{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# An example machine learning notebook\n",
"\n",
"### Notebook by [Randal S. Olson](http://www.randalolson.com/)\n",
"#### Supported by [Jason H. Moore](http://www.epistasis.org/)\n",
"#### [University of Pennsylvania Institute for Bioinformatics](http://upibi.org/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**It is recommended to [view this notebook in nbviewer](http://nbviewer.ipython.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb) for the best viewing experience.**\n",
"\n",
"**You can also [execute the code in this notebook on Binder](https://mybinder.org/v2/gh/rhiever/Data-Analysis-and-Machine-Learning-Projects/master?filepath=example-data-science-notebook%2FExample%20Machine%20Learning%20Notebook.ipynb) - no local installation required.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of contents\n",
"\n",
"1. [Introduction](#Introduction)\n",
"\n",
"2. [License](#License)\n",
"\n",
"3. [Required libraries](#Required-libraries)\n",
"\n",
"4. [The problem domain](#The-problem-domain)\n",
"\n",
"5. [Step 1: Answering the question](#Step-1:-Answering-the-question)\n",
"\n",
"6. [Step 2: Checking the data](#Step-2:-Checking-the-data)\n",
"\n",
"7. [Step 3: Tidying the data](#Step-3:-Tidying-the-data)\n",
"\n",
" - [Bonus: Testing our data](#Bonus:-Testing-our-data)\n",
"\n",
"8. [Step 4: Exploratory analysis](#Step-4:-Exploratory-analysis)\n",
"\n",
"9. [Step 5: Classification](#Step-5:-Classification)\n",
"\n",
" - [Cross-validation](#Cross-validation)\n",
"\n",
" - [Parameter tuning](#Parameter-tuning)\n",
"\n",
"10. [Step 6: Reproducibility](#Step-6:-Reproducibility)\n",
"\n",
"11. [Conclusions](#Conclusions)\n",
"\n",
"12. [Further reading](#Further-reading)\n",
"\n",
"13. [Acknowledgements](#Acknowledgements)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"In the time it took you to read this sentence, terabytes of data have been collectively generated across the world — more data than any of us could ever hope to process, much less make sense of, on the machines we're using to read this notebook.\n",
"\n",
"In response to this massive influx of data, the field of Data Science has come to the forefront in the past decade. Cobbled together by people from a diverse array of fields — statistics, physics, computer science, design, and many more — the field of Data Science represents our collective desire to understand and harness the abundance of data around us to build a better world.\n",
"\n",
"In this notebook, I'm going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like.\n",
"\n",
"In addition to providing code examples, I also hope to imbue in you a sense of good practices so you can be a more effective — and more collaborative — data scientist.\n",
"\n",
"I will be following along with the data analysis checklist from [The Elements of Data Analytic Style](https://leanpub.com/datastyle), which I strongly recommend reading as a free and quick guidebook to performing outstanding data analysis.\n",
"\n",
"**This notebook is intended to be a public resource. As such, if you see any glaring inaccuracies or if a critical topic is missing, please feel free to point it out or (preferably) submit a pull request to improve the notebook.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## License\n",
"\n",
"[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely usable and shareable as possible."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Required libraries\n",
"\n",
"[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
"\n",
"This notebook uses several Python packages that come standard with the Anaconda Python distribution. The primary libraries that we'll be using are:\n",
"\n",
"* **NumPy**: Provides a fast numerical array structure and helper functions.\n",
"* **pandas**: Provides a DataFrame structure to store data in memory and work with it easily and efficiently.\n",
"* **scikit-learn**: The essential Machine Learning package in Python.\n",
"* **matplotlib**: Basic plotting library in Python; most other Python plotting libraries are built on top of it.\n",
"* **Seaborn**: Advanced statistical plotting library.\n",
"* **watermark**: A Jupyter Notebook extension for printing timestamps, version numbers, and hardware information.\n",
"\n",
"To make sure you have all of the packages you need, install them with `conda`:\n",
"\n",
" conda install numpy pandas scikit-learn matplotlib seaborn\n",
" \n",
" conda install -c conda-forge watermark\n",
"\n",
"`conda` may ask you to update some of them if you don't have the most recent version. Allow it to do so.\n",
"\n",
"**Note:** I will not be providing support for people trying to run this notebook outside of the Anaconda Python distribution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The problem domain\n",
"\n",
"[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"For the purposes of this exercise, let's pretend we're working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We're working with a moderately-sized team of data scientists and will be building part of the data analysis pipeline for this app.\n",
"\n",
"We've been tasked by our company's Head of Data Science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone.\n",
"\n",
"\n",
