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 14 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 Tags
- pandas 10
- features 9
- machine learning 9
- pre-process 8
- visualization 8
- sklearn 7
- EDA 4
- time series 4
- seaborn 3
- clustering 3
- docker 3
- aws 3
- fastapi 2
- sql 2
- encoding 1
- normalization 1
- flask 1
- dash 1
- topic modeling 1
- mlflow 1
- airflow 1
pandas
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.
Statify
An app where anyone can join with Spotify credentials and see a lot of cool statistics about his/her profile.
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...
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...
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.
features
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.
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...
Normalization and Standardization
Why to use normalization and standardization? Which are their differences? When to use them and when to avoid?
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
End to End ML Project
This project aims to apply the best software engineering practices in a Machine Learning project in order to deploy the model.
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...
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...
pre-process
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.
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...
visualization
YouTube Video Resolution Downgrade Classification
Feature engineering, selection, visualization, model development, feature explainability.
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?
Visualizations with Seaborn
Code examples for histogram, kde, box, count, pie, scatter, join, reg, hex, line bar, violin, boxen, strip, correlation plots.
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...
sklearn
End to End ML Project
This project aims to apply the best software engineering practices in a Machine Learning project in order to deploy the model.
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.
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...
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...
EDA
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...
Visualizations with Seaborn
Code examples for histogram, kde, box, count, pie, scatter, join, reg, hex, line bar, violin, boxen, strip, correlation plots.
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...
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...
seaborn
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.
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...
docker
End to End ML Project
This project aims to apply the best software engineering practices in a Machine Learning project in order to deploy the model.
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.
aws
End to End ML Project
This project aims to apply the best software engineering practices in a Machine Learning project in order to deploy the model.
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.
fastapi
End to End ML Project
This project aims to apply the best software engineering practices in a Machine Learning project in order to deploy the model.
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.
sql
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.
encoding
Encoding Categorical Features
Why to encode categorical features? 6 ways to encode different kinds of data.
normalization
Normalization and Standardization
Why to use normalization and standardization? Which are their differences? When to use them and when to avoid?
flask
Statify
An app where anyone can join with Spotify credentials and see a lot of cool statistics about his/her profile.
dash
Statify
An app where anyone can join with Spotify credentials and see a lot of cool statistics about his/her profile.
topic modeling
Topic Modeling on my Watched Movies
Merge IMDb with Wikipedia movie data and build, evaluate and visualize topics using LDA.
mlflow
MLOps Lifecycle with MLFlow, Airflow, Amazon S3, PostgreSQL
Full Machine Learning Lifecycle using Airflow, MLflow, and AWS S3.
airflow
MLOps Lifecycle with MLFlow, Airflow, Amazon S3, PostgreSQL
Full Machine Learning Lifecycle using Airflow, MLflow, and AWS S3.