culminator 0.7

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Culminator

Culminator is a Python package designed to facilitate regression, classification, and clustering. It provides functionalities for data loading, preprocessing, training, and predicting. This README file serves as a guide to understand and utilize the package effectively.

Installation

To install culminator, you can use pip:

pip install culminator

Regression

The regression module provides functions for training and predicting regression models.

Functions:

1. regression: Trains a regression model
2. predict_values_regression: Predicts values using a trained regression model

from culminator import regression

X, y, degree, parameters, predictions, mean, std = regression(filename="Diamonds Prices V2.csv", y_column=7, drop_columns=[0], round_digits=2)

input_data = np.array([[0.3,"Ideal","I","SI2",62,54,348,4.31,4.34,2.68],
                       [0.42,"Premium","G","VS2",61.9,58,1087,4.82,4.77,2.97],
                       [0.7,"Premium","F","SI2",62.5,60,2373,5.66,5.61,3.52]])
input_predictions = predict_values(input_data, "Diamonds are Forever V2.csv", mean, std, degree, parameters, 7, [0], 2)

Classification

The classification module provides functions for training and predicting classification models.

Functions:

1. classification: Trains a classification model
2. predict_values_classification: Predicts values using the trained classification model

from culminator import classification

X, y, parameters, predictions, label_map, mean, std = classification(filename="Diabetes.csv", drop_columns=[0,2])

input_data = np.array([[1,95,66,13,38,19.6,0.334,25],
                       [7,81,78,40,48,46.7,0.261,42],
                       [10,101,86,37,0,45.6,1.136,38]])
input_predictions = predict_values(input_data, "Diabetes.csv", mean, std, parameters, label_map, drop_columns=[0,2])

Clustering

The clustering module provides functions for clustering data points.

Functions:

1. clustering: Performs clustering on a dataset
2. predict_values_clustering: Predicts cluster assignments for new data points

from culminator import clustering

centroids, cluster_assignments, mean, std = clustering(filename="Flower Species.csv", k=3)

input_data = np.array([[13, "Blue, 2.59,2.87],
                       [11, "Purple", 0.74,2.5],
                       [13, "Green", 4.28,2.26]])
input_predictions = predict_values(input_data, "Wine Types.csv", mean, std, centroids)

Notes

• Ensure that the input data files are properly formatted and contain necessary columns.
• This README provides a brief overview of the package and its functionalities. For detailed documentation, including parameter descriptions and usage examples, refer to the official documentation