"\n",
"We've been given a [data set](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/raw/master/example-data-science-notebook/iris-data.csv) from our field researchers to develop the demo, which only includes measurements for three types of *Iris* flowers:\n",
"\n",
"### *Iris setosa*\n",
"\n",
"\n",
"\n",
"### *Iris versicolor*\n",
"\n",
"\n",
"### *Iris virginica*\n",
"\n",
"\n",
"The four measurements we're using currently come from hand-measurements by the field researchers, but they will be automatically measured by an image processing model in the future.\n",
"\n",
"**Note:** The data set we're working with is the famous [*Iris* data set](https://archive.ics.uci.edu/ml/datasets/Iris) — included with this notebook — which I have modified slightly for demonstration purposes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Answering the question\n",
"\n",
"[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"The first step to any data analysis project is to define the question or problem we're looking to solve, and to define a measure (or set of measures) for our success at solving that task. The data analysis checklist has us answer a handful of questions to accomplish that, so let's work through those questions.\n",
"\n",
">Did you specify the type of data analytic question (e.g. exploration, association causality) before touching the data?\n",
"\n",
"We're trying to classify the species (i.e., class) of the flower based on four measurements that we're provided: sepal length, sepal width, petal length, and petal width.\n",
"\n",
">Did you define the metric for success before beginning?\n",
"\n",
"Let's do that now. Since we're performing classification, we can use [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision) — the fraction of correctly classified flowers — to quantify how well our model is performing. Our company's Head of Data has told us that we should achieve at least 90% accuracy.\n",
"\n",
">Did you understand the context for the question and the scientific or business application?\n",
"\n",
"We're building part of a data analysis pipeline for a smartphone app that will be able to classify the species of flowers from pictures taken on the smartphone. In the future, this pipeline will be connected to another pipeline that automatically measures from pictures the traits we're using to perform this classification.\n",
"\n",
">Did you record the experimental design?\n",
"\n",
"Our company's Head of Data has told us that the field researchers are hand-measuring 50 randomly-sampled flowers of each species using a standardized methodology. The field researchers take pictures of each flower they sample from pre-defined angles so the measurements and species can be confirmed by the other field researchers at a later point. At the end of each day, the data is compiled and stored on a private company GitHub repository.\n",
"\n",
">Did you consider whether the question could be answered with the available data?\n",
"\n",
"The data set we currently have is only for three types of *Iris* flowers. The model built off of this data set will only work for those *Iris* flowers, so we will need more data to create a general flower classifier.\n",
"\n",
"
\n",
"\n",
"Notice that we've spent a fair amount of time working on the problem without writing a line of code or even looking at the data.\n",
"\n",
"**Thinking about and documenting the problem we're working on is an important step to performing effective data analysis that often goes overlooked.** Don't skip it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Checking the data\n",
"\n",
"[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"The next step is to look at the data we're working with. Even curated data sets from the government can have errors in them, and it's vital that we spot these errors before investing too much time in our analysis.\n",
"\n",
"Generally, we're looking to answer the following questions:\n",
"\n",
"* Is there anything wrong with the data?\n",
"* Are there any quirks with the data?\n",
"* Do I need to fix or remove any of the data?\n",
"\n",
"Let's start by reading the data into a pandas DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
sepal_length_cm
\n",
"
sepal_width_cm
\n",
"
petal_length_cm
\n",
"
petal_width_cm
\n",
"
class
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
5.1
\n",
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3.5
\n",
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1.4
\n",
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0.2
\n",
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Iris-setosa
\n",
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\n",
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\n",
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1
\n",
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4.9
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3.0
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1.4
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0.2
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Iris-setosa
\n",
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\n",
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\n",
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2
