Walmart Sales Prediction Using Rapidminer Prepared by Naga.docx

Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail
corporation that
operates a chain of discount department stores and Warehouse
Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded
by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in
27 countries, under a total 71
banners. Walmart is the world's largest company by revenue,
according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the
world with 2.2 million employees.
Walmart is a family-owned business, as the company is
controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their
holding company, Walton

Enterprises, and through their individual holdings. The company
was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the
company rose from a regional to
a national giant. By 1988, Walmart was the most profitable
retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales
throughout
numerous strategic selections taken by the corporate. The
analysis is performed on historical
sales data across 45 Walmart stores located in different regions.
The foremost necessary is
Walmart runs many promotional markdown events throughout
the year and we have to check
the impact it creates on sales during that particular period. The
markdowns precede prominent
holidays, the four largest of which are the Labor Day,
Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in
the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class

over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from
Kaggle.com
containing the information about the stores, departments,
average temperature in that
particular region, CPI, day of the week, sales and mainly
indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:

we assume it to be
available in the particular
Train:
-02-
05 to 2012-11-
les in USD for the given department in the given

-
ce
they were not considered
Test:
ict the sales for each triplet of store and
Features:
average temperature in
that region which is measured which ranges from -
articular region is available in the
dataset from which we can

identify that if the fuel is higher in that particular region,
whether it affects the sales as
o measure the
changes in price level in that
-
promotional markdowns
which Walmart is running. There are 5 markdowns in that
particular duration in the
Among these 5 files, Features and train files contain every
necessary elements to do the

predication. So we are going to take advantage of them.
III. Data transformation & Preprocessing:
Our aim is to find Walmart’s weekly sales. We have all the
necessary data for
predicting the weekly sales in Feature data. But the previous
weekly sales report is in Train data
set. So we decided to combine all the data sets into a single data
set. After combining, we now
have the sales data of 45 stores with up to 99 departments which
consists of totally 421,000
records. Now our final data set has the following attributes.
-

Now we have 13 attributes, in which attributes such as
department number (Dept), Date and
Type are not required for our prediction, since it doesn’t have
any impact on its weekly sales.
Hence we are removing these attributes from our final data set.
After removing all the unwanted
data and attributes in our dataset we now have 6,435 records on
total.With our huge dataset, it is
difficult to get exact result about future sales from models
because of the uncertainty in so many
disturbing elements. So we decided to group the data and then
implementing with classifiers or
regression models. By using Discretize by Size operators, we
grouped the data based on
Weekly_Sales into 5 categories and then exported the output as
CSV file to desktop.
After processing, now check the data of weekly sales, we found
that the ranges are as
follows;

range1 [-? - 9144.495]
range2 [9144.495 - 14213.040]
range3 [14213.040 - 22267.010]
range4 [22267.010 - 39499.755]
range5 [39499.755 - ?]
Now we manually label the whole 6,435 records into 5 groups
each contain 1,287 records.
Groups Mark as Description
Group 1 A range1 [-? - 9144.495]
Group 2 B range2 [9144.495 - 14213.040]
Group 3 C range3 [14213.040 - 22267.010]
Group 4 D range4 [22267.010 - 39499.755]
Group 5 E range5 [39499.755 - ?]

IV. Mining models & Performance:
Our purpose is to build a model to predict Walmart’s weekly
sales in future, so we would
like to select the most suitable model for predication. Look into
the table, we find out the data is
structured. However, the parameters are complex and do not
show its rules obviously. Even
more, we have no idea about whether it is linear or not.
According to problems above, we determined to use Neural
Network Model with our project
because it has the characteristics below:
•It is for complicated prediction problems.
•Visualization or understanding of the rules are not needed.
•Accuracy is very important.
The design for implementing the Neural Network model in rapid
miner is as follows:

The output we obtained is as follows,
Accuracy = 77.61%, when Learning Rate = 2000 & Training
Cycles = 0.05
true A
true B
true C
true D
true E
class
precision

pred. A 939 49 83 36 95 78.12%
pred. B 17 1114 58 75 101 83.61%
pred. C 160 102 923 148 29 68.77%
pred. D 60 95 93 780 187 65.20%
pred. E 23 276 43 161 788 63.04%
67.67%
class recall 78.32% 68.09% 77.92% 65.00%
We tried to test our result by changing learning rate and training
cycle values and noted down
the changes too. The result obtained in this process is as
follows;

