diff --git a/HW 5/Iris_knn_W2025_template.ipynb b/HW 5/Iris_knn_W2025_template.ipynb index a443351..f67157d 100644 --- a/HW 5/Iris_knn_W2025_template.ipynb +++ b/HW 5/Iris_knn_W2025_template.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 49, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -49,134 +49,18 @@ "id": "DjCaxH3BKJ_O", "outputId": "ab33e45d-d332-412e-db2a-88cc4c9528f4" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
...............
1466.32.55.01.9
1476.53.05.22.0
1486.23.45.42.3
1495.93.05.11.8
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150 rows × 4 columns

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" - ], - "text/plain": [ - " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", - "0 5.1 3.5 1.4 0.2\n", - "1 4.9 3.0 1.4 0.2\n", - "2 4.7 3.2 1.3 0.2\n", - "3 4.6 3.1 1.5 0.2\n", - ".. ... ... ... ...\n", - "146 6.3 2.5 5.0 1.9\n", - "147 6.5 3.0 5.2 2.0\n", - "148 6.2 3.4 5.4 2.3\n", - "149 5.9 3.0 5.1 1.8\n", - "\n", - "[150 rows x 4 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "####################################\n", - "from sklearn.datasets import load_iris\n", + "#from sklearn.datasets import load_iris\n", "#data = load_iris()\n", - "df = pd.DataFrame(data.data, columns=data.feature_names)\n", - "df" + "#df = pd.DataFrame(data.data, columns=data.feature_names)\n", + "#df" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 50, "metadata": { "id": "ANJhDRNPKPR5" }, @@ -276,7 +160,7 @@ "4 5.0 3.6 1.4 0.2 setosa remove_me" ] }, - "execution_count": 13, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" }