# Python: K Means Cluster

K Means Cluster will be our introduction to Unsupervised Machine Learning. What is Unsupervised Machine Learning exactly? Well, the simplest explanation I can offer is that unlike supervised where our data set contains a result, unsupervised does not.

Think of a simple regression where I have the square footage and selling prices (result) of 100 houses. Taking that data, I can easily create a prediction model that will predict the selling price of a house based off of square footage. – This is supervised machine learning

Now, take a data set containing 100 houses with the following data: square footage, house style, garage/no garage, but no selling price. We can’t create a prediction model since we have no knowledge of prices, but we can group the houses together based on commonalities. These groupings (clusters) can be used to gain knowledge of your data set.

I think seeing it in action will help.

If you want to play along, download the data set here: KMeans1

The data set contains a 1 year repair history of 197 Ultrasound medical devices.

Data dictionary (ID Tag – asset number assigned device, Model – model name of device, WO Count – count of repair work orders, AVG Labor – average labor minutes per repair, Labor Cost – average labor cost per repair, No Problem-  count of repairs where no problem was found, Avg Cost -average cost of parts, Travel – average travel hours per repair, Travel Cost – average travel cost per repair, Department – department that owns the ultrasound device) We want to see what kind of information we can extract from this data.

To do so, we are going to use K Means Clustering.

How does K Means Clustering work? Each row in the table is converted to a vector. Imagine the vectors now graphed in N-dimension space. Next pick the number of clusters you want to create. For each cluster, you will place a  point(a centroid) in space and the vectors are grouped based on their proximity to their nearest centroid.

The calculation to tell proximity is made using geometric means (not arithmetic)- hence the name K-Means Cluster

(each dot below is a row in your table, the colors represent a cluster) ## Let’s do it in Python

Import the data.

```import pandas as pd Now, we are going to drop a few columns: ID Tag – is a random number, has no value in clustering. Then Model and Department,as they are text and while there are ways to work with the text, it is more complicated so for now, we are just going to drop the columns

```df1 = df.drop(["ID Tag", "Model", "Department"], axis = 1) Now lets import KMeans from sklearn.cluster

We then initialize KMeans (n_clusters= 4 -no of clusters you want, init=’k-means++’ -sets how the centroids are places. k-means++ is one of the faster methods of centroid placement, n_init=10 – number times the algorithm with run placing new centroids each iteration)

```from sklearn.cluster import KMeans
km = KMeans(n_clusters=4, init='k-means++', n_init=10)``` Choosing number of clusters is a bit of an art. Play with it a bit and see how different values play out for you.

Now fit the model

`km.fit(df1)` Now, export the cluster identifiers to a list. Notice my values are 0 -3. One value for each cluster.