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4.7
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3.2
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1.3
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0.2
\n",
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Iris-setosa
\n",
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\n",
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\n",
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3
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4.6
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3.1
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1.5
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0.2
\n",
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Iris-setosa
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4
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5.0
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3.6
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1.4
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0.2
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Iris-setosa
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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",
"4 5.0 3.6 1.4 0.2 \n",
"\n",
" class \n",
"0 Iris-setosa \n",
"1 Iris-setosa \n",
"2 Iris-setosa \n",
"3 Iris-setosa \n",
"4 Iris-setosa "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"iris_data = pd.read_csv('iris-data.csv')\n",
"iris_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're in luck! The data seems to be in a usable format.\n",
"\n",
"The first row in the data file defines the column headers, and the headers are descriptive enough for us to understand what each column represents. The headers even give us the units that the measurements were recorded in, just in case we needed to know at a later point in the project.\n",
"\n",
"Each row following the first row represents an entry for a flower: four measurements and one class, which tells us the species of the flower.\n",
"\n",
"**One of the first things we should look for is missing data.** Thankfully, the field researchers already told us that they put a 'NA' into the spreadsheet when they were missing a measurement.\n",
"\n",
"We can tell pandas to automatically identify missing values if it knows our missing value marker."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"iris_data = pd.read_csv('iris-data.csv', na_values=['NA'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voilà! Now pandas knows to treat rows with 'NA' as missing values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, it's always a good idea to look at the distribution of our data — especially the outliers.\n",
"\n",
"Let's start by printing out some summary statistics about the data set."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
sepal_length_cm
\n",
"
sepal_width_cm
\n",
"
petal_length_cm
\n",
"
petal_width_cm
\n",
"
\n",
" \n",
" \n",
"
\n",
"
count
\n",
"
150.000000
\n",
"
150.000000
\n",
"
150.000000
\n",
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145.000000
\n",
"
\n",
"
\n",
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mean
\n",
"
5.644627
\n",
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3.054667
\n",
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3.758667
\n",
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1.236552
\n",
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\n",
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\n",
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std
\n",
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1.312781
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0.433123
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1.764420
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0.755058
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min
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0.055000
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2.000000
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1.000000
\n",
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0.100000
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\n",
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\n",
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25%
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5.100000
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2.800000
\n",
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1.600000
\n",
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0.400000
\n",
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\n",
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\n",
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50%
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5.700000
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3.000000
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4.350000
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1.300000
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75%
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5.100000
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1.800000
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max
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7.900000
