Creating Customer Segments

32 minute read

Machine Learning Engineer Nanodegree

Unsupervised Learning

Project: Creating Customer Segments

Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with ‘Implementation’ in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a ‘Question X’ header. Carefully read each question and provide thorough answers in the following text boxes that begin with ‘Answer:’. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.

Getting Started

In this project, you will analyze a dataset containing data on various customers’ annual spending amounts (reported in monetary units) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.

The dataset for this project can be found on the UCI Machine Learning Repository. For the purposes of this project, the features 'Channel' and 'Region' will be excluded in the analysis — with focus instead on the six product categories recorded for customers.

Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported.

# Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames

# Import supplementary visualizations code
import visuals as vs

# Pretty display for notebooks
%matplotlib inline

# Load the wholesale customers dataset
    data = pd.read_csv("customers.csv")
    data.drop(['Region', 'Channel'], axis = 1, inplace = True)
    print("Wholesale customers dataset has {} samples with {} features each.".format(*data.shape))
    print("Dataset could not be loaded. Is the dataset missing?")
Wholesale customers dataset has 440 samples with 6 features each.

Data Exploration

In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.

Run the code block below to observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: ‘Fresh’, ‘Milk’, ‘Grocery’, ‘Frozen’, ‘Detergents_Paper’, and ‘Delicatessen’. Consider what each category represents in terms of products you could purchase.

# Display a description of the dataset
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
count 440.000000 440.000000 440.000000 440.000000 440.000000 440.000000
mean 12000.297727 5796.265909 7951.277273 3071.931818 2881.493182 1524.870455
std 12647.328865 7380.377175 9503.162829 4854.673333 4767.854448 2820.105937
min 3.000000 55.000000 3.000000 25.000000 3.000000 3.000000
25% 3127.750000 1533.000000 2153.000000 742.250000 256.750000 408.250000
50% 8504.000000 3627.000000 4755.500000 1526.000000 816.500000 965.500000
75% 16933.750000 7190.250000 10655.750000 3554.250000 3922.000000 1820.250000
max 112151.000000 73498.000000 92780.000000 60869.000000 40827.000000 47943.000000

Implementation: Selecting Samples

To get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add three indices of your choice to the indices list which will represent the customers to track. It is suggested to try different sets of samples until you obtain customers that vary significantly from one another.

# TODO: Select three indices of your choice you wish to sample from the dataset
indices = [0,2,6]

# Create a DataFrame of the chosen samples
samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)
print("Chosen samples of wholesale customers dataset: ")
Chosen samples of wholesale customers dataset: 
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
0 12669 9656 7561 214 2674 1338
1 6353 8808 7684 2405 3516 7844
2 12126 3199 6975 480 3140 545
# Import Seaborn, a very powerful library for Data Visualisation
import seaborn as sns
samples_bar = samples.append(data.describe().loc['mean'])
samples_bar.index = indices + ['mean']
_ = samples_bar.plot(kind='bar', figsize=(14,6))


Question 1

Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers.

  • What kind of establishment (customer) could each of the three samples you’ve chosen represent?

Hint: Examples of establishments include places like markets, cafes, delis, wholesale retailers, among many others. Avoid using names for establishments, such as saying “McDonalds” when describing a sample customer as a restaurant. You can use the mean values for reference to compare your samples with. The mean values are as follows:

  • Fresh: 12000.2977
  • Milk: 5796.2
  • Grocery: 7951.3
  • Detergents_paper: 2881.4
  • Delicatessen: 1524.8

Knowing this, how do your samples compare? Does that help in driving your insight into what kind of establishments they might be?


  • index 0 : is having more Fresh and Milk product i think it belongs to the Dairy

  • index 2 : is having everything in average so i think it belogs to wholesale.

  • index 6 : is having Fresh and grocery so i think it is a grocery shop.

Implementation: Feature Relevance

One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.

