Repository for studying ML
- cancer project
- MNN test (picture analythe)
- fish project (picture analythe, CNN)
- spam project (text analythe, RNN)
- avocado project
- wine project
- mashroom project
Structure of project:
cancer
├──Breast Cancer.ipynb - code for data analysis and machine learning
The goal was to analythe data and learn boosting and compare the results.
Structure of project:
MNN_test
├── MNN_test.ipynb - code for testing mnn
The goal was to test Mobile NN model for predicting objects from pictures.
Structure of project:
fish
├── fish.ipynb - code for machine learning
The goal was to build CNN for predicting fish and compare the results.
Best CPU result:
Best GPU result:
Upd: test Mobile NN and compare with CNN result.
Structure of project:
spam
├── spam.ipynb - code for machine learning
Database contains text and classification. The goal was to build model for predicting spam or ham and learn steps for text analythe.
In branch v2 RNN was used.
Structure of project:
avocado
├── avocado.ipynb - code for data analysis and machine learning
Database contains data about avocado (Date, AveragePrice, Total Volume, 4046 (total number of avocados with PLU 4046 sold), 4225 (total number of avocados with PLU 4225 sold), 4770 (total number of avocados with PLU 4770 sold), Total Bags, Small Bags, Large Bags, XLarge Bags, type, year, region) where region or AveragePrice are predicted values. The goal was to predict 2 or more values from one dataset and compare the result depending on algorithm's parameters.
Structure of project:
wine
├── winequality-red.csv - contains data
├── Wine_Quality.ipynb - code for data analysis and machine learning
winequality-red.csv contains data about wine (fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, alcohol, quality) where quality is predicted value. The goal was to study the SMOTE method and compare the results.
Structure of project:
mashroom
├── mashrooms.csv - contains data
├── mashrooms.ipynb - code for data analysis and machine learning
winequality-red.csv contains data about mashrooms (cap-shape, cap-surface, cap-color, bruises, odor, gill-attachment, gill-spacing, gill-size, gill-color, stalk-shape, stalk-root, stalk-surface-above-ring, stalk-surface-below-ring, stalk-color-above-ring, stalk-color-below-ring, veil-type, veil-color, ring-number, ring-type, spore-print-color, population, habitat) where class is predicted value (e - edible, p - poison). The goal was to analyze database and compare the results of algorithms.