{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Iris Classification"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# dataset\n",
"from sklearn import datasets\n",
"# plot\n",
"import seaborn as sns\n",
"sns.set_style(\"darkgrid\")\n",
"import matplotlib.pyplot as plt\n",
"# data manipulation\n",
"import pandas as pd\n",
"import numpy as np\n",
"# To display image in the jupyter notebook cell\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Question\n",
"\n",
"In the last section, we learn about perceptron. How to use it to classify data? To answer this question, let's look at an example first: there is a data set which consists of two species of Iris. Our questions are: \n",
"\n",
"- How to classify those two species? \n",
"- When given a new Iris, how to predict which Iris category it belongs to?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Set\n",
"\n",
"The data set we will use is modified from the Iris dataset which can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Iris/). It consists of 50 samples from each of **two species** of Iris (Iris setosa, Iris virginica). **Two features** were measured from each sample: the length of the sepals and petals, in centimeters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get a close look at the dataset."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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