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4.400000
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6.900000
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2.500000
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\n",
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"text/plain": [
" sepal_length_cm sepal_width_cm petal_length_cm petal_width_cm\n",
"count 150.000000 150.000000 150.000000 145.000000\n",
"mean 5.644627 3.054667 3.758667 1.236552\n",
"std 1.312781 0.433123 1.764420 0.755058\n",
"min 0.055000 2.000000 1.000000 0.100000\n",
"25% 5.100000 2.800000 1.600000 0.400000\n",
"50% 5.700000 3.000000 4.350000 1.300000\n",
"75% 6.400000 3.300000 5.100000 1.800000\n",
"max 7.900000 4.400000 6.900000 2.500000"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see several useful values from this table. For example, we see that five `petal_width_cm` entries are missing.\n",
"\n",
"If you ask me, though, tables like this are rarely useful unless we know that our data should fall in a particular range. It's usually better to visualize the data in some way. Visualization makes outliers and errors immediately stand out, whereas they might go unnoticed in a large table of numbers.\n",
"\n",
"Since we know we're going to be plotting in this section, let's set up the notebook so we can plot inside of it."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# This line tells the notebook to show plots inside of the notebook\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's create a **scatterplot matrix**. Scatterplot matrices plot the distribution of each column along the diagonal, and then plot a scatterplot matrix for the combination of each variable. They make for an efficient tool to look for errors in our data.\n",
"\n",
"We can even have the plotting package color each entry by its class to look for trends within the classes."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# We have to temporarily drop the rows with 'NA' values\n",
"# because the Seaborn plotting function does not know\n",
"# what to do with them\n",
"sb.pairplot(iris_data.dropna(), hue='class')\n",
";"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the scatterplot matrix, we can already see some issues with the data set:\n",
"\n",
"1. There are five classes when there should only be three, meaning there were some coding errors.\n",
"\n",
"2. There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\n",
"\n",
"3. We had to drop those rows with missing values.\n",
"\n",
"In all of these cases, we need to figure out what to do with the erroneous data. Which takes us to the next step..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Tidying the data\n",
"\n",
"[[ go back to the top ]](#Table-of-contents)\n",
"\n",
"Now that we've identified several errors in the data set, we need to fix them before we proceed with the analysis.\n",
"\n",
"Let's walk through the issues one-by-one.\n",
"\n",
">There are five classes when there should only be three, meaning there were some coding errors.\n",
"\n",
"After talking with the field researchers, it sounds like one of them forgot to add `Iris-` before their `Iris-versicolor` entries. The other extraneous class, `Iris-setossa`, was simply a typo that they forgot to fix.\n",
"\n",
"Let's use the DataFrame to fix these errors."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'\n",
"iris_data.loc[iris_data['class'] == 'Iris-setossa', 'class'] = 'Iris-setosa'\n",
"\n",
"iris_data['class'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Much better! Now we only have three class types. Imagine how embarrassing it would've been to create a model that used the wrong classes.\n",
"\n",
">There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\n",
"\n",
"Fixing outliers can be tricky business. It's rarely clear whether the outlier was caused by measurement error, recording the data in improper units, or if the outlier is a real anomaly. For that reason, we should be judicious when working with outliers: if we decide to exclude any data, we need to make sure to document what data we excluded and provide solid reasoning for excluding that data. (i.e., \"This data didn't fit my hypothesis\" will not stand peer review.)\n",
"\n",
"In the case of the one anomalous entry for `Iris-setosa`, let's say our field researchers know that it's impossible for `Iris-setosa` to have a sepal width below 2.5 cm. Clearly this entry was made in error, and we're better off just scrapping the entry than spending hours finding out what happened."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# This line drops any 'Iris-setosa' rows with a separal width less than 2.5 cm\n",
"iris_data = iris_data.loc[(iris_data['class'] != 'Iris-setosa') | (iris_data['sepal_width_cm'] >= 2.5)]\n",
"iris_data.loc[iris_data['class'] == 'Iris-setosa', 'sepal_width_cm'].hist()\n",
";"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Excellent! Now all of our `Iris-setosa` rows have a sepal width greater than 2.5.\n",
"\n",
"The next data issue to address is the several near-zero sepal lengths for the `Iris-versicolor` rows. Let's take a look at those rows."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
sepal_length_cm
\n",
"
sepal_width_cm
\n",
"
petal_length_cm
\n",
"
petal_width_cm
