ml_project-without_config_file.py

#mport important packages
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import joblib


#path to the dataset
filename = "../data/breast-cancer-wisconsin.data"

#load data 
data = pd.read_csv(filename)

#replace "?" with -99999
data = data.replace('?', -99999)

# drop id column
data = data.drop(['id'], axis=1)

# Define X (independent variables) and y (target variable)

X = data.drop(['class'], axis=1)
y = data['class']

#split data into train and test set 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


# call our classifer and fit to our data
classifier = KNeighborsClassifier(n_neighbors=5, weights="uniform",
								 algorithm = "auto", leaf_size = 25,
								 p=1, metric="minkowski", n_jobs=-1)
								 
#training the classifier
classifier.fit(X_train, y_train)

#test our classifier 
result = classifier.score(X_test, y_test)
print("Accuracy score is. {:.1f}".format(result))

#save our classifier in the model directory
model_name = "KNN_classifier"
joblib.dump(classifier, '../models/{}.pkl'.format(model_name))