In the code block below, you will need to implement the following:

  • Assign new_data a copy of the data by removing a feature of your choice using the DataFrame.drop function.
  • Use sklearn.cross_validation.train_test_split to split the dataset into training and testing sets.
    • Use the removed feature as your target label. Set a test_size of 0.25 and set a random_state.
  • Import a decision tree regressor, set a random_state, and fit the learner to the training data.
  • Report the prediction score of the testing set using the regressor’s score function.
# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature
new_data = data.copy()
new_data_X = new_data.drop('Detergents_Paper',axis = 1)
new_data_y = new_data['Detergents_Paper']

# TODO: Split the data into training and testing sets(0.25) using the given feature as the target
# Set a random state.
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split( new_data_X,new_data_y, test_size=0.25, random_state=42)

# TODO: Create a decision tree regressor and fit it to the training set
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(),y_train)

# TODO: Report the score of the prediction using the testing set
score = regressor.score(X_test,y_test)


/home/nihal/anaconda3/lib/python3.6/site-packages/sklearn/ DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)


Question 2

  • Which feature did you attempt to predict?
  • What was the reported prediction score?
  • Is this feature necessary for identifying customers’ spending habits?

Hint: The coefficient of determination, R^2, is scored between 0 and 1, with 1 being a perfect fit. A negative R^2 implies the model fails to fit the data. If you get a low score for a particular feature, that lends us to beleive that that feature point is hard to predict using the other features, thereby making it an important feature to consider when considering relevance.


  • I attempt to predict the ‘Detergents_Paper’ Feature.

  • The prediction score was 0.48990002662382115

  • as the score is near to 0.5 which is half,therefore i think it is not a important feature.

Visualize Feature Distributions

To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. Run the code block below to produce a scatter matrix.

# Produce a scatter matrix for each pair of features in the data
pd.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');
/home/nihal/anaconda3/lib/python3.6/site-packages/ FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead


Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
Fresh 1.000000 0.100510 -0.011854 0.345881 -0.101953 0.244690
Milk 0.100510 1.000000 0.728335 0.123994 0.661816 0.406368
Grocery -0.011854 0.728335 1.000000 -0.040193 0.924641 0.205497
Frozen 0.345881 0.123994 -0.040193 1.000000 -0.131525 0.390947
Detergents_Paper -0.101953 0.661816 0.924641 -0.131525 1.000000 0.069291
Delicatessen 0.244690 0.406368 0.205497 0.390947 0.069291 1.000000
import seaborn as sns; 
sns.heatmap(data.corr(), annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7f7d99452da0>



  • Detergents_Paper and Grocery: We can see a linear correlation here that’s apparently strongly correlated with a coefficient of 0.92.
  • Detergents_Paper and Milk: Another linear correlation, but less clear than DP+Grocery - with a correlation coefficient of 0.66.
  • Grocery and Milk: Another linear correlation with similar strength to DP+Milk of 0.73.

Detergents_Paper has a weak relevance in establishing the profile of the data, which backs up the conclusion from the previous score.

Data Preprocessing

In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful.

Implementation: Feature Scaling

If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most often appropriate to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a Box-Cox test, which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.

In the code block below, you will need to implement the following:

  • Assign a copy of the data to log_data after applying logarithmic scaling. Use the np.log function for this.
  • Assign a copy of the sample data to log_samples after applying logarithmic scaling. Again, use np.log.

Question 3

  • Using the scatter matrix as a reference, discuss the distribution of the dataset, specifically talk about the normality, outliers, large number of data points near 0 among others. If you need to sepearate out some of the plots individually to further accentuate your point, you may do so as well.
  • Are there any pairs of features which exhibit some degree of correlation?
  • Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict?
  • How is the data for those features distributed?

Hint: Is the data normally distributed? Where do most of the data points lie? You can use corr() to get the feature correlations and then visualize them using a heatmap(the data that would be fed into the heatmap would be the correlation values, for eg: data.corr()) to gain further insight.

# TODO: Scale the data using the natural logarithm
log_data = np.log(data)

# TODO: Scale the sample data using the natural logarithm
log_samples = np.log(samples)

# Produce a scatter matrix for each pair of newly-transformed features
pd.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');
/home/nihal/anaconda3/lib/python3.6/site-packages/ FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead



After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).

Run the code below to see how the sample data has changed after having the natural logarithm applied to it.

# Display the log-transformed sample data
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
0 9.446913 9.175335 8.930759 5.365976 7.891331 7.198931
1 8.756682 9.083416 8.946896 7.785305 8.165079 8.967504
2 9.403107 8.070594 8.850088 6.173786 8.051978 6.300786

Implementation: Outlier Detection

Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many “rules of thumb” for what constitutes an outlier in a dataset. Here, we will use Tukey’s Method for identfying outliers: An outlier step is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal.