Cycles = 0.05
true A
true B
true C
true D
true E
class
precision
pred. A 1012 62 96 42 102 77.62%

pred. B 23 1193 58 76 120 84.61%
pred. C
184
114
1025
181
28
68.91%
pred. D 69 124 104 842 209 64.89%
pred. E 34 306 37 179 861 61.89%

class recall 76.72% 69.31% 78.65% 63.79% 67.23%
Cycles = 0.3
class
true A
true B
true C
true D
true E

precision
pred. A 908 86 114 56 108 74.38%
pred. B 25 1033 50 64 83 86.31%
pred. C 164 123 895 176 40 64.02%
pred. D 73 117 94 732 189 62.75%
pred. E 29 277 47 172 780 63.07%
class recall 77.03% 65.24% 77.28% 64.42% 67.02%

Since weekly sales is the core for any industry. So it is
necessary to get the more accurate
results, since it have direct impact on companies revenue. To
achieve better performance and
accuracy results, we have now increased the hidden layer size to
10.
We now combined all the hidden layer results obtained from
each node in a single table to check
where the weekly sales have a greater impact.
Attributes Node 1 Node 2
Store -30.224 27.949
Temperature -0.192 0.68
Fuel_Price 1.054 1.459
MarkDown1 0.114 -2.679
MarkDown2 0.88 0.476
MarkDown3 9.209 -0.635

Node 3 Node 4 Node 5 Node 6 Node 7 Node 8 Node 9 Node 10
-20.9 -31.401 5.072 -17.01 40.862 27.088 18.266 16.821
-0.965 -0.601 0.038 -0.847 -0.436 -1.513 -1.094 0.126
-0.46 0.632 -1.242 -1.713 0.698 1.303 2.589 -0.007
-0.24 -0.636 -4.109 -1.322 1.004 0.648 -1.147 -2.402
-0.03 2.65 -0.387 -1.306 0.328 0.633 1.983 -0.821
0.631 7.522 -6.247 -2.194 -6.743 -0.075 1.157 6.285
MarkDown4 -0.613 -0.554 0.131 1.257 -2.967 -0.445 0.753
0.691 -0.115 0.599
MarkDown5 0.41 -3.728 -3.697 1.786 -14.643 -2.111 0.341 -
0.999 -7.048 -0.908
CPI -6.316 29.004 -12.179 -12.975 0.568 11.217 -27.856 -
15.442 -5.764 1.045
Unemployment -12.906 -17.876 4.064 -1.446 8.895 3.908 7.056
12.174 -31.068 1.142
IsHoliday 0.056 0 -0.191 -0.484 -0.068 -0.244 -0.151 -0.27 -
0.036 -0.025
Bias -13.162 -10.766 -9.052 -8.572 -25.596 9.488 -10.875
20.103 -2.328 -9.733

The hidden layer network for this dataset will look like:
Other Models:
We tried to analyze data with more models other than Neural
Network. But we find out that
Neural Network model is the most suitable one for Walmart
dataset. Other models cannot
work on this example.
Naïve Bayes:

Design:
Output:
Accuracy = 23.42%
true A true B true C true D true D
class
precision
pred. A 8190 0 0 0 0 100.00%
pred. B 19795 23905 23910 23905 23909 20.71%
pred. C 15895 19128 19124 19128 19126 20.70%

pred. D 0 0 0 0 0 0.00%
pred. E 3917 4782 4781 4782 4780 20.74%
class
recall
17.13% 49.99% 40.00% 0.00% 10.00%
K-NN:
Design:
Output:

When K = 1, Accuracy = 28.76%
true A true B true C true D true E
class
precision
pred. A 385 207 373 180 199 28.65%
pred. B 190 619 259 311 301 36.85%
pred. C 352 271 187 225 188 15.29%
pred. D 113 267 124 180 210 20.13%
pred. E 159 272 257 304 302 23.34%
class

32.11% 37.84% 15.58% 15.00% 25.17%
recall
When K = 10, Accuracy = 31.06%
true A true B true C true D true E
class
precision
pred. A 429 234 403 168 206 29.79%
pred. B 272 834 288 421 428 37.18%
pred. C 219 123 186 97 213 24.20%

pred. D 195 266 162 347 281 28.64%
pred. E 84 179 161 167 72 10.86%
class
35.78% 50.98% 15.50% 28.92% 6.00%
recall
Decision Tree:
No matter what the parameters are, the Accuracy is 25.42% and
never change.
true D true S true C true B true A

class
precision
pred. D 0 0 0 0 0 0.00%
pred. S 1199 1636 1200 1200 1200 25.42%
pred. C 0 0 0 0 0 0.00%
pred. B 0 0 0 0 0 0.00%
pred. A 0 0 0 0 0 0.00%
class
0.00% 100.00% 0.00% 0.00% 0.00%
recall

