These 2 computer vision tools will blow your mind — Article Co-authors with : @bonnefoypy and @emeric.chaize CEOs at Olexya. Building the best model is a key step after exploratory data analysis and feature selection in any data science project. …

Tips and tricks senior data scientists use for fast features engineering — Article Co-authors with : @bonnefoypy and @emeric.chaize CEOs at Olexya. Features engineering is a natural step after exploratory data analysis in most data science projects. This process consists of engineering the right features for obtaining the best predictions. Easy to use features engineering methods include Pycaret and Azure studio.

A single line code comparison of Pypolar, Vaex, Pandas, Modim — Article Co-author with : @bonnefoypy and @emeric.chaize CEOs at Olexya. Read data is a crucial step in most data science projects whether your developing machine and deep learning models or business analytics. This process is before handling missing data, coding data types (continuous, categorical), and shaping data structures. Due to…

A Fast and easy way to get a full data science analysis report in few lines of Python code — Article Co-author with : @bonnefoypy , CEO at Olexya. Due to the high quantity of data, finding tricks for faster analysis using automatizations library is a key advantage for becoming a unicorn data scientist. AWS sagemaker offers various tools for developing machine and deep learning models in few lines of…

A Complete Pythonic Encoding Tutorial — Categorical encoding is a fundamental step in exploratory data analysis (EDA) for most advanced data science projects whether your developing machine learning models or business analytics. Most libraries including scikit-learn use a variety of data encoding methods. Due to the high quantity of data, finding tricks for getting the best…

3 Best methods for handling missing values — Handling missing values is a key step in exploratory data analysis (EDA) for most data science projects whether your developing machine learning models or business analytics. Most libraries including scikit-learn don’t build a model using data with missing values. Due to the high quantity of data, finding tricks for getting…

The exclusive guide to pythonic outlier detections with guaranteed results — Outliers detection is a natural preliminary step in exploratory data analysis for getting the most impacting results in most data science projects. This process consists of eliminating exceptionally out-limits values for obtaining the best predictions. Easy to use outlier’s detection methods generally include: Min-max Analysis, Z-Score Extreme Value Analysis, Inter…

Or How to Get The Best Machine learning Model Without Overfitting? — Choosing the best model is a key step after feature selection in any data science projects. This process consists of using the best algorithms (supervised, unsupervised) for obtaining the best predictions. Automate model selection methods for high dimensional datasets generally include Libra and Pycaret. A unicorn data-scientist needs to master…

The 5 Best Feature Selection Methods in few lines of codes — Features selection is a second natural step after exploratory data analysis in most data science projects. This process consists of choosing the right features for obtaining the best predictions. Easy to use features selection methods generally include SelectFromModel, Feature ranking with recursive feature elimination, filter-based univariate selection, features importance, Voting…