Merge pull request #1 from HarshVR1947/HarshVR1947-patch-1

Create Artificial_NeuralNet.py

Author: HarshVR1947

machine_learning/Artificial_NeuralNet.py

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+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+# Load the Pima Indians Diabetes dataset
+file_path = "pimaindiandiabetes.csv"  # Replace with the actual file path
+columns = ["Pregnancies", "Glucose", "BloodPressure", "SkinThickness", "Insulin", "BMI", "DiabetesPedigreeFunction", "Age", "Outcome"]
+data = pd.read_csv(file_path, names=columns)
+
+# Split the data into features and target
+X = data.drop("Outcome", axis=1)
+y = data["Outcome"]
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Standardize the features (mean=0, std=1)
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Create an ANN model with backpropagation
+model = keras.Sequential([
+    layers.Input(shape=(X_train.shape[1],)),
+    layers.Dense(64, activation='relu'),
+    layers.Dense(32, activation='relu'),
+    layers.Dense(1, activation='sigmoid')
+])
+
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Train the model
+model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)
+
+# Evaluate the model on the test data
+_, accuracy = model.evaluate(X_test, y_test)
+print(f"Accuracy on Pima Indians Diabetes dataset: {accuracy * 100:.2f}%")