Machine-Learning-Model-Deployment/MachineLearningCode.py at master · kittupriyatham/Machine-Learning-Model-Deployment · GitHub

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"""
docstring
"""

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
import numpy as np
import pandas as pd

class MachineLearningCode:
    """
    docstring
    """
    def __init__(self):
        self.iris = load_iris()
        self.knn = None
        self.x_new = None
        self.x_train = None
        self.x_test = None
        self.y_train = None
        self.y_test = None
        self.prediction = None

    def train(self):
        """
        docstring
        """
        self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(
            self.iris['data'], self.iris['target'], random_state=0)
        self.knn = KNeighborsClassifier(n_neighbors=1)
        self.knn.fit(self.x_train, self.y_train)

    def predict(self, sepal_length, sepal_width, petal_length, petal_width):
        """
        docstring
        """
        self.x_new = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
        self.prediction = self.knn.predict(self.x_new)
        return [(self.iris['target_names'][self.prediction]), (self.knn.score(
            self.x_test, self.y_test))]

    def save_data(self):
        df = pd.DataFrame(self.iris['data'], columns=self.iris['feature_names'])
        l = self.iris['target'].tolist()
        species = self.iris['target_names'].tolist()
        l = [("iris-" + species[i]) for i in l]
        df['species'] = l
        df.index.name = "index"
        df.to_csv("iris.csv")