move code

szabgab · Jun 20, 2024 · 6c2c2fc · 6c2c2fc
1 parent b22b018
commit 6c2c2fc
Showing 1 changed file with 33 additions and 23 deletions.
diff --git a/python/examples/ml/iris.ipynb b/python/examples/ml/iris.ipynb
@@ -8,6 +8,16 @@
     "Source [Kaggle Iris](https://www.kaggle.com/uciml/iris)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9796f468-014b-47e3-82fd-38d2169d8168",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install seaborn scikit-learn pandas numpy matplotlib"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -70,25 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b2e8b3dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = df[\"Species\"]\n",
-    "X = df[[\"SepalLengthCm\", \"SepalWidthCm\",\"PetalLengthCm\",\"PetalWidthCm\"]]\n",
-    "x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y)\n",
-    "model = LogisticRegression(max_iter=200).fit(x_train, y_train)\n",
-    "print('train accuracy:', model.score(x_train, y_train))\n",
-    "print('test accuracy:', model.score(x_test, y_test))\n",
-    "y_prediction = model.predict(x_test)\n",
-    "print('test classification report:')\n",
-    "print(classification_report(y_test, y_prediction))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2009513b",
+   "id": "1a6962cb-180c-4d7c-9422-7275f5e35fd8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +91,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e43ea3c1",
+   "id": "6a3cbab0-706c-486c-9c25-163a054d8b6a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8f49821",
+   "id": "ecec6a69-47c7-4ca0-9cd1-4a3de69b9171",
    "metadata": {},
    "source": [
     "Set the color manually"
@@ -120,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "04beedc6",
+   "id": "afc942c1-c205-4e10-a34f-830510e1ee0f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,6 +122,24 @@
     "sns.scatterplot(x=\"SepalLengthCm\", y=\"SepalWidthCm\", hue=\"Species\", data=df, palette=palette);"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2e8b3dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = df[\"Species\"]\n",
+    "X = df[[\"SepalLengthCm\", \"SepalWidthCm\",\"PetalLengthCm\",\"PetalWidthCm\"]]\n",
+    "x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y)\n",
+    "model = LogisticRegression(max_iter=200).fit(x_train, y_train)\n",
+    "print('train accuracy:', model.score(x_train, y_train))\n",
+    "print('test accuracy:', model.score(x_test, y_test))\n",
+    "y_prediction = model.predict(x_test)\n",
+    "print('test classification report:')\n",
+    "print(classification_report(y_test, y_prediction))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -291,7 +301,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,