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 [27]:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
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 [28]:
# 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 [29]:
# TODO: Select three indices of your choice you wish to sample from the dataset
indices = [22, 165, 380]
# 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:"
display(samples)
import seaborn as sns
sns.heatmap((samples-data.mean())/data.std(ddof=0), annot=True, cbar=False, square=True)
Out[29]:
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: By looking at the data set above of 3 samples,
Sample 0 customer buys a lot of Fresh and Frozen products as compared to others. This might represent a big retailer.
Sample 1 customer buys Milk, Grocery and Detergents and Paper. This kind will likely represent a restaurant.
Sample 2 customer buys Fresh products more than anyone. This one also can represent a market/retailer dealing in fresh products mostly.
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 [30]:
# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature
new_data = data.drop(['Delicatessen'], axis=1)
test_label = data['Delicatessen']
from sklearn.cross_validation import train_test_split
# 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, test_label, test_size=0.25, random_state=2)
from sklearn.tree import DecisionTreeClassifier
# TODO: Create a decision tree regressor and fit it to the training set
regressor = DecisionTreeClassifier(random_state=2).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
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: I attempted to predict 'Delicatessen'.
The prediction score given by Decision tree is 0.00909
Since the score predicted is very less if we remove this feature, it means by dropping this feature necessary information will be lost in correctly predicting the customer behavior.
Delicatessen feature thus is important part of the data and cannot be removed.
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 [31]:
corr = data.corr()
mask = np.zeros_like(corr)
mask[np.triu_indices_from(mask, 1)] = True
with sns.axes_style("white"):
ax = sns.heatmap(corr, mask=mask, square=True, annot=True,
cmap='RdBu', fmt='+.3f')
plt.xticks(rotation=45, ha='center')
# Produce a scatter matrix for each pair of features in the data
pd.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');
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: The pair of features exhibiting some type of correlation are [Milk, Grocery], [Grocery, Detergents_Paper].
From the above data, Delicatessen feature doesn't seem to have any correlation between any other features. All the features when compared to Deli are bunched near the origin and thus don't have a normalized distribution.
This denies my suspicion about the relevance of the feature since the scatter matrix did not show any correlation between Deli and other features.
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 [40]:
fig, axes = plt.subplots(2, 3)
axes = axes.flatten()
fig.set_size_inches(18, 6)
fig.suptitle('Distribution of Features')
for i, col in enumerate(data.columns):
feature = data[col]
sns.distplot(feature, label=col, ax=axes[i]).set(xlim=(-1000, 20000),)
axes[i].axvline(feature.mean(),linewidth=2, color='y')
axes[i].axvline(feature.median(),linewidth=1, color='r')
# 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)
fig, axes = plt.subplots(2, 3)
axes = axes.flatten()
fig.set_size_inches(18, 6)
fig.suptitle('Distribution of Features for Log Data')
for i, col in enumerate(log_data.columns):
feature = log_data[col]
sns.distplot(feature, label=col, ax=axes[i])
axes[i].axvline(feature.mean(),linewidth=2, color='y')
axes[i].axvline(feature.median(),linewidth=1, color='r')
# Produce a scatter matrix for each pair of newly-transformed features
pd.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');
# set plot style & color scheme
sns.set_style('ticks')
with sns.color_palette("Reds_r"):
# plot densities of log data
plt.figure(figsize=(8,4))
for col in data.columns:
sns.kdeplot(log_data[col], shade=True)
plt.legend(loc='best')
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 [41]:
# 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 [42]:
# 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], 25.0)
# TODO: Calculate Q3 (75th percentile of the data) for the given feature
Q3 = np.percentile(log_data[feature], 75.0)
# 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 = [65, 66, 75, 128, 154]
# Remove the outliers, if any were specified
good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)
Answer:
There are some data points which are categorized as outliers in atleast 2 features. The data points added to outliers list are: 65, 66, 75, 128, 154
These points should be removed from the dataset because these data points don't lie within normalized distribution range for two or more features. By removing these outliers, we can then correctly classify other data points.
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 [43]:
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)
# 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: The variance in data explained by first and second PCA is 0.7068 or 70.68%
The variance explained by first four PCA dimensions is 0.9311 or 93.11%
The dimensions representing customer spending is as follows:
Dimension-1 It indicates that customer spends more on Detergents_Paper and if customer spends more on Detergents_Paper it will also spend more on Milk and Groceries while spending less on Fresh products and Frozen items. This dimension is inclined to represent maybe a coffee/drinks restaurant.
Dimension-2 In this dimension, all the weights are positive. Customer spends most on Fresh products, Frozen and Deli items with less focus on Milk, Grocery but also buys other items like Milk, Grocery and Detergents_Paper. This will likely represent a market.
Dimension-3 Here customer spends most on Deli and frozen products and doesn't focus on buying Fresh products and Detergents and paper. This most likely represents a Deli restaurant.
Dimension-4 This dimension is represented by Frozen. A customer buys more of Frozen products and Detergents and Paper while keeping away from Deli items and Fresh produce.
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 [44]:
# 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 [45]:
# 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 [46]:
# 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 [47]:
# Create a biplot
vs.biplot(good_data, reduced_data, pca)
Out[47]:
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.
Answer:
Advantages of K-means:
Reference:
Advantages of Gaussian Mixture Model:
In hard classification (like in K-means), a data point is assigned to let's say cluster A with 100% probability or belief that it belongs to cluster A. As the algorithm progresses, it might reverse it belief that the same data point now belongs to another cluster, Cluster B, but with same 100% probability that it belongs to this new cluster. In effect, in every iteration, the cluster assignment is hard in sense that either a data point belong to Cluster or not.
In Soft classification (like in GMM), a data point is assigned to Cluster A with some probability like 90%. At the same time the data point also has 10% OF chance that it belongs to Cluster B. In the next iteration, algorithm will recalculate cluster centers and it might lower the chance that data point now has 80% chance of belonging to Cluster A and 20% chance of belonging to Cluster B. GMM incorporates this degree of uncertainity into the algorithm.
References:
Based on the dataset and PCA analysis, it looks like some of the data points (customers) doesn't really belong to a particular group. The PCA dimension-2 group can be seen as this example. Customers buying Fresh, Frozen and Deli can also be loosely placed in group of customers buying more Frozen items or Deli items. For this reason, Gaussian Mixture model is more appropriate to use.
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 [48]:
from sklearn.mixture import GMM
# TODO: Apply your clustering algorithm of choice to the reduced data
clusterer = GMM(n_components=2).fit(reduced_data)
# TODO: Predict the cluster for each data point
preds = clusterer.predict(reduced_data)
# TODO: Find the cluster centers
centers = clusterer.means_
# TODO: Predict the cluster for each transformed sample data point
sample_preds = clusterer.predict(pca_samples)
from sklearn.metrics import silhouette_score
# TODO: Calculate the mean silhouette coefficient for the number of clusters chosen
score = silhouette_score(reduced_data, preds)
print score
Answer:
| Number of Clusters | Silhouette Score |
|---|---|
| 2 | 0.41181 |
| 3 | 0.37245 |
| 5 | 0.29544 |
| 7 | 0.32197 |
| 11 | 0.25546 |
| 15 | 0.22718 |
| 30 | 0.18454 |
| 130 | 0.12487 |
| 205 | 0.22035 |
| 435 | 0.29435 |
The number of clusters with best silhouette score is 2.
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 [49]:
# 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 [51]:
# 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)
import seaborn as sns
sns.heatmap((true_centers-data.mean())/data.std(ddof=1), annot=True, cbar=False, square=True)
Out[51]:
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: Based on the cluster centers in above figure and the statistical representation
Segment 1 will then represent coffee/drink restaurant and Segment 0 will represent a prepared foods industry like catering for airline etc.
In [52]:
# Display the predictions
for i, pred in enumerate(sample_preds):
print "Sample point", i, "predicted to be in Cluster", pred
Answer:
Sample points 1 & 2 are predicted correctly. It looks like sample point 0 belongs more towards cluster 0, but is predicted to be in cluster 1.
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: Segment 1 customers buys more of Milk, Grocery and Detergent and Paper while Segment 0 customers buys more of Fresh products and Frozen items.
Distributor can first select segment 1 customers since Milk, Grocery items need not be delivered everyday and can work with reduced number of days. Distributor can then perform A/B test by dividing the segment 0 customers into two where one group will receive shipments 5 days a week and other group 3 days a week. Based on the response of two groups, if the group with shipment of 3 days respond positively, distributor can choose to switch customers of segment 1 to 3 day shipments.
Similarly, customers in Segment 0 order more Fresh produce which might make customers unhappy about 3 day shipment as fresh produce will not be much 'fresh' as compared to 5 day delivery.
However, all customers in segment 1 cannot be considered as equally since some customers may specifically require 5 day delivery to maintain fresh stock.
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: By combining the customer segment data as a new feature with the 6 original features, distributor can use supervised learning algorithm like Decision Trees or Gradient Boosting to train and predict a customer's delivery schedule based on the results of A/B testing done earlier.
The target variable will then be delivery schedule for new customers.
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 [54]:
# 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:
The clustering algorithm does well in predicting 2 as the number of clusters. Seeing the distribution above for 'Channel' feature, the clustering algorithm also predicts nearly same distribution.
Yes, based on the above distribution visualizing 'Channel' feature, most of the customers belong to either one of the customer segments and can be classified as 'Retailers' or 'Hotel/Restaurants/Cafe'.
Yes, the clustering algorithm was able to find a distribution similar to above one based on the 6 features and not including 'Channel' feature and came to same conclusion by giving two segments/clusters as output.
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.