (110.000+ views, 1400+ upvotes, 200+ comments) that won me the 1st prize, as the most informative and upvoted kernel for the pop...
Clustering
Outlier Detection — Theory, Visualizations, and Code
Five Algorithms to rule them all, Five Algorithms to find them, Five Algorithms to bring them all and in the darkness bind them.
Clustering Theory
Clustering is the most common form of unsupervised learning. It is the process that involves the grouping of data points into classes of similar objects.
Battle of the Cities
Let’s suppose that you want to relocate. You don’t know where, yet. You have enough money in order to relocate and then search for a job in your new city. Yo...
Deployment
MLOps Lifecycle with MLFlow, Airflow, Amazon S3, PostgreSQL
Full Machine Learning Lifecycle using Airflow, MLflow, and AWS S3.
Developing and Deploying a COMPLETE project using FastAPI, Jinja2, SQLAlchemy, Docker and AWS
Using the best Software Engineering practices to create a simple and modern app.
Statify
An app where anyone can join with Spotify credentials and see a lot of cool statistics about his/her profile.
Software Development
Developing and Deploying a COMPLETE project using FastAPI, Jinja2, SQLAlchemy, Docker and AWS
Using the best Software Engineering practices to create a simple and modern app.
Statify
An app where anyone can join with Spotify credentials and see a lot of cool statistics about his/her profile.
Web Development
Developing and Deploying a COMPLETE project using FastAPI, Jinja2, SQLAlchemy, Docker and AWS
Using the best Software Engineering practices to create a simple and modern app.
Statify
An app where anyone can join with Spotify credentials and see a lot of cool statistics about his/her profile.
API
Developing and Deploying a COMPLETE project using FastAPI, Jinja2, SQLAlchemy, Docker and AWS
Using the best Software Engineering practices to create a simple and modern app.
Statify
An app where anyone can join with Spotify credentials and see a lot of cool statistics about his/her profile.
MLOps
MLOps Lifecycle with MLFlow, Airflow, Amazon S3, PostgreSQL
Full Machine Learning Lifecycle using Airflow, MLflow, and AWS S3.