Step 2:Load the heart disease dataset from a file using pd.read_csv().
Step 3:Separate the features and labels from the dataset using data.iloc values for features (X) and data.iloc[:, -1].values for labels (y).
Step 4:Split the dataset into training and testing sets using train_test_split().
Step 5:Normalize the feature data using StandardScaler() to scale the features to have zero mean and unit variance.
Step 6:Create an MLPClassifier model with desired architecture and hyperparameters, such as hidden_layer_sizes, max_iter, and random_state.
Step 7:Train the MLP model on the training data using mlp.fit(X_train, y_train). The model adjusts its weights and biases iteratively to minimize the training loss.
Step 8:Make predictions on the testing set using mlp.predict(X_test).
Step 9:Evaluate the model's accuracy by comparing the predicted labels (y_pred) with the actual labels (y_test) using accuracy_score().
Step 10:Print the accuracy of the model.
Step 11:Plot the error convergence during training using plt.plot() and plt.show().
import numpy as np
import pandas as pd
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
data = pd.read_csv("/content/heart.csv")
X=data.iloc[:, :-1].values
y=data.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
mlp = MLPClassifier(hidden_layer_sizes=(100, 100), max_iter=1000, random_state=42)
training_loss = mlp.fit(X_train, y_train).loss_curve_
y_pred = mlp.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
plt.plot(training_loss)
plt.title("MLP Training Loss Convergence")
plt.xlabel("Iteration")
plt.ylabel("Training Loss")
plt.show()
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.Series(data.target)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
mlp_classifier = MLPClassifier(hidden_layer_sizes=(64,), max_iter=1000, random_state=42)
mlp_classifier.fit(X_train_scaled, y_train)
y_pred = mlp_classifier.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
classification_rep = classification_report(y_test, y_pred)print(f"Accuracy: {accuracy}")
print("\nConfusion Matrix:")
print(conf_matrix)
print("\nClassification Report:")
print(classification_rep)
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