In the code block below, you will need to implement the following:

  • Assign the value of the 25th percentile for the given feature to Q1. Use np.percentile for this.
  • Assign the value of the 75th percentile for the given feature to Q3. Again, use np.percentile.
  • Assign the calculation of an outlier step for the given feature to step.
  • Optionally remove data points from the dataset by adding indices to the outliers list.

NOTE: If you choose to remove any outliers, ensure that the sample data does not contain any of these points!
Once you have performed this implementation, the dataset will be stored in the variable good_data.

# For each feature find the data points with extreme high or low values
for feature in log_data.keys():
    # TODO: Calculate Q1 (25th percentile of the data) for the given feature
    Q1 = np.percentile(log_data[feature],25.)
    # TODO: Calculate Q3 (75th percentile of the data) for the given feature
    Q3 = np.percentile(log_data[feature],75.)
    # TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)
    step = (Q3-Q1)*1.5
    # Display the outliers
    print("Data points considered outliers for the feature '{}':".format(feature))
    display(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))])
# OPTIONAL: Select the indices for data points you wish to remove
outliers  = []

# Remove the outliers, if any were specified
good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)
Data points considered outliers for the feature 'Fresh':
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
65 4.442651 9.950323 10.732651 3.583519 10.095388 7.260523
66 2.197225 7.335634 8.911530 5.164786 8.151333 3.295837
81 5.389072 9.163249 9.575192 5.645447 8.964184 5.049856
95 1.098612 7.979339 8.740657 6.086775 5.407172 6.563856
96 3.135494 7.869402 9.001839 4.976734 8.262043 5.379897
128 4.941642 9.087834 8.248791 4.955827 6.967909 1.098612
171 5.298317 10.160530 9.894245 6.478510 9.079434 8.740337
193 5.192957 8.156223 9.917982 6.865891 8.633731 6.501290
218 2.890372 8.923191 9.629380 7.158514 8.475746 8.759669
304 5.081404 8.917311 10.117510 6.424869 9.374413 7.787382
305 5.493061 9.468001 9.088399 6.683361 8.271037 5.351858
338 1.098612 5.808142 8.856661 9.655090 2.708050 6.309918
353 4.762174 8.742574 9.961898 5.429346 9.069007 7.013016
355 5.247024 6.588926 7.606885 5.501258 5.214936 4.844187
357 3.610918 7.150701 10.011086 4.919981 8.816853 4.700480
412 4.574711 8.190077 9.425452 4.584967 7.996317 4.127134
Data points considered outliers for the feature 'Milk':
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
86 10.039983 11.205013 10.377047 6.894670 9.906981 6.805723
98 6.220590 4.718499 6.656727 6.796824 4.025352 4.882802
154 6.432940 4.007333 4.919981 4.317488 1.945910 2.079442
356 10.029503 4.897840 5.384495 8.057377 2.197225 6.306275
Data points considered outliers for the feature 'Grocery':
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
75 9.923192 7.036148 1.098612 8.390949 1.098612 6.882437
154 6.432940 4.007333 4.919981 4.317488 1.945910 2.079442
Data points considered outliers for the feature 'Frozen':
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
38 8.431853 9.663261 9.723703 3.496508 8.847360 6.070738
57 8.597297 9.203618 9.257892 3.637586 8.932213 7.156177
65 4.442651 9.950323 10.732651 3.583519 10.095388 7.260523
145 10.000569 9.034080 10.457143 3.737670 9.440738 8.396155
175 7.759187 8.967632 9.382106 3.951244 8.341887 7.436617
264 6.978214 9.177714 9.645041 4.110874 8.696176 7.142827
325 10.395650 9.728181 9.519735 11.016479 7.148346 8.632128
420 8.402007 8.569026 9.490015 3.218876 8.827321 7.239215
429 9.060331 7.467371 8.183118 3.850148 4.430817 7.824446
439 7.932721 7.437206 7.828038 4.174387 6.167516 3.951244
Data points considered outliers for the feature 'Detergents_Paper':
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
75 9.923192 7.036148 1.098612 8.390949 1.098612 6.882437
161 9.428190 6.291569 5.645447 6.995766 1.098612 7.711101
Data points considered outliers for the feature 'Delicatessen':
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
66 2.197225 7.335634 8.911530 5.164786 8.151333 3.295837
109 7.248504 9.724899 10.274568 6.511745 6.728629 1.098612
128 4.941642 9.087834 8.248791 4.955827 6.967909 1.098612
137 8.034955 8.997147 9.021840 6.493754 6.580639 3.583519
142 10.519646 8.875147 9.018332 8.004700 2.995732 1.098612
154 6.432940 4.007333 4.919981 4.317488 1.945910 2.079442
183 10.514529 10.690808 9.911952 10.505999 5.476464 10.777768
184 5.789960 6.822197 8.457443 4.304065 5.811141 2.397895
187 7.798933 8.987447 9.192075 8.743372 8.148735 1.098612
203 6.368187 6.529419 7.703459 6.150603 6.860664 2.890372
233 6.871091 8.513988 8.106515 6.842683 6.013715 1.945910
285 10.602965 6.461468 8.188689 6.948897 6.077642 2.890372
289 10.663966 5.655992 6.154858 7.235619 3.465736 3.091042
343 7.431892 8.848509 10.177932 7.283448 9.646593 3.610918