\n",
"
class
\n",
"
\n",
" \n",
" \n",
"
\n",
"
77
\n",
"
0.067
\n",
"
3.0
\n",
"
5.0
\n",
"
1.7
\n",
"
Iris-versicolor
\n",
"
\n",
"
\n",
"
78
\n",
"
0.060
\n",
"
2.9
\n",
"
4.5
\n",
"
1.5
\n",
"
Iris-versicolor
\n",
"
\n",
"
\n",
"
79
\n",
"
0.057
\n",
"
2.6
\n",
"
3.5
\n",
"
1.0
\n",
"
Iris-versicolor
\n",
"
\n",
"
\n",
"
80
\n",
"
0.055
\n",
"
2.4
\n",
"
3.8
\n",
"
1.1
\n",
"
Iris-versicolor
\n",
"
\n",
"
\n",
"
81
\n",
"
0.055
\n",
"
2.4
\n",
"
3.7
\n",
"
1.0
\n",
"
Iris-versicolor
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" sepal_length_cm sepal_width_cm petal_length_cm petal_width_cm \\\n",
"77 0.067 3.0 5.0 1.7 \n",
"78 0.060 2.9 4.5 1.5 \n",
"79 0.057 2.6 3.5 1.0 \n",
"80 0.055 2.4 3.8 1.1 \n",
"81 0.055 2.4 3.7 1.0 \n",
"\n",
" class \n",
"77 Iris-versicolor \n",
"78 Iris-versicolor \n",
"79 Iris-versicolor \n",
"80 Iris-versicolor \n",
"81 Iris-versicolor "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\n",
" (iris_data['sepal_length_cm'] < 1.0)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How about that? All of these near-zero `sepal_length_cm` entries seem to be off by two orders of magnitude, as if they had been recorded in meters instead of centimeters.\n",
"\n",
"After some brief correspondence with the field researchers, we find that one of them forgot to convert those measurements to centimeters. Let's do that for them."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\n",
" (iris_data['sepal_length_cm'] < 1.0),\n",
" 'sepal_length_cm'] *= 100.0\n",
"\n",
"iris_data.loc[iris_data['class'] == 'Iris-versicolor', 'sepal_length_cm'].hist()\n",
";"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Phew! Good thing we fixed those outliers. They could've really thrown our analysis off.\n",
"\n",
">We had to drop those rows with missing values.\n",
"\n",
"Let's take a look at the rows with missing values:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
sepal_length_cm
\n",
"
sepal_width_cm
\n",
"
petal_length_cm
\n",
"
petal_width_cm
\n",
"
class
\n",
"
\n",
" \n",
" \n",
"
\n",
"
7
\n",
"
5.0
\n",
"
3.4
\n",
"
1.5
\n",
"
NaN
\n",
"
Iris-setosa
\n",
"
\n",
"
\n",
"
8
\n",
"
4.4
\n",
"
2.9
\n",
"
1.4
\n",
"
NaN
\n",
"
Iris-setosa
\n",
"
\n",
"
\n",
"
9
\n",
"
4.9
\n",
"
3.1
\n",
"
1.5
\n",
"
NaN
\n",
"
Iris-setosa
\n",
"
\n",
"
\n",
"
10
\n",
"
5.4
\n",
"
3.7
\n",
"
1.5
\n",
"
NaN
\n",
"
Iris-setosa
\n",
"
\n",
"
\n",
"
11
\n",
"
4.8
\n",
"
3.4
\n",
"
1.6
\n",
"
NaN
\n",
"
Iris-setosa
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" sepal_length_cm sepal_width_cm petal_length_cm petal_width_cm \\\n",
"7 5.0 3.4 1.5 NaN \n",
"8 4.4 2.9 1.4 NaN \n",
"9 4.9 3.1 1.5 NaN \n",
"10 5.4 3.7 1.5 NaN \n",
"11 4.8 3.4 1.6 NaN \n",
"\n",
" class \n",
"7 Iris-setosa \n",
"8 Iris-setosa \n",
"9 Iris-setosa \n",
"10 Iris-setosa \n",
"11 Iris-setosa "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
" (iris_data['sepal_width_cm'].isnull()) |\n",
" (iris_data['petal_length_cm'].isnull()) |\n",
" (iris_data['petal_width_cm'].isnull())]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's not ideal that we had to drop those rows, especially considering they're all `Iris-setosa` entries. Since it seems like the missing data is systematic — all of the missing values are in the same column for the same *Iris* type — this error could potentially bias our analysis.\n",
"\n",
"One way to deal with missing data is **mean imputation**: If we know that the values for a measurement fall in a certain range, we can fill in empty values with the average of that measurement.\n",
"\n",
"Let's see if we can do that here."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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KMNAlqQgDXZKKMNAlqQgDXZKK+H/pqU7puCtsZQAAAABJRU5ErkJggg==\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].hist()\n",
";"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most of the petal widths for `Iris-setosa` fall within the 0.2-0.3 range, so let's fill in these entries with the average measured petal width."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [sepal_length_cm, sepal_width_cm, petal_length_cm, petal_width_cm, class]\n",
"Index: []"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
" (iris_data['sepal_width_cm'].isnull()) |\n",
" (iris_data['petal_length_cm'].isnull()) |\n",
" (iris_data['petal_width_cm'].isnull())]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! Now we've recovered those rows and no longer have missing data in our data set.\n",
"\n",
"**Note:** If you don't feel comfortable imputing your data, you can drop all rows with missing data with the `dropna()` call:\n",
"\n",
" iris_data.dropna(inplace=True)\n",
"\n",
"After all this hard work, we don't want to repeat this process every time we work with the data set. Let's save the tidied data file *as a separate file* and work directly with that data file from now on."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"iris_data.to_csv('iris-data-clean.csv', index=False)\n",
"\n",
"iris_data_clean = pd.read_csv('iris-data-clean.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's take a look at the scatterplot matrix now that we've tidied the data."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
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