{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# KNN with Iris" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sklearn.neighbors as nei\n", "import pandas as pd\n", "import sklearn.model_selection as mod" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Load the iris data set from a URL.\n", "df = pd.read_csv(\"https://github.com/ianmcloughlin/datasets/raw/master/iris.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", " | sepal_length | \n", "sepal_width | \n", "petal_length | \n", "petal_width | \n", "class | \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", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
145 | \n", "6.7 | \n", "3.0 | \n", "5.2 | \n", "2.3 | \n", "virginica | \n", "
146 | \n", "6.3 | \n", "2.5 | \n", "5.0 | \n", "1.9 | \n", "virginica | \n", "
147 | \n", "6.5 | \n", "3.0 | \n", "5.2 | \n", "2.0 | \n", "virginica | \n", "
148 | \n", "6.2 | \n", "3.4 | \n", "5.4 | \n", "2.3 | \n", "virginica | \n", "
149 | \n", "5.9 | \n", "3.0 | \n", "5.1 | \n", "1.8 | \n", "virginica | \n", "
150 rows × 5 columns
\n", "