Question 4

  • Are there any data points considered outliers for more than one feature based on the definition above?
  • Should these data points be removed from the dataset?
  • If any data points were added to the outliers list to be removed, explain why.

** Hint: ** If you have datapoints that are outliers in multiple categories think about why that may be and if they warrant removal. Also note how k-means is affected by outliers and whether or not this plays a factor in your analysis of whether or not to remove them.


There were several outliers which occur in more than one feature :

  • 154: An outlier for Delicatessen, Milk and Grocery.
  • 128: An outlier for Delicatessen and Fresh.
  • 75: An outlier for Detergents_Paper and Grocery.
  • 66: An outlier for Delicatessen and Fresh
  • 65: An outlier for Frozen and Fresh

It seems reasonable to remove these from the overall data. They add no value to any predictive models, and would only skew the results.

Feature Transformation

In this section you will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers.

Implementation: PCA

Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the good_data to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the explained variance ratio of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new “feature” of the space, however it is a composition of the original features present in the data.

In the code block below, you will need to implement the following:

  • Import sklearn.decomposition.PCA and assign the results of fitting PCA in six dimensions with good_data to pca.
  • Apply a PCA transformation of log_samples using pca.transform, and assign the results to pca_samples.
from sklearn.decomposition import PCA
# TODO: Apply PCA by fitting the good data with the same number of dimensions as features
pca = PCA(n_components=6)

# TODO: Transform log_samples using the PCA fit above
pca_samples = pca.transform(log_samples)

# Generate PCA results plot
pca_results = vs.pca_results(good_data, pca)


Question 5

  • How much variance in the data is explained* in total *by the first and second principal component?
  • How much variance in the data is explained by the first four principal components?
  • Using the visualization provided above, talk about each dimension and the cumulative variance explained by each, stressing upon which features are well represented by each dimension(both in terms of positive and negative variance explained). Discuss what the first four dimensions best represent in terms of customer spending.

Hint: A positive increase in a specific dimension corresponds with an increase of the positive-weighted features and a decrease of the negative-weighted features. The rate of increase or decrease is based on the individual feature weights.


First 2 components:

  • 1st PC: 49.9%
  • 2nd PC: 22.6%
  • Total: 72.5%

First 4 components:

  • 3rd PC: 10.5%
  • 4th PC: 9.8%
  • Total: 92.8%

Each component represents different sections of customer spending

1st PC represents a wide variety of the featureset. Most prominently it represents Detergents_Paper, but also provides information Gain for Milk, Grocery and Delicatassen to some extent. However, it badly predicts Fresh and Frozen categories and needs another component to help. This could represent the 'convenience' or 'supermarket' spending category.

2nd PC allows for the recovery of Information Gain for Fresh and Frozen features, and supplements Delicatessen. It provides small gains for Milk and Grocery, and a very small loss of Detergents_Paper. This could represent customers who are in the hospitality or restaurant industry.

3rd PC represents gains for Fresh and Detergents_Paper, and minimal or losses for other categories. This could represent smaller corner shops, with convenience items and small amounts of groceries.

4th PC represents Frozen and Detergents_Paper, and losses for other categories. This could represent bulk buyers of frozen goods, such as fish importers.


Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions. Observe the numerical value for the first four dimensions of the sample points. Consider if this is consistent with your initial interpretation of the sample points.

# Display sample log-data after having a PCA transformation applied
display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))
Dimension 1 Dimension 2 Dimension 3 Dimension 4 Dimension 5 Dimension 6
0 -1.7510 -0.0705 -0.9118 -1.7265 0.2741 0.3984
1 -1.8937 -1.6766 1.3189 -0.4852 -0.3736 0.3284
2 -1.1326 0.2016 -1.3001 -0.6000 -0.4962 -0.0956

Implementation: Dimensionality Reduction

When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the cumulative explained variance ratio is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.

In the code block below, you will need to implement the following:

  • Assign the results of fitting PCA in two dimensions with good_data to pca.
  • Apply a PCA transformation of good_data using pca.transform, and assign the results to reduced_data.
  • Apply a PCA transformation of log_samples using pca.transform, and assign the results to pca_samples.
# TODO: Apply PCA by fitting the good data with only two dimensions
pca = PCA(n_components=2)

# TODO: Transform the good data using the PCA fit above
reduced_data = pca.transform(good_data)

# TODO: Transform log_samples using the PCA fit above
pca_samples = pca.transform(log_samples)

# Create a DataFrame for the reduced data
reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])
vs.pca_results(good_data, pca)
Explained Variance Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
Dimension 1 0.4424 0.1737 -0.3945 -0.4544 0.1722 -0.7455 -0.1494
Dimension 2 0.2766 -0.6851 -0.1624 -0.0694 -0.4877 -0.0419 -0.5097



Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions.

# Display sample log-data after applying PCA transformation in two dimensions
display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))
Dimension 1 Dimension 2
0 -1.7510 -0.0705
1 -1.8937 -1.6766
2 -1.1326 0.2016

Visualizing a Biplot

A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case Dimension 1 and Dimension 2). In addition, the biplot shows the projection of the original features along the components. A biplot can help us interpret the reduced dimensions of the data, and discover relationships between the principal components and original features.

Run the code cell below to produce a biplot of the reduced-dimension data.

# Create a biplot
vs.biplot(good_data, reduced_data, pca)
<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9c68b5f8>



Once we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on 'Milk', 'Grocery' and 'Detergents_Paper', but not so much on the other product categories.

From the biplot, which of the original features are most strongly correlated with the first component? What about those that are associated with the second component? Do these observations agree with the pca_results plot you obtained earlier?


In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale.

Question 6

  • What are the advantages to using a K-Means clustering algorithm?
  • What are the advantages to using a Gaussian Mixture Model clustering algorithm?
  • Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?

** Hint: ** Think about the differences between hard clustering and soft clustering and which would be appropriate for our dataset.


  • K-means clustering algorithm :

    • Easy to implement

    • With a large number of variables, K-Means may be computationally faster than hierarchical clustering (if K is small).

    • k‐Means may produce Hghter clusters than hierarchical clustering.

    • An instance can change cluster (move to another cluster) when the centroids are recomputed.

  • Gaussian Mixture Model :

    • It has many parameters Z, μ, pi, σ, and is a method of ‘soft clustering’.

    • By using Gaussian distributions and probabilities, data points do not necessarilly have to be assigned rigidly, and ones with lower probability could be assigned to multiple clusters at once.

    • It is able to assign non-spherical clusters.

    • It can be used to predict probabilities of events rather than rigid features.

  • Both of the Model have advantages and disadvantages and both will be good in this case , so i am gonna choose KMeans Alorightm because it’s easy to implement and have other advantages listed above.

Implementation: Creating Clusters

Depending on the problem, the number of clusters that you expect to be in the data may already be known. When the number of clusters is not known a priori, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the “goodness” of a clustering by calculating each data point’s silhouette coefficient. The silhouette coefficient for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the mean silhouette coefficient provides for a simple scoring method of a given clustering.

