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@@ -0,0 +1,101 @@
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+{
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ " age sex bmi bp s1 s2 s3 \\\n",
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+ "0 0.038076 0.050680 0.061696 0.021872 -0.044223 -0.034821 -0.043401 \n",
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+ "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163 0.074412 \n",
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+ "2 0.085299 0.050680 0.044451 -0.005670 -0.045599 -0.034194 -0.032356 \n",
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+ "3 -0.089063 -0.044642 -0.011595 -0.036656 0.012191 0.024991 -0.036038 \n",
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+ "4 0.005383 -0.044642 -0.036385 0.021872 0.003935 0.015596 0.008142 \n",
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+ "\n",
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+ " s4 s5 s6 Target \n",
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+ "0 -0.002592 0.019907 -0.017646 1 \n",
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+ "1 -0.039493 -0.068332 -0.092204 0 \n",
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+ "2 -0.002592 0.002861 -0.025930 1 \n",
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+ "3 0.034309 0.022688 -0.009362 1 \n",
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+ "4 -0.002592 -0.031988 -0.046641 0 \n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "from sklearn.datasets import load_diabetes\n",
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+ "import pandas as pd\n",
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+ "import numpy as np\n",
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+ "\n",
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+ "# Load the diabetes dataset\n",
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+ "diabetes = load_diabetes()\n",
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+ "X = diabetes.data\n",
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+ "y = diabetes.target\n",
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+ "\n",
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+ "# Convert the target into a binary classification problem by thresholding at the median\n",
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+ "threshold = np.median(y)\n",
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+ "y_binary = (y > threshold).astype(int)\n",
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+ "\n",
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+ "# Display the first few rows of the data\n",
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+ "df = pd.DataFrame(X, columns=diabetes.feature_names)\n",
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+ "df['Target'] = y_binary\n",
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+ "print(df.head())\n"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "X_train shape: (353, 10)\n",
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+ "X_test shape: (89, 10)\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "from sklearn.model_selection import train_test_split\n",
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+ "from sklearn.preprocessing import StandardScaler\n",
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+ "\n",
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+ "# Split the data into training and test sets (80% train, 20% test)\n",
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+ "X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, random_state=42)\n",
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+ "\n",
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+ "# Standardize the features\n",
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+ "scaler = StandardScaler()\n",
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+ "X_train_scaled = scaler.fit_transform(X_train)\n",
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+ "X_test_scaled = scaler.transform(X_test)\n",
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+ "\n",
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+ "# Check the shape of the data\n",
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+ "print(f\"X_train shape: {X_train_scaled.shape}\")\n",
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+ "print(f\"X_test shape: {X_test_scaled.shape}\")\n"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "base",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.12.7"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+}
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