Machine Learning Assignment -1 Topic:- -Instance-Based Learning knn Decision Tree, Naive Bayes, ANN скачать в хорошем качестве

Machine Learning Assignment -1 Topic:- -Instance-Based Learning knn Decision Tree, Naive Bayes, ANN

on topic:- -Instance-Based Learning (k-Nearest Neighbours), Training Models (Linear Regression), Decision Tree, Naive Bayes, ANN (Artificial Neural Network). Code Implementation Below is the Python implementation of the models used in this study: import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.naive_bayes import GaussianNB from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score Sample dataset data = { 'Annual_Spending': [500, 1500, 3000, 700, 2500, 1000, 3500, 1200, 800, 2800], 'Avg_Transaction_Value': [50, 150, 300, 70, 250, 100, 350, 120, 80, 280], 'Purchase_Frequency': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10], 'Customer_Tenure': [2, 5, 7, 3, 6, 4, 8, 3, 2, 7], 'Spending_Category': ['Low', 'Medium', 'High', 'Low', 'High', 'Medium', 'High', 'Medium', 'Low', 'High'] } df = pd.DataFrame(data) Encoding categorical values df['Spending_Category'] = df['Spending_Category'].map({'Low': 0, 'Medium': 1, 'High': 2}) Splitting dataset X = df.drop(columns=['Spending_Category']) y = df['Spending_Category'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) Standardizing the dataset scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) 5 k-NN Model knn = KNeighborsClassifier(n_neighbors=3) knn.fit(X_train, y_train) y_pred_knn = knn.predict(X_test) Decision Tree Model dt = DecisionTreeClassifier() dt.fit(X_train, y_train) y_pred_dt = dt.predict(X_test) Naive Bayes Model nb = GaussianNB() nb.fit(X_train, y_train) y_pred_nb = nb.predict(X_test) Performance Evaluation def evaluate_model(y_true, y_pred, model_name): print(f'Performance of {model_name}:') print(f'Accuracy: {accuracy_score(y_true, y_pred):.2f}') print(f'Precision: {precision_score(y_true, y_pred, average="weighted"):.2f}') print(f'Recall: {recall_score(y_true, y_pred, average="weighted"):.2f}') print(f'F1-score: {f1_score(y_true, y_pred, average="weighted"):.2f}') print('
') Evaluating each model evaluate_model(y_test, y_pred_knn, 'k-NN') evaluate_model(y_test, y_pred_dt, 'Decision Tree') evaluate_model(y_test, y_pred_nb, 'Naive Bayes')