diff --git a/example1.ipynb b/example1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7096d27b143f0c1f7f92da8291e7c0681335e66c
--- /dev/null
+++ b/example1.ipynb
@@ -0,0 +1,105 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 2)\n",
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2]\n",
+      "(150,)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn import datasets\n",
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "\n",
+    "# import some data from a predefined datatset\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris.data[:, :2]  # we only take the first two features.\n",
+    "Y = iris.target\n",
+    "#print shape of the array for X and Y. Also get value of targets\n",
+    "print (X.shape)\n",
+    "print (Y)\n",
+    "print (Y.shape)\n",
+    "\n",
+    "\n",
+    "# Create an instance of Logistic Regression Classifier and fit the data.\n",
+    "logreg = LogisticRegression(C=1)\n",
+    "logreg.fit(X, Y)\n",
+    "\n",
+    "_, ax = plt.subplots(figsize=(4, 3))\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    logreg,\n",
+    "    X,\n",
+    "    cmap=plt.cm.Paired,\n",
+    "    ax=ax,\n",
+    "    response_method=\"auto\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    shading=\"auto\",\n",
+    "    xlabel=\"Sepal length\",\n",
+    "    ylabel=\"Sepal width\",\n",
+    "    eps=0.5,\n",
+    ")\n",
+    "\n",
+    "# Plot the training points\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors=\"k\", cmap=plt.cm.Paired)\n",
+    "\n",
+    "\n",
+    "plt.xticks(())\n",
+    "plt.yticks(())\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}