{
"cells": [
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"source": [
"Iris Plant Data Modeling
\n",
"by Wilfred Morgan
"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Problem Description:\n",
"Predict the flower class based on available attributes."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"from IPython.display import IFrame"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Flower Classification\n",
"IFrame('http://www.spataru.at/iris-dataset-svm/iris_types.jpg', width=1280, height=300)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Problem assumptions:\n",
"* Data is accurate\n",
"* No missing values\n",
"* Data is or can be in a format to be used for machine learning."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Similar problems:\n",
"* Classifying other floral species\n",
"* Classifying cancer cells"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Basic Information\n",
"* Number of observations: **150**\n",
"* Number of features: **4**\n",
"* Data types of features: **Numerical**\n",
"* Target variable: **class** *(Categorical)*"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"### Peek at the Data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# Import Libaries\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# Import Data\n",
"data_location = \"/Users/wmemorgan/Google Drive/Computer_Data_Science_Lab/iris_data_modeling/data/02_prepared_data/iris.data\" \n",
"names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
"data = pd.read_csv(data_location, names=names)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sepal-length | \n",
" sepal-width | \n",
" petal-length | \n",
" petal-width | \n",
" class | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 5.1 | \n",
" 3.5 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
" | 1 | \n",
" 4.9 | \n",
" 3.0 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
" | 2 | \n",
" 4.7 | \n",
" 3.2 | \n",
" 1.3 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
" | 3 | \n",
" 4.6 | \n",
" 3.1 | \n",
" 1.5 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
" | 4 | \n",
" 5.0 | \n",
" 3.6 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
"
\n",
" \n",
"
\n",
"
THANK YOU
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": []
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"file_extension": ".py",
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