{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "code", "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "import pandas as pd" ], "metadata": { "id": "w9GeLlpvziWO" }, "execution_count": 1, "outputs": [] }, { "cell_type": "code", "source": [ "dataset = pd.read_csv('iris.csv')" ], "metadata": { "id": "QcVXNYM3zkqX" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "dataset.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "S1xkiw_kzmJp", "outputId": "ecbd9a50-5018-4df6-f917-b83400f396ea" }, "execution_count": 3, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ], "text/html": [ "\n", "
\n", " | sepal_length | \n", "sepal_width | \n", "petal_length | \n", "petal_width | \n", "species | \n", "
---|---|---|---|---|---|
0 | \n", "5.1 | \n", "3.5 | \n", "1.4 | \n", "0.2 | \n", "setosa | \n", "
1 | \n", "4.9 | \n", "3.0 | \n", "1.4 | \n", "0.2 | \n", "setosa | \n", "
2 | \n", "4.7 | \n", "3.2 | \n", "1.3 | \n", "0.2 | \n", "setosa | \n", "
3 | \n", "4.6 | \n", "3.1 | \n", "1.5 | \n", "0.2 | \n", "setosa | \n", "
4 | \n", "5.0 | \n", "3.6 | \n", "1.4 | \n", "0.2 | \n", "setosa | \n", "
SVC(kernel='linear', random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
SVC(kernel='linear', random_state=0)