In the code block below, you will need to implement the following:

  • Fit a clustering algorithm to the reduced_data and assign it to clusterer.
  • Predict the cluster for each data point in reduced_data using clusterer.predict and assign them to preds.
  • Find the cluster centers using the algorithm’s respective attribute and assign them to centers.
  • Predict the cluster for each sample data point in pca_samples and assign them sample_preds.
  • Import sklearn.metrics.silhouette_score and calculate the silhouette score of reduced_data against preds.
    • Assign the silhouette score to score and print the result.
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

def useKMeans(k) :
    global preds,centers,sample_preds
    # TODO: Apply your clustering algorithm of choice to the reduced data 
    clusterer = KMeans(n_clusters=k, random_state=0)

    # TODO: Predict the cluster for each data point
    preds = clusterer.predict(reduced_data)

    # TODO: Find the cluster centers
    centers = clusterer.cluster_centers_

    # TODO: Predict the cluster for each transformed sample data point
    sample_preds = clusterer.predict(pca_samples)

    # TODO: Calculate the mean silhouette coefficient for the number of clusters chosen
    score = silhouette_score(reduced_data,preds)

    return score
for i in range(2,16) :
    print("score for "+str(i)+" cluster is : "+str(useKMeans(i)))
score for 2 cluster is : 0.4191660832029231
score for 3 cluster is : 0.3936785258078331
score for 4 cluster is : 0.330208290906621
score for 5 cluster is : 0.3438750594739367
score for 6 cluster is : 0.36017073611258016
score for 7 cluster is : 0.3519140228940458
score for 8 cluster is : 0.3548465923968779
score for 9 cluster is : 0.3596381559358986
score for 10 cluster is : 0.34864549124235267
score for 11 cluster is : 0.3538126560738491
score for 12 cluster is : 0.3392935941442673
score for 13 cluster is : 0.3609942551032537
score for 14 cluster is : 0.37295487333769883
score for 15 cluster is : 0.3610319230880394

Question 7

  • Report the silhouette score for several cluster numbers you tried.
  • Of these, which number of clusters has the best silhouette score?


The silhouette scores for several sizes of clusters are displayed above.

Overall, a Kmeans Clustering with 2 clusters has the best silhouette score.

Cluster Visualization

Once you’ve chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters.

# Display the results of the clustering from implementation
vs.cluster_results(reduced_data, preds, centers, pca_samples)


Implementation: Data Recovery

Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the averages of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster’s center point corresponds to the average customer of that segment. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations.

In the code block below, you will need to implement the following:

  • Apply the inverse transform to centers using pca.inverse_transform and assign the new centers to log_centers.
  • Apply the inverse function of np.log to log_centers using np.exp and assign the true centers to true_centers.
# TODO: Inverse transform the centers
log_centers = pca.inverse_transform(centers)

# TODO: Exponentiate the centers
true_centers = np.exp(log_centers)

# Display the true centers
segments = ['Segment {}'.format(i) for i in range(0,len(centers))]
true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())
true_centers.index = segments
Fresh Milk Grocery Frozen Detergents_Paper Delicatessen
Segment 0 8994.0 1909.0 2366.0 2081.0 290.0 681.0
Segment 1 3570.0 7749.0 12463.0 900.0 4567.0 966.0

Question 8

  • Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project(specifically looking at the mean values for the various feature points). What set of establishments could each of the customer segments represent?

Hint: A customer who is assigned to 'Cluster X' should best identify with the establishments represented by the feature set of 'Segment X'. Think about what each segment represents in terms their values for the feature points chosen. Reference these values with the mean values to get some perspective into what kind of establishment they represent.

            square=True, annot=True, cbar=False)
<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9c00ce80>



  • Cluster/Segment 0: This most likely represents cafes/restaurants serving fresh food due to the strong weight upon the Fresh category. Whilst the volume falls below the overall population mean, it is consistent with the original prediction for what a Restaurant might look like in the Data Explotation section.

  • Cluster/Segment 1: The quantities of Grocery and Milk are predominant here. The Milk and Grocery values in this cluster exceed the overall means observed in the Data Exploration section, which suggests the are bulk distributors or large resellers such as supermarkets.

Question 9

  • For each sample point, which customer segment from* Question 8 *best represents it?
  • Are the predictions for each sample point consistent with this?*

Run the code block below to find which cluster each sample point is predicted to be.

