Exploring Datasets from kaggle or other open-source directories

Commit rules:

1. Dataset name: the work done - what to do next / what is needed
2. README.md : update

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Car-Evaluation Dataset

Evaluating the following famous Car Evaluation Data Set by Marco Bohavic with following two models.

1. Decision Tree model with entropy and gini index acquired ±78% accuracy score on both criterions.
2. However, Support Vector Michine model with GridSearch found that SVM with ninth degree polynomial brings accuracy score of ±90%. More cleaner representation of this method you can see in this Kaggle notebook

Both models are stored in one car.ipynb file.

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Student Performance Dataset

Evaluating the following famous Student Performance Data Set by Paulo Cortez.

1. Two netobooks:

• Student performance - main file with all explanations and procedure
• briefly testing models - simple draft for testing model

2. Detailed visualization of feature engineering
3. Detailed explanation of such important things, as scaling, encoding and feature chose

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_neural networks

Directory dedicated to learning ANN

1. Two netobooks:

• Minimal Intro with TensorFlow 2 - implementing simple linear formula with simpliest ANN
• Simple TensorFlow Example - TF as low-level API