```x = km.fit_predict(df1)
x``` Create a new column on the original dataframe called Cluster and place your results (x) in that column

```df["Cluster"]= x ```df1 = df.sort(['Cluster'])
df1``` Now as you start to examine the data in each cluster, you show start to see patterns emerge.

Below is an example of the patterns I found in the clusters. Now remember, this is just an INTRODUCTION to unsupervised learning. We will learn more tricks to help you discover the patterns as we move forward.

# Python: K Nearest Neighbor

K Nearest Neighbor (Knn) is a classification algorithm. It falls under the category of supervised machine learning. It is supervised machine learning because the data set we are using to “train” with contains results (outcomes). It is easier to show you what I mean.

Here is our training set: logi

Let’s import our set into Python This data set contains 42 student test score (Score) and whether or not they were accepted (Accepted) in a college program.  It is the presence of the Accepted column that makes supervised machine learning possible. Knowing the outcomes of past events, we can create a prediction model for future events. So you could use the finished model to predict whether someone will be accepted based on their test score.

### So how does Knn work?

Look at the chart below. Imagine this represents our data set. Each blue dot is accepted (1) while each red dot is not(0). What if I want to know about my new data point (green star)? Is it a 1 or a 0? I start by choosing a neighbor count – in this example I will choose 3, and I find the 3 nearest neighbors to my new point.

Let’s look at the results, I have 2 red(0) and 1 blue(1). Using basic probability, I am 67% (2/3) certain that you will not get in. ## Now, let’s code it!

First we need to separate our data into 2 dataframes: Our training set X (Score) and our target set y (Accepted)

df.pop() removes the Accepted column from your dataframe and places it in a newly created one.  ### Import sklearn

sklearn is a massive library of machine learning algorithms available for Python. Today we are going to use KNeighborsClassfier

So below imported KNeighborsClassifier from sklearn.neighbors

Next I set my neighbor count to 5. You can experiment with other numbers and see how works out for you. Setting the neighbor count is something you kind of have to develop a feel for. Now let’s fit the model with our training set(X) and target set(y) Now we can use our model to make predictions.

ne.predict() will return 1 or 0 – (Accepted or Not)

while ne.predict_proba() will return a probability range. Results below read as (40% change of not Accepted(0), 60% chance of Accepted(1)) So there you go, you have now built a prediction model using K Nearest Neighbor.

# Logistic Regression with Gretl

One of the most popular machine learning algorithms, Logistic Regression is actually a classification algorithm. Broken down to its simplest terms, binary logistic regression (the one we will be focusing on here) is answering a yes or no question. Will the customer buy or not? Is the email SPAM or not?

## Score Accepted 982          0 1304         1 1256         1 1562         1 703          0

Above is a small sample from the data set we will be using for this lesson. In this set, student scores for an entrance test are listed in the first column and whether they were Accepted (1) or Not(0) is in the second column.

I ran a scatter plot on the data with Scores on the X axis. As you can see the dots for 2 horizontal lines at 1 and 0. You may notice that the 1 (Accepted) dots seem to cluster towards higher scores and 0 (Not Accepted) dots cluster towards lower scores. Well since the point of Logistic Regression is help us make predictions, here is how the predictions work. The Logistic Regression, represented by my crudely drawn red S, goes from 1 to 0. And just like with Linear Regression, if we take a value for X, to make our prediction, we look for the value of Y on the line at that point. In the case of a 1200 score, if we check the value of Y on the line, we get .80. This roughly translates to mean, that with a score of 1200, a student has an 80% chance of being accepted.

## Let’s meet Gretl

While there are third party add-ons you can download for Excel that will do Logistical Regression, in its native form, Excel does not do a good job in this area. So I thought this would be a great opportunity to introduce you to a neat piece of FREE software called Gretl.

So why Gretl? Why not R or Python? I mean those are the languages real data scientists use right?

That is true, and R and Python can easily do a Logical Regression. The problem is  however, in order to use R and Python, you need to know how to program. Gretl, on the other hand, is GUI based. Think of it as a point and click light weight R. It is no where near as robust as R, but for learning how to do Logistical Regression, Gretl does a fine job.

After you install and start Gretl, the next step is to load in the data. Go to File>Open Data>User File. Search for the Excel file you downloaded previously in this lesson. Make sure you then select Excel from the file type at the bottom of the screen. Select logi.xlsx. Leave the Start Import at window at 1 and 1. This is where the data starts in our Excel file: 1rst column, 1rst row. You will get a message letting you know how much data was imported.

The next pop up will noted that the data is undated. Click No on this window. You data columns (Score, Passed) will appear in the  Gretl window. If you click on one, the data from that column will appear in a pop-up window. **note in the file you download, column 2 will be Accepted not Passed.

## Let’s Model

Without further ado, let us do some modelings. From the menu bar Model>Limited dependent variable>Logit>Binary… Now you have to select you Dependent variable and Regressors. Here is a hint, the dependent variable is what we want to find. What are we looking for? Will the person be Accepted. So Accepted goes in Depentdent variable and Score goes in Regressors. Pick the Show p-values radio button and then click Okay. Below are the results of your Logistic Regression model

I am not going to give a Stats lesson here, but I will cover the important points. 1. The top red box contains some important information. First the coefficients represent the b and m values from the linear equation we will be using later: y=mX+b =y=0.0105216X + -11.2757
2. The p-value of Score = 0.0009 This is important as the p-value is a probabilistic value  that determines whether or not the regressor variable truly affects the dependent variable. The most common p-value threshold you are likely to come across is 0.05. If your regressor variable has a p-value above 0.05, you will want to reconsider your model.
3. The matrix at the bottom of the screen. This shows you how successfully your model predicted outcomes from the training data set. It translates of the 0’s (not accepted) the model got 19 out of 21 right. For 1’s(accepted) the model got 19 out of 21 right. That is a 90% success rate. Not bad.

## Let’s Use the Model

Okay, so maybe you jumped ahead and tried 1200 in the linear formula we developed above. It is 1.325?? How is that? Isn’t this supposed to be between 0 and 1.

Well the problem is, we are not looking for Y we are looking for probability (p). Y in this case is not the Y intercept, but instead: Well, we know Y = 1.325 for a score of 1200, how do we find p from that? We solve  for p. Now feel free to go and do the math yourself if you want, but I already did the work for you. The equation below solves for p. If you don’t trust me and want to do it yourself, be my guest, but I assure you the equation below is good. ## Let’s Make a Prediction

Let us put the formula’s we have found into Excel Now you have a working prediction model. Any value you place in the score cell will be calculated to Y and p (probability). As the example above shows, a score of 1200 give us a probability of .79.

Turns out my crummy drawing wasn’t so bad after all. # Linear Regression using Excel

Link to video on Linear Regression using Excel

Regression Analysis is still the most popular method used in Predictive Analytics. The main reason is that it works. It is well known and understood. With its different flavors, regression analysis covers a width swath of problems. Another great reason to use it, is that regression tools are easy to find.

## What is Linear Regression?

Linear Regression is a method of statistical modeling where the value of a dependent variable based can be found calculated based on the value of one or more independent variables. The general idea, as seen in the picture below, is finding a line of best fit through the data. Using that line, you can then predict the value of Y given X. I am not going to go too deep into the math here. I highly the Khan Academy video posted below if you are looking to brush up on your statistics.

## Lets Start by Looking at the Data

If you download the Excel file at the top of the page, you will find 2 columns labeled Years and Salary. This example data set shows us the years of service and salary of 39 employees for an imaginary company. What we are going to attempt to do is to develop a model using Linear Regression that will allow us to predict the salary of an employee given their years of service.

## Step 1: Build a Scatter Plot

The first thing we want to do is build a scatter plot. Excel makes this simple enough. Just highlight all of your data > select the Insert Tab from the Ribbon > Select Scatter from Charts: What you will get should look something like this: We have a scatter chart with Salary on the Y Axis and Years on the X Axis. **Excel scatter charts set the left most column of the data set to the X Axis by default.

Before we move on, I want to take a moment to look at the scatter plot. Do you see a pattern? Can you see where you might be able to draw a line through the data?

I am not trying to just fill space here. I am asking a serious question. Because the answer is sometimes you will not see a pattern. Sometimes the scattering of data will be so random that there will no need to go forward with a linear regression. Learning to look for patterns in data visualizations is skill worth developing. In this example there is a general pattern, or more accurately, we see what looks like Positive Correlation. We call it positive because it appears that as X increases so does Y. So now that our scatter chart has passed the visual test, it is time perform our regression.

## Trend Line

Performing a simple linear regression in Excel is ridiculously easy. Simply click on your scatter plot > from the Ribbon select Chart Tools – Design > Add Chart Element > Trendline > Linear Your trendline appears on your chart. I personally find the line a little hard to see as is, so I am going to format it a bit. Start by double clicking on the trendline and the Format Trendline window will open on the right.

Line: — Color: Red  — Width: 3pt  — Dash type: Solid Line

Trendline Options — Select Display Equation on chart and Display R-squared value on chart Alright, that line is much easier to read. Now let us talk about the numbers in the circle. Now I know I said I was not going to get too deep into the math, but I feel I can’t do this subject justice without at least a cursory explanation of what is going on. What exactly did Excel do when it added the trendline? Technically it performed a statistical function known as Ordinary Least Squares.  What does that mean? Well if you wanted to attempt this by hand, one approach you could take would be to start by drawing a line that looked best to you. You would then measure the Residuals (the distance from the actual data points and line you drew) You then repeat the process (picking a new line and measuring residuals) until you find the line that results in the lowest overall residual.Once you have it, you get the equation for your line:  y = 1357.9x+50974 (Luckily for us Excel makes the process a lot easier)

Now a quick refresher on the line formula: Y= mX + b (where m = Slope and b = Y-Intercept). This equation is what you would use to make predictions. In our equation a person with 0 years in service would have a salary of 50974: Y = 1357.9(0) + 50974 — Y= 50974. And each year of service would add 1357.90 to the salary.

Before we go start using your equation to start making predictions, we still need to discuss the R² you see below your line equation. I won’t bore you with how R² is calculated. You don’t really need to know how it is calculated to use linear regression, but you do need to know how to read it.

The simplest explanation I can give you for R² is that a value of 1 means perfect fit – every point in your data matches up to your line. 0 on the other hand, means your line doesn’t match anything. Our R² is 0.4423, which really is not that great. I generally prefer to aim for a R² value above 0.6.

How can we improve our R² value? My preference would be to get more data. We currently only have 39 tuples. More data could improve our accuracy. If more data is not available though, you can look at your outliers as Linear Regression can be greatly affected by outliers. Unfortunately outliers are often tricky to deal with. A person with 1 year of service making 100,000 a year would definitely be an outlier, but it is not an impossibility. If this employee is a highly experienced individual who just transferred from another company, it is totally feasible they could be earning 100,000.

The hard truth is, considering only the data we have, we cannot rightfully develop a reliable model. This happens more often than you might think. That is okay though, we will chalk this up as a learning experience and move on.