# Display the predictions
for i, pred in enumerate(sample_preds):
    print("Sample point", i, "predicted to be in Cluster", pred)
Sample point 0 predicted to be in Cluster 1
Sample point 1 predicted to be in Cluster 1
Sample point 2 predicted to be in Cluster 1


This could suggest the model did a bad job of characteristing the data. However, I would argue it is just a different - and perhaps more logical - interpretation of the data. The model seems to take the opinion that a customer with a variety of prominent features - Fresh, Milk, Grocery, Frozen etc suggests that it is Cluster 1 (Supermarket or Retailer of some sort). Customers with a particular focus on a single feature - Fresh - are regarded as Cluster 0 (Restaurant/Cafes). This actually seems like a valid interpretation that I could have originally made.


In this final section, you will investigate ways that you can make use of the clustered data. First, you will consider how the different groups of customers, the customer segments, may be affected differently by a specific delivery scheme. Next, you will consider how giving a label to each customer (which segment that customer belongs to) can provide for additional features about the customer data. Finally, you will compare the customer segments to a hidden variable present in the data, to see whether the clustering identified certain relationships.

Question 10

Companies will often run A/B tests when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively.

  • How can the wholesale distributor use the customer segments to determine which customers, if any, would react positively to the change in delivery service?*

Hint: Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?


  • The model has established two main customer types - Cluster 1 ‘supermarkets’/’bulk distributors’ (who stock lots of different items) and Cluster 0 ‘restaurants/cafes’ who stock fresh food.

  • It is likely that customers from Cluster 0 who serve lots of fresh food are going to want 5-day weeks in order to keep food as fresh as possible

  • Cluster 1 could be more flexible - they buy a more wide variety of perishable and non-perishable goods so do not necessarilly need a daily delivery.

With this in mind, the Company could run A/B tests and generalize. By picking a subset customers from each Cluster, they can evaluate feedback seperately. It could be established whether changing the delivery service is critical to each segment, and whether customers are happy with the change.

If a trend is found in a particular cluster, it allows a business to make educated and targeted decisions that would benefit their customers going forward depending on their profile. This is as opposed to which would generalize the entire customer-base.

Question 11

Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a customer segment it best identifies with (depending on the clustering algorithm applied), we can consider ‘customer segment’ as an engineered feature for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a customer segment to determine the most appropriate delivery service.

  • How can the wholesale distributor label the new customers using only their estimated product spending and the customer segment data?

Hint: A supervised learner could be used to train on the original customers. What would be the target variable?


We can use semi-supervised techniques to classify new customers:

  • By first running an unsupervised clustering approach, such as KMeans, we first establish clusters and use this as a new feature - which cluster they are in. We can call this feature ‘Customer Segment’, and they could be assigned abritrary enumerated values e.g. 0 and 1 for this worksheet.
  • We’d then create new data points for each new customer, with all of their spending estimates. We can then use a Supervised learning technique, for example a Support Vector Machine (which does very well to seperate classified clusters) with a target variable of ‘Customer Segment’
  • Standard Supervised Learning optimizations could be used to tune the model - boosting, cross-validation etc

Visualizing Underlying Distributions

At the beginning of this project, it was discussed that the 'Channel' and 'Region' features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the 'Channel' feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.

Run the code block below to see how each data point is labeled either 'HoReCa' (Hotel/Restaurant/Cafe) or 'Retail' the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling.

# Display the clustering results based on 'Channel' data
vs.channel_results(reduced_data, outliers, pca_samples)


Question 12

  • How well does the clustering algorithm and number of clusters you’ve chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers?
  • Are there customer segments that would be classified as purely ‘Retailers’ or ‘Hotels/Restaurants/Cafes’ by this distribution?
  • Would you consider these classifications as consistent with your previous definition of the customer segments?


  • The actual data appears to correlate very strongly with our predicted clusters earlier. It shows that the KMeans clustering was able to establish the key relationships very well. It wasn’t able to capture some of the more anamolous data points - particularly Retailers lying within the Hotel/Restaurant/Cafe cluster.

  • The actual distribution has a less well defined seperation between clusters, but it can be stated with reasonable confidence that datapoints with a very positive 1st PC (4<) and 2nd PC (2<) are most certainly a Retailer. Data points with a very negative 1st PC (<-2) and 2nd PC (<-1)

  • Yes, they are almost exactly the guesses I made regarding their classification - Cluster 0 I thought to be Restaurants/Cafes (I didn’t consider hotels) and Cluster 1 being Bulk Distributor or Supermarkets, which is analagous to retailers.

Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to
File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

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