Machine Learning Program / Project - 05

Question 05: Implement K-Nearest Neighbors algorithm on diabetes.csv dataset. Compute confusion matrix, accuracy, error rate, precision and recall on the given dataset.

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Question ?

Answer

Programming Code:

Following code write in: ML_P05.py

# ML Project Program 05 

# K-Nearest Neighbors Algorithm on diabetes.csv dataset
import pandas as pd
import numpy as np

data = pd.read_csv("./diabetes_dataset/diabetes.csv")
data
data.info()
data.describe()
data.columns
# Checking null values

data.isnull().sum()
# create variables
data_x = data.drop(columns = "Outcome", axis=1)
data_y = data['Outcome']
data.shape
data_x.shape , data_y.shape
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
scaledX = scale.fit_transform(data_x)

# split into Train & Test 
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(scaledX, data_y, test_size = 0.2,)
# Machine Learning Model - KNN
from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors = 7)

knn.fit(x_train, y_train)
y_pred = knn.predict(x_test)
from sklearn import metrics

# Confusion Matrix

cs = metrics.confusion_matrix(y_test, y_pred)

print("Confusion Matrix is : \n", cs)
# Accuracy score

ac = metrics.accuracy_score(y_test, y_pred)

print("Accuracy score is : ", ac)                # Model Accuracy is 69%
# Error Rate

er = 1 - ac

print("Error rate is : ", er)           # Error Rate is : 0.305
# Precision

p = metrics.precision_score(y_test, y_pred)

print("Precision: ", p)
#  Recall

r = metrics.recall_score(y_test, y_pred)

print("Recall: ", r)
# Precision score is: 0.607            &
# Recall score is: 0.534
# Thanks for Watching

# Thanks For Reading.

Output: