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.
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.
In [1]:
# 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 visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the wholesale customers dataset
try:
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)
except:
print"Dataset could not be loaded. Is the dataset missing?"
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.
In [2]:
# Display a description of the dataset
display(data.describe())
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.
In [3]:
# TODO: Select three indices of your choice you wish to sample from the dataset
indices = []
print data[(data['Fresh'] >= 12647) & (data['Fresh'] <= 1.1*12647)].index
print data[(data['Milk'] >= 7380) & (data['Milk'] <= 1.1*7380)].index
print data[(data['Delicatessen'] >= 2820) & (data['Delicatessen'] <= 1.1*2820)].index
#indices = [181, 85, 183]
indices = [11, 137, 39]
# Create a DataFrame of the chosen samples
samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)
#samples = pd.DataFrame(data.loc[indices], columns = data.keys())
print "Chosen samples of wholesale customers dataset:"
display(samples)
display(data.describe()) # just to confirm nothing changed with the original
print "\nComparison with Mean--"
display(samples - np.round(data.mean()))
print "\nComparison with Median--"
display(samples - np.round(data.median()))
print "\nComparison with 75th percentile--"
display(samples - np.round(data.quantile(q=0.75)))
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, and retailers, among many others. Avoid using names for establishments, such as saying "McDonalds" when describing a sample customer as a restaurant.
Answer:
See display statements for comparison of samples spendings vis-a-vis full data stats.
Sample0 spends the median amount on Fresh and closer to the 25% on other items. It could be a small cafe.
Sample1 spends near the median on Milk and above 50% on Groceries. This could be a retailer.
Sample2 spends near median of Deli and above 75% on Milk. This could be a restaurant.
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:
new_data a copy of the data by removing a feature of your choice using the DataFrame.drop function.sklearn.cross_validation.train_test_split to split the dataset into training and testing sets.test_size of 0.25 and set a random_state.random_state, and fit the learner to the training data.score function.
In [4]:
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature
new_data = data.copy()
droppedFeat = 'Detergents_Paper'
new_data.drop([droppedFeat], axis = 1, inplace = True)
labels = data.loc[:,[droppedFeat]]
#labels = data[droppedFeat]
print new_data.head()
print labels.head()
# TODO: Split the data into training and testing sets using the given feature as the target
X_train, X_test, y_train, y_test = train_test_split(new_data, labels, test_size=0.25, random_state=3)
display(X_train.describe())
display(X_test.describe())
# TODO: Create a decision tree regressor and fit it to the training set
regressor = DecisionTreeRegressor(random_state=0)
regressor.fit(X_train, y_train)
# TODO: Report the score of the prediction using the testing set
score = regressor.score(X_test, y_test)
print score
zip(new_data, regressor.feature_importances_)
Out[4]:
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.
Answer:
Delicatessen was the label I tried to predict. The R^2 score was negative, implying that the model is unable to predict. Hence this feature is necessary; removing it will be the total opposite of information gain; we will lose information.
Detergents_Paper was also selected as label to predict. R^2 score was 0.75 implying the regressor was quite good. So this feature can probably be projected onto a latent feature.
On reviewing the feature_importances of the regressor it can be seen that Grocery having a score of 0.88 can be used the best to derive Detergent_Paper.
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.
In [5]:
# Produce a scatter matrix for each pair of features in the data
#pd.plotting.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');
axes = pd.plotting.scatter_matrix(data, alpha = 0.3, figsize = (16,10), diagonal = 'kde')
corr = data.corr().as_matrix()
for i, j in zip(*np.triu_indices_from(axes, k=1)):
axes[i, j].annotate("%.3f" %corr[i,j], (0.8, 0.8), xycoords='axes fraction', ha='center', va='center')
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?
Answer:
Milk and Grocery is one pair that show some correlation. So does Detergents_Paper and Grocery.
The matrix for Delicatessen - almost no correlation with all others. The data points are all concentrated along the X-axis. This confirms the inability of the regressor to predict Delicatessen as a function of the other features.
In the scatter matrix, I have plotted the correlation also. The following pairs are highly correlated (>0.50)
Milk-Grocery; Milk-D_P; Grocery-D_P; Fresh, Frozen and Deli aren't well correlated with any other features.
Regarding data distribution of each feature -- Below, the boxplots for the data are plotted. It can be seen that for all features, the median is closer to the first quartile and the tail of the distribution is longer on the right side than left. Hence it can be concluded that the data is skewed right. The histograms in the diagonal of the scatter plot above also infers the same conclusion.
In [6]:
color = dict(boxes='Green', whiskers='DarkOrange', medians='Black', caps='Yellow')
data.plot.box(color=color, sym='r+', vert=False)
Out[6]:
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.
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:
log_data after applying logarithmic scaling. Use the np.log function for this.log_samples after applying logarithmic scaling. Again, use np.log.
In [7]:
from scipy import stats
# 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.plotting.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');
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.
In [8]:
# Display the log-transformed sample data
display(log_samples)
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:
Q1. Use np.percentile for this.Q3. Again, use np.percentile.step.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.
In [9]:
# For each feature find the data points with extreme high or low values
import collections
dupCnt = collections.Counter()
outlierIndices = []
concatFrList = []
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)
print "Q1={}".format(Q1)
# TODO: Calculate Q3 (75th percentile of the data) for the given feature
Q3 = np.percentile(log_data[feature], 75)
print "Q3={}".format(Q3)
# TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)
step = (Q3 - Q1)*1.5
print "step={}".format(step)
# 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))])
concatFrList.append(list(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))].index.values))
print concatFrList
tempList = []
for featList in concatFrList:
for x in featList:
tempList.append(x)
print "Number of possible outliers", len(tempList)
print collections.Counter(tempList).most_common()
# 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)
display(good_data.describe()) # just to confirm nothing has changed
Answer:
I used the collections.Counter feature and here are the data points that are considered outliers for more than one feature-- #154 (3x), #128, #65, #66 and #75 (2x). There are several more for one feature only. Since there are six features, I wasn't comfortable to remove all these; So I plan to increase step size by 1.9x instead of 1.5x of IQR and arrive at list of 'outliers'.
In [10]:
# For each feature find the data points with extreme high or low values
import collections
dupCnt = collections.Counter()
outlierIndices = []
concatFrList = []
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)
print "Q1={}".format(Q1)
# TODO: Calculate Q3 (75th percentile of the data) for the given feature
Q3 = np.percentile(log_data[feature], 75)
print "Q3={}".format(Q3)
# TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)
step = (Q3 - Q1)*1.9
print "step={}".format(step)
# 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))])
concatFrList.append(list(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))].index.values))
print concatFrList
tempList = []
for featList in concatFrList:
for x in featList:
tempList.append(x)
if x in indices:
print "ERROR ERROR: outlier is in sample list! Index:", x
print collections.Counter(tempList).most_common()
# OPTIONAL: Select the indices for data points you wish to remove
outliers = tempList
print "Number of outliers", len(outliers)
# Remove the outliers, if any were specified
good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)
display(good_data.describe()) # just to confirm reduced size
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.
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:
sklearn.decomposition.PCA and assign the results of fitting PCA in six dimensions with good_data to pca.log_samples using pca.transform, and assign the results to pca_samples.
In [11]:
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).fit(good_data)
# TODO: Transform log_samples using the PCA fit above
pca_samples = pca.transform(log_samples)
print "cumsum for explained variance", np.cumsum(pca.explained_variance_ratio_)
# Generate PCA results plot
pca_results = vs.pca_results(good_data, pca)
How much variance in the data is explained in total by the first and second principal component? What about the first four principal components? Using the visualization provided above, 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.
Answer: 0.7298 is the variance explained by the first two PCs put together. 0.9329 is the variance explained by the first four PCs put together. And it is 1.0 for all six PCs put together, obviously!
D1 -- The weights of Milk, Grocery and Det_paper are high. This indicates two things - 1. these three are highly correlated with each other; 2. The variance of these three are correlated with this Dimension 1. We can conclude that D1 represents customer spending on Milk, Grocery and Det_paper. Supermarkets or retaiers would be an example of these customers.
D2 represents customer spending on the remaining three namely Fresh, Frozen and Deli. Similar logic that was applied to D1 can be applied to D2, for the other three features. Restaurants would be an example of these customers.
D3 -- in this dimension, Fresh vs Frozen (or Deli) have opposite signs - this D3 captures customers who, if they spend more on Frozen and/or Deli, they would spend less on Fresh. Alternatively, if a customer spends more on Fresh, they would spend less on Frozen/Deli. So this D3 captures customers different than D2. These could be specific type of restaurants that offer 'organic' recipes rather than processed food recipes.
D4 -- we see negative correlation betw Deli and Det_paper/Frozen. This dimension could represent hotels that need cleaning supplies but not necessarily deli prepared food.
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.
In [12]:
# Display sample log-data after having a PCA transformation applied
display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))
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:
good_data to pca.good_data using pca.transform, and assign the results to reduced_data.log_samples using pca.transform, and assign the results to pca_samples.
In [13]:
# TODO: Apply PCA by fitting the good data with only two dimensions
pca = PCA(n_components=2).fit(good_data)
# 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'])
In [14]:
# Display sample log-data after applying PCA transformation in two dimensions
display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))
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.
In [15]:
# Create a biplot
vs.biplot(good_data, reduced_data, pca)
Out[15]:
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?
My observation: With the 1st component labeled as Dimension1, Milk, Det_p and Grocery, which are "along" the x-axis are strongly correlated. Wth Dimension2, Fresh, Frozen and Deli are strongly correlated. And Yes, the biplot does agree with the vs.pca_results bar chart.
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.
Answer: Advantage of using K-Means - faster than GMM.
With GMM - soft assignment of datapoints to clusters is the biggest advantage. GMM works with probabilities of a sample belonging to each cluster. As the model iterates, these probabbilities are refined. GMM works better than KMeans on non-linear data. KMeans uses Euclidian distance which can cause unequal weighting of underlying factors, GMM uses weighted distance.
GMM works well if the data points are distributed in a Gaussian manner. This is as per the doc in sklearn. Below I have plotted the histogram of good_data. It can be seen that all features (except to some extent Det_Paper) exhibit a normal distribution. Hence I have chosen to go with GMM even though it is slower than KMeans. Given our size of data, GMM should be ok.
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:
reduced_data and assign it to clusterer.reduced_data using clusterer.predict and assign them to preds.centers.pca_samples and assign them sample_preds.sklearn.metrics.silhouette_score and calculate the silhouette score of reduced_data against preds.score and print the result.
In [16]:
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.mixture import GaussianMixture
clustererChoice = "GMM"
for i in range(14, 1, -1):
# TODO: Apply your clustering algorithm of choice to the reduced data
clusterer = KMeans(n_clusters=i, n_init=10, random_state=1).fit(reduced_data)
# TODO: Predict the cluster for each data point
preds = clusterer.predict(reduced_data)
#print preds
# 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)
print "KMeans num clusters=", i, score
GMMclusterer = GaussianMixture(n_components=i, n_init=10, random_state=1).fit(reduced_data)
GMMpreds = GMMclusterer.predict(reduced_data)
GMMcenters = GMMclusterer.means_
GMMsample_preds = GMMclusterer.predict(pca_samples)
GMMscore = silhouette_score(reduced_data, GMMpreds)
print "GMM num clusters=", i, GMMscore
#Final choice
clusterer = KMeans(n_clusters=2, n_init=10, random_state=1).fit(reduced_data)
preds = clusterer.predict(reduced_data)
centers = clusterer.cluster_centers_
sample_preds = clusterer.predict(pca_samples)
score = silhouette_score(reduced_data, preds)
print "Final choice is KMeans n_components = 2", "score =", score
Answer: Best silhoutte score is for KMeans 2 clusters. score = 0.4417
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.
In [17]:
# Display the results of the clustering from implementation
vs.cluster_results(reduced_data, preds, centers, pca_samples)
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:
centers using pca.inverse_transform and assign the new centers to log_centers.np.log to log_centers using np.exp and assign the true centers to true_centers.
In [18]:
# 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
display(true_centers)
true_centers=true_centers.append(data.describe().loc['mean'])
true_centers=true_centers.append(data.describe().loc['std'])
true_centers=true_centers.append(data.describe().loc['50%'])
true_centers.plot(kind = 'bar', figsize = (16, 6))
Out[18]:
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. 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'.
Answer:
Sg0_Fresh ~= Fresh_50% <---
Sg0_Milk << Milk_Mean < Milk_Median
Sg0_Grocery << Groc_Mean ~= Groc_Median
Sg0_Froz ~=Froz_50% <---
Sg0_Dp << Dp_Mean < Dp_Med
Sg0_Deli ~= Deli_50% <---
Sg1_Fresh << Fresh_Mean < Fresh_Median
Sg1_Milk > Milk_Mean ~= Milk_Median <---
Sg1_Grocery >> Groc_Mean ~= Groc_Median <---
Sg1_Froz << Froz_Mean < Froz_Med
Sg1_Dp >> Dp_Mean < Dp_Med <---
Sg1_Deli ~= Deli_Mean and less than Deli_Med
Customers assigned to Segment0 are characterized by focused/combined spending on Fresh, Frozen and Deli. (Eg, restaurants).
Customers assigned to Segment1 are characterized by focused/combined spending on Grocery, Milk and Det_paper. (Eg Retailers)
In [19]:
# Display the predictions
for i, pred in enumerate(sample_preds):
print "Sample point", i, "predicted to be in Cluster", pred
print 'The distance between sample point {} and center of cluster {}:'.format(i, pred)
print (samples.iloc[i] - true_centers.iloc[pred])
Answer:
Sample0 - Quite close to the cluster0 center for Frozen and Deli; Close to the median Fresh spending, and higher than the cluster0 center for Fresh; so to predict as cluster0 is reasonable.
Sample1 - Very close to cluster center for Milk. Quite far from Deli; these two justify cluster1 prediction.
Sample2 - Seems too far from cluster0 center for Milk. Milk Groc and Det_pap are quite close to cluster0 center. This could have been classified as cluster1; borderline perhaps.
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.
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?
Answer: The wholesale distributor can construct an A/B test as follows-
To create the pairs of customers for experiments, one could perhaps cluster the unlabeled data into 3 clusters instead of two, and run the A/B test.
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?
Answer: A DecisionTreeClassifier can be built using the customers.csv with one added column namely "Segment" which takes the value 0 or 1. This "Segment" is the targer variable. Then, when the new customers come in, the DecisionTreeClassifier model can be run to predict whether they are retail type or they are restaurant type.
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.
In [20]:
# Display the clustering results based on 'Channel' data
vs.channel_results(reduced_data, outliers, pca_samples)
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?
Answer: From the viz for "underlying distributions" it can be seen that there is a left-right distribution of retail-horeca. This is quite similar to the what the clustering model has inferred. There are a bunch of data points that are actually horeca but predicted to be retailers by the clustering algorithm
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.