From f1e69345d188c23ceb99b75c7d417d278e799a6c Mon Sep 17 00:00:00 2001
From: Web4 <sandraisom0@gmail.com>
Date: Mon, 9 Dec 2024 10:16:39 -0500
Subject: [PATCH] Add files via upload

---
 HAI.js | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)
 create mode 100644 HAI.js

diff --git a/HAI.js b/HAI.js
new file mode 100644
index 000000000..df4982cea
--- /dev/null
+++ b/HAI.js
@@ -0,0 +1,38 @@
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.utils import to_categorical
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+# Load your dataset
+data = pd.read_csv('path/to/your/dataset.csv')
+
+# Assuming the last column is the label
+labels = data.iloc[:, -1]
+features = data.iloc[:, :-1]
+
+# Convert labels to categorical if necessary
+labels = to_categorical(labels)
+
+# Split the data into training and testing sets
+train_data, test_data, train_labels, test_labels = train_test_split(features, labels, test_size=0.2)
+
+# Build the model
+model = Sequential([
+Dense(64, input_shape=(features.shape[1],), activation='relu'),
+Dense(64, activation='relu'),
+Dense(labels.shape[1], activation='softmax')
+])
+
+# Compile the model
+model.compile(optimizer='adam',
+loss='categorical_crossentropy',
+metrics=['accuracy'])
+
+# Train the model
+model.fit(train_data, train_labels, epochs=50, batch_size=32)
+
+# Evaluate the model
+loss, accuracy = model.evaluate(test_data, test_labels)
+print(f'Test accuracy: {accuracy}')
\ No newline at end of file