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DATA SCIEN 2020MRA Project Milestone 2
MARKETING & 
RETAIL 
ANALYTICS 
MILESTONE - 2 
SANDYA VB 
29-08-2021 
z 
PROBLEM STATEMENT 
▪ A Grocery Store shared the transactional data with 
you. Your job is to identify the most popular combos 
that can be suggested to the Grocery Store chain after 
a thorough analysis of the most commonly occurring 
sets of items in the customer orders. The Store 
doesn’t have any combo offers. Can you suggest the 
best combos & offers? 
▪ DATA: dataset_ 
z 
TOOLS USED 
▪ TABLEAU Tool: Used for Ex...
- Presentation
- • 20 pages •
MARKETING & 
RETAIL 
ANALYTICS 
MILESTONE - 2 
SANDYA VB 
29-08-2021 
z 
PROBLEM STATEMENT 
▪ A Grocery Store shared the transactional data with 
you. Your job is to identify the most popular combos 
that can be suggested to the Grocery Store chain after 
a thorough analysis of the most commonly occurring 
sets of items in the customer orders. The Store 
doesn’t have any combo offers. Can you suggest the 
best combos & offers? 
▪ DATA: dataset_ 
z 
TOOLS USED 
▪ TABLEAU Tool: Used for Ex...
DATA SCIEN 2020MRA Project MIlestone
MARKETING & 
RETAIL ANALYTICS 
z 
PROBLEM STATEMENT 
▪ An automobile parts manufacturing company has collected data 
of transactions for 3 years. They do not have any in-house data 
science team, thus they have hired you as their consultant. Your 
job is to use your magical data science skills to provide them 
with suitable insights about their data and their customers. 
▪ DATA: Sales_D 
z 
DATA DICTIONARY 
ORDERNUMBER : Order Number CUSTOMERNAM 
E : customer 
QUANTITYORDERED 
: 
Quantity or...
- Presentation
- • 29 pages •
MARKETING & 
RETAIL ANALYTICS 
z 
PROBLEM STATEMENT 
▪ An automobile parts manufacturing company has collected data 
of transactions for 3 years. They do not have any in-house data 
science team, thus they have hired you as their consultant. Your 
job is to use your magical data science skills to provide them 
with suitable insights about their data and their customers. 
▪ DATA: Sales_D 
z 
DATA DICTIONARY 
ORDERNUMBER : Order Number CUSTOMERNAM 
E : customer 
QUANTITYORDERED 
: 
Quantity or...
DATA SCIEN 2020Business Report TSF Questions and Answers(2022/2023)
BUSINESS ANALYSIS 
REPORT 
TIME SERIES FORECASTING 
 
JUNE 20, 2021 
SANDYA V B 
 
CONTENTS 
1. Read the data as an appropriate Time Series data and plot the data. 
2. Perform appropriate Exploratory Data Analysis to understand the data and also perform 
decomposition. 
3. Split the data into training and test. The test data should start in 1991. 
4. Build various exponential smoothing models on the training data and evaluate the model 
using RMSE on the test data. 
Other models such as regressi...
- Presentation
- • 24 pages •
BUSINESS ANALYSIS 
REPORT 
TIME SERIES FORECASTING 
 
JUNE 20, 2021 
SANDYA V B 
 
CONTENTS 
1. Read the data as an appropriate Time Series data and plot the data. 
2. Perform appropriate Exploratory Data Analysis to understand the data and also perform 
decomposition. 
3. Split the data into training and test. The test data should start in 1991. 
4. Build various exponential smoothing models on the training data and evaluate the model 
using RMSE on the test data. 
Other models such as regressi...
DATA SCIEN 2020 TIME SERIES FORECASTING PROJECT
Problem: 
For this particular assignment, the data of different types 
of wine sales in the 20th century is to be analysed. Both 
of these data are from the same company but of different 
wines. As an analyst in the ABC Estate Wines, you are 
tasked to analyse and forecast Wine Sales in the 20th 
century. 
Dataset - R 
In [1]: import numpy as np 
import pandas as pd 
import seaborn as sns 
from matplotlib import pyplot as plt 
from import rcParams 
rcParams['ze'] = 13, 6 
1. Read the data as ...
- Presentation
- • 196 pages •
Problem: 
For this particular assignment, the data of different types 
of wine sales in the 20th century is to be analysed. Both 
of these data are from the same company but of different 
wines. As an analyst in the ABC Estate Wines, you are 
tasked to analyse and forecast Wine Sales in the 20th 
century. 
Dataset - R 
In [1]: import numpy as np 
import pandas as pd 
import seaborn as sns 
from matplotlib import pyplot as plt 
from import rcParams 
rcParams['ze'] = 13, 6 
1. Read the data as ...
ACNT 2402Chapter 12 - Study Guide Questions and Answers(2022/2023)
Chapter 12 -- Statement of Cash Flows – Study Guide 
1. Collecting the principal on a loan to another company would be reported on the 
investing activities section of the statement of cash flows. 
TRUE 
2. Money received from issuing bonds payable would be included as part of a 
company's financing activities on the statement of cash flows. 
TRUE 
3. The collection of a loan made to a supplier would be treated as an investing 
activity on a statement of cash flows. 
TRUE 
4. Paying taxes to ...
- Other
- • 6 pages •
Chapter 12 -- Statement of Cash Flows – Study Guide 
1. Collecting the principal on a loan to another company would be reported on the 
investing activities section of the statement of cash flows. 
TRUE 
2. Money received from issuing bonds payable would be included as part of a 
company's financing activities on the statement of cash flows. 
TRUE 
3. The collection of a loan made to a supplier would be treated as an investing 
activity on a statement of cash flows. 
TRUE 
4. Paying taxes to ...
ACNT 2402Chapter 9 - Study Guide Questions and Answers(2022/2023)
Chapter 09 -- Performance Measurement in Decentralized 
Organizations – Study Guide 
1. Land held for possible plant expansion would not be included as an operating asset when 
computing return on investment (ROI). 
TRUE 
2. When used in return on investment (ROI) calculations, operating assets do not include 
investments in land held for future use and investments in other companies. 
TRUE 
3. A disadvantage of using ROI to evaluate performance is that it encourages the manager to 
reduce the...
- Other
- • 10 pages •
Chapter 09 -- Performance Measurement in Decentralized 
Organizations – Study Guide 
1. Land held for possible plant expansion would not be included as an operating asset when 
computing return on investment (ROI). 
TRUE 
2. When used in return on investment (ROI) calculations, operating assets do not include 
investments in land held for future use and investments in other companies. 
TRUE 
3. A disadvantage of using ROI to evaluate performance is that it encourages the manager to 
reduce the...
ACNT 2402Chapter 06 - Study Guide
Chapter 06 -- Variable Costing and Segment Reporting: Tools for 
Management – Study Guide 
1. Under variable costing, variable production costs are not treated as product costs. 
FALSE 
2. Under variable costing, fixed manufacturing overhead cost is not treated as a product 
cost. 
TRUE 
3. Under variable costing, product costs consist of direct materials, direct labor, and variable 
manufacturing overhead. 
TRUE 
4. The costs assigned to units in inventory are typically lower under variable c...
- Other
- • 11 pages •
Chapter 06 -- Variable Costing and Segment Reporting: Tools for 
Management – Study Guide 
1. Under variable costing, variable production costs are not treated as product costs. 
FALSE 
2. Under variable costing, fixed manufacturing overhead cost is not treated as a product 
cost. 
TRUE 
3. Under variable costing, product costs consist