# LearnSL 1.0.0

- New `decission_tree()` function to create a perfect decision tree based on a set of observations and (if selected) see the step-by-step procedure;
- New `multivariate_linear_regression()` function to generate linear equation lines that approximate the values on a set of observations and (if selected) see the step-by-step procedure;
- New `polynomial_regression()` function to generate polynomial equation lines that approximate the values on a set of observations to selected degree and (if selected) see the step-by-step procedure;
- New `perceptron()` function to calculate the weights of a perceptron and predict values on a set of observations and (if selected) see the step-by-step procedure;
- New `knn()` function to perform k-nearest neighbors classification on a set of observations and (if selected) see the step-by-step procedure;
- New `print.tree_struct()` function that prints a tree with the structure of the output of the decision_tree() function;
- New `act_method()` function that calculates the selected activation function to a given input;
- New `db1rl` data.frame with 20 observations (4 features). Values form different types of lines (linear, exponential, logarithmic, sine);
- New `db_per_and` data.frame with 8 observations (2 features). "AND" logic gate;
- New `db_per_or` data.frame with 8 observations (2 features). "OR" logic gate;
- New `db_per_xor` data.frame with 8 observations (2 features). "XOR" logic gate;
- New `db_flowers` data.frame with 25 observations (4 features) containing values about flowers;
- New `db2` data.frame with 10 observations (4 features) containing values about vehicles;
- New `db3` data.frame with 12 observations (5 features) containing values about vehicles.
- New `db_tree_struct` data.frame with 12 observations (5 features) containing values about vehicles.

* Initial CRAN submission.