As we show above, Naïve Bayes Model products the lowest
Accuracy
(18.63%). Abstractly, the Naïve Bayes model is a conditional
model like this:
P (Sales | Store, Data, Temperature … & IsHoliday)
=
P(Sales)*P(Store, Data, Temperature, … & IsHoliday | Sales)
P(Store, Data, Temperature, … & IsHoliday)
Because variable Store, Data to IsHoliday are independent on
each other, so:
P (Store, Date, Temperature … & IsHoliday) =P (Store)*P
(Date)*…..*P (IsHoliday)
Due to so many numbers in columns Store, Date … IsHoliday

that do not repeat, the
probability of each Variables is too small. So P (Store)*P
(Date)*…..*P (IsHoliday) will be far
lower than 1/6435. This means the probability of sales basing
such a model is infeasible.
V. Interpretation:
As a result, we find out that how each parameter affects the
weekly sales. MarkDown 1 to 5
has the highest weight as 9 to -16 which mean it really makes an
enormous impact on the sales.
Promotion will increase weekly sales remarkably. Fuel price
and temperature also makes a
positive impact, higher price and temperature makes higher
sales. CPI and Unemployment rate
having a heavy negative impact on the prospects of sales. The
higher CPI and unemployment
rate, the less weekly sales. Holidays affect weekly sales
slightly. We think customers don’t care
whether today is holiday or not, the only reason they buy items
is promotion. Therefore,
Promotion drives sales and revenue to increase.

VI. Conclusion:
The main objective was to model the effects of markdown
events throughout the year and it
was successfully measured using various algorithms. From the
analysis it is noted that MarkDown 1
to 5 has the highest weight as 16, which means it really makes
an enormous impact on the sales.
Promotion will increase weekly sales remarkably. Fuel price
and temperature also makes a positive
impact, higher price makes higher sales. CPI and
Unemployment rate has a heavy negative impact
on the prospects of sales. The higher CPI and unemployment
rate, the less is the weekly sales.
Holidays affect weekly sales slightly. We felt customers didn’t
care whether it was a holiday or not
because the only reason they buy items is due to promotion. It is
notable that they are attracted to
various deals in departmental stores all over the world.
References:
1. https://www.kaggle.com/c/walmart-recruiting-store-sales-
forecasting/data
2. http://www.walmart.com/
3. http://en.wikipedia.org/wiki/Walmart
https://www.kaggle.com/c/walmart-recruiting-store-sales-
forecasting/data
http://www.walmart.com/

http://en.wikipedia.org/wiki/Walmart
Allstate Purchase Prediction Challenge
Predict a purchased policy based on transaction history
As a customer shops an insurance policy, he/she will receive a
number of quotes with different coverage options before
purchasing a plan. This is represented in this challenge as a
series of rows that include a customer ID, information about the
customer, information about the quoted policy, and the cost.
Your task is to predict the purchased coverage options using a
limited subset of the total interaction history. If the eventual
purchase can be predicted sooner in the shopping window, the
quoting process is shortened and the issuer is less likely to lose
the customer's business.
Using a customer’s shopping history, can you predict what
policy they will end up choosing?
Files
test_v2.csv.zip
train.csv.zip
Data Introduction
The training and test sets contain transaction history for
customers that ended up purchasing a policy. For each
customer_ID, you are given their quote history. In the training
set you have the entire quote history, the last row of which
contains the coverage options they purchased. In the test set,
you have only a partial history of the quotes and do not have the
purchased coverage options. These are truncated to certain
lengths to simulate making predictions with less history (higher
uncertainty) or more history (lower uncertainty).
For each customer_ID in the test set, you must predict the seven
coverage options they end up purchasing.
What is a customer?
Each customer has many shopping points, where a shopping
point is defined by a customer with certain characteristics
viewing a product and its associated cost at a particular time.

· Some customer characteristics may change over time (e.g. as
the customer changes or provides new information), and the cost
depends on both the product and the customer characteristics.
· A customer may represent a collection of people, as policies
can cover more than one person.
· A customer may purchase a product that was not viewed!
Product Options
Each product has 7 customizable options selected by customers,
each with 2, 3, or 4 ordinal values possible:
A product is simply a vector with length 7 whose values are
chosen from each of the options listed above. The cost of a
product is a function of both the product options and customer
characteristics.
Variable Descriptions
customer_ID - A unique identifier for the customer
shopping_pt - Unique identifier for the shopping point of a
given customer
record_type - 0=shopping point, 1=purchase point
day - Day of the week (0-6, 0=Monday)
time - Time of day (HH:MM)
state - State where shopping point occurred
location - Location ID where shopping point occurred
group_size - How many people will be covered under the policy
(1, 2, 3 or 4)
homeowner - Whether the customer owns a home or not (0=no,
1=yes)
car_age - Age of the customer’s car
car_value - How valuable was the customer’s car when new
risk_factor - An ordinal assessment of how risky the customer is
(1, 2, 3, 4)
age_oldest - Age of the oldest person in customer's group
age_youngest - Age of the youngest person in customer’s group
married_couple - Does the customer group contain a married
couple (0=no, 1=yes)
C_previous - What the customer formerly had or currently has

