Create a Program to Implement Classification in Python Assignment Solution.

Instructions

Objective

Write a python assignment program to implement classification.

Requirements and Specifications

Source Code

K Nearest Neighbors (KNN) ### Name: Gedion ### Dataset - Pima Indians Diabetes Database Dataset link. This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on. - We will try to classify if whether or not a patient has diabetes using KNN ### Read the Dataset import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import math as m %matplotlib inline import warnings warnings.filterwarnings('ignore') from sklearn.model_selection import learning_curve, train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, log_loss df = pd.read_csv("diabetes.csv") df.head() df.shape ### Preprocessing #### deal with null values df.isnull().sum() print("Shape of data:",df.shape) print("Number of missing values in the data", df.isnull().sum().sum()) df.describe().T df.info() ### Visualization #### Number of recoreds for every Pregnancies sns.countplot(x="Pregnancies", data=df) #### Age distribution sns.distplot(df['Age'], label='count') #### Difference between Pregnancies and Age effect sns.pointplot(data=df, x="Pregnancies", y="Age", hue="Outcome") #### The correlation heatmap plt.figure(figsize=(16,8), dpi=150) sns.heatmap(df.corr(),annot=True) ### Feature & target selection y = df['Outcome'] X = df.drop('Outcome', axis = 1) ### Split the dataset X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2 ,stratify=y, random_state = 42) ### Evaluation phase K = np.arange(3,12) train_accuracy =np.empty(len(K)) test_accuracy = np.empty(len(K)) for i,k in enumerate(K): knn = KNeighborsClassifier(n_neighbors=k) knn.fit(X_train, y_train) train_accuracy[i] = knn.score(X_train, y_train) test_accuracy[i] = knn.score(X_test, y_test) train_accuracy test_accuracy plt.title('KNN Accuracy Versus Number of Neighbors') plt.plot(K, test_accuracy, label='Testing Accuracy') plt.plot(K, train_accuracy, label='Training accuracy') plt.legend() plt.xlabel('Number of neighbors') plt.ylabel('Accuracy') plt.show() - max testing accuracy for k=4,6,8. ### Training phase knn4 = KNeighborsClassifier(n_neighbors=4) knn4.fit(X_train, y_train) knn6 = KNeighborsClassifier(n_neighbors=6) knn6.fit(X_train, y_train) knn8 = KNeighborsClassifier(n_neighbors=8) knn8.fit(X_train, y_train) y_pred4 = knn4.predict(X_train) y_pred6 = knn6.predict(X_train) y_pred8 = knn8.predict(X_train) accuracy_score(y_train, y_pred4) # KNN when K=4 knn_accuracy4 = accuracy_score(y_train, y_pred4) knn_confusionMatrix4 = confusion_matrix(y_train, y_pred4) knn_classification4 = classification_report(y_train, y_pred4) knn_log_loss4 = log_loss(y_train, y_pred4) # KNN when K=6 knn_accuracy6 = accuracy_score(y_train, y_pred6) knn_confusionMatrix6 = confusion_matrix(y_train, y_pred6) knn_classification6 = classification_report(y_train, y_pred6) knn_log_loss6 = log_loss(y_train, y_pred6) # KNN when K=8 knn_accuracy8 = accuracy_score(y_train, y_pred8) knn_confusionMatrix8 = confusion_matrix(y_train, y_pred8) knn_classification8 = classification_report(y_train, y_pred8) knn_log_loss8 = log_loss(y_train, y_pred8) print("- Accuracy score of KNN") print(f"K=4: {knn_accuracy4}") print(f"K=6: {knn_accuracy6}") print(f"K=8: {knn_accuracy8}\n\n") print("- Confusion matrix of KNN") print(f"K=4:\n {knn_confusionMatrix4}\n") print(f"K=6:\n {knn_confusionMatrix6}\n") print(f"K=8:\n {knn_confusionMatrix8}\n\n") print("- Classification report of KNN") print(f"\nK=4:\n {knn_classification4}\n") print(f"\nK=6:\n {knn_classification6}\n") print(f"\nK=8:\n {knn_classification8}") print("- Log Loss of KNN") print(f"\nK=4:\n {knn_log_loss4}\n") print(f"\nK=6:\n {knn_log_loss6}\n") print(f"\nK=8:\n {knn_log_loss8}") ### Testing phase y_pred4 = knn4.predict(X_test) y_pred6 = knn6.predict(X_test) y_pred8 = knn8.predict(X_test) # KNN when K=4 knn_accuracy4 = accuracy_score(y_test, y_pred4) knn_confusionMatrix4 = confusion_matrix(y_test, y_pred4) knn_classification4 = classification_report(y_test, y_pred4) knn_log_loss4 = log_loss(y_test, y_pred4) # KNN when K=6 knn_accuracy6 = accuracy_score(y_test, y_pred6) knn_confusionMatrix6 = confusion_matrix(y_test, y_pred6) knn_classification6 = classification_report(y_test, y_pred6) knn_log_loss6 = log_loss(y_test, y_pred6) # KNN when K=8 knn_accuracy8 = accuracy_score(y_test, y_pred8) knn_confusionMatrix8 = confusion_matrix(y_test, y_pred8) knn_classification8 = classification_report(y_test, y_pred8) knn_log_loss8 = log_loss(y_test, y_pred8) print("- Accuracy score of KNN") print(f"K=4: {knn_accuracy4}") print(f"K=6: {knn_accuracy6}") print(f"K=8: {knn_accuracy8}\n\n") print("- Confusion matrix of KNN") print(f"K=4:\n {knn_confusionMatrix4}\n") print(f"K=6:\n {knn_confusionMatrix6}\n") print(f"K=8:\n {knn_confusionMatrix8}\n\n") print("- Classification report of KNN") print(f"\nK=4:\n {knn_classification4}\n") print(f"\nK=6:\n {knn_classification6}\n") print(f"\nK=8:\n {knn_classification8}") print("- Classification report of KNN") print(f"\nK=4:\n {knn_log_loss4}\n") print(f"\nK=6:\n {knn_log_loss6}\n") print(f"\nK=8:\n {knn_log_loss8}")