by Higher School of Economics & Yandex Data School

I've completed the course in March, 2016 with the 100 % score.

NumPy for operations on vectors & matrices:

• 01-numpy-matrix-normalization.py
• 02-numpy-matrix-operations.py
• 03-numpy-matrix-union.py

Pandas for data preconditioning:

• 04-pandas-data-preconditioning-01.py
• 04-pandas-data-preconditioning-02.py
• 04-pandas-data-preconditioning-03.py
• 04-pandas-data-preconditioning-04.py
• 04-pandas-data-preconditioning-05.py
• 04-pandas-data-preconditioning-06.py

Lesson Insights: There were 577 males & 314 females aboard Titanic. The most frequent female first name was Anna (second frequent name was Mary). Average age was 29.7, while median is 28. 24 % of all passengers had 1st class tickets. Only 38 % survived.

Decision tree feature importances: 01-sklearn-decision-tree-feature-importances.py

Lesson Insights: Females & passengers with the most expensive tickets had the most chance to survive.

kNN method for classification, k parameter determination: 01-neighbours-number-determination.py

kNN method for regression, metric determination: 02-metric-determination.py

Feature normalization for classification with Perceptron: 01-feature-normalization.py

Support vector selection: 01-svm.py

Text analysis: 02-text-analysis.py

Logistic regression & AUC-ROC score calculation: 01-logistic-regression.py

Basic & complex metrics calculations: 01-score-metrics.py

Ridge regression of sparse features: 01-ridge-regression.py

Dow Jones index analysis: 01-principal-components.py

Random forest size calculation: 01-random-forest-size.py

Gradient boosting vs. random forest comparison: 02-gradient-boosting.py

Image color count reduction: 01-image-color-count-reduction.py

Gradient boosting & logistic regression: 01-solution.py

See my results on Kaggle.