for product option C (0=nothing, 1, 2, 3,4)
duration_previous - how long (in years) the customer was
covered by their previous issuer
A,B,C,D,E,F,G - the coverage options
cost - cost of the quoted coverage options
Data Mining Steps
Problem Definition
Market Analysis
Customer Profiling, Identifying Customer Requirements, Cross
Market Analysis, Target Marketing, Determining Customer
purchasing pattern
Corporate Analysis and Risk Management
Finance Planning and Asset Evaluation, Resource Planning,
Competition
Fraud Detection
Customer Retention
Production Control
Science Exploration
> Data Preparation
Data preparation is about constructing a dataset from one or
more data sources to be used for exploration and modeling. It is
a solid practice to start with an initial dataset to get familiar
with the data, to discover first insights into the data and have a

good understanding of any possible data quality issues. The
Datasets you are provided in these projects were obtained from
kaggle.com.
Variable selection and description
Numerical – Ratio, Interval
Categorical – Ordinal, Nominal
Simplifying variables: From continuous to discrete
Formatting the data
Basic data integrity checks: missing data, outliers
> Data Exploration
Data Exploration is about describing the data by means of
statistical and visualization techniques.
· Data Visualization:
· Univariate analysis explores variables (attributes) one by one.
Variables could be either categorical or numerical.
Univariate Analysis - Categorical
Statistics
Visualization
Description
Count
Bar Chart
The number of values of the specified variable.
Count%
Pie Chart
The percentage of values of the specified variable
Univariate Analysis - Numerical
Statistics
Visualization
Equation
Description
Count
Histogram
N
The number of values (observations) of the variable.

Minimum
Box Plot
Min
The smallest value of the variable.
Maximum
Box Plot
Max
The largest value of the variable.
Mean
Box Plot
The sum of the values divided by the count.
Median
Box Plot
The middle value. Below and above median lies an equal
number of values.
Mode
Histogram
The most frequent value. There can be more than one mode.
Quantile
Box Plot
A set of 'cut points' that divide a set of data into groups
containing equal numbers of values (Quartile, Quintile,
Percentile, ...).
Range
Box Plot
Max-Min
The difference between maximum and minimum.
Variance
Histogram
A measure of data dispersion.
Standard Deviation

Histogram
The square root of variance.
Coefficient of Deviation
Histogram
A measure of data dispersion divided by mean.
Skewness
Histogram
A measure of symmetry or asymmetry in the distribution of
data.
Kurtosis
Histogram
A measure of whether the data are peaked or flat relative to a
normal distribution.
Note: There are two types of numerical variables, interval and
ratio. An interval variable has values whose differences are
interpretable, but it does not have a true zero. A good example
is temperature in Centigrade degrees. Data on an interval scale
can be added and subtracted but cannot be meaningfully
multiplied or divided. For example, we cannot say that one day
is twice as hot as another day. In contrast, a ratio variable has
values with a true zero and can be added, subtracted, multiplied
or divided (e.g., weight).
· Bivariate analysis is the simultaneous analysis of two
variables (attributes). It explores the concept of relationship
between two variables, whether there exists an association and
the strength of this association.
There are three types of bivariate analysis.
1.Numerical & Numerical
Scatter Plot, Linear Correlation …
2.Categorical & Categorical

Stacked Column Chart, Combination Chart, Chi-square Test
3.Numerical & Categorical
Line Chart with Error Bars, Combination Chart, Z-test and t-test
> Modeling
· Predictive modeling is the process by which a model is created
to predict an outcome
· If the outcome is categorical it is called classification and if
the outcome is numerical it is called regression.
· Descriptive modeling or clustering is the assignment of
observations into clusters so that observations in the same
cluster are similar.
· Finally, association rules can find interesting associations
amongst observations.
Classification algorithms:
1. Frequency Table
· ZeroR, OneR, Naive Bayesian, Decision Tree
2. Covariance Matrix
· Linear Discriminant Analysis, Logistic Regression
3. Similarity Functions
· K Nearest Neighbors
4. Others
· Artificial Neural Network, Support Vector Machine
Regression
1. Frequency Table
· Decision Tree
2. Covariance Matrix
· Multiple Linear Regression
3. Similarity Function
· K Nearest Neighbors
4. Others
· Artificial Neural Network, Support Vector Machine
Clustering algorithms are:
1. Hierarchical
· Agglomerative, Divisive
2. Partitive

· K Means, Self-Organizing Map
> Evaluation
· helps to find the best model that represents our data and how
well the chosen model will work in the future. Hold-Out and
Cross-Validation
> Deployment
The concept of deployment in predictive data mining refers to
the application of a model for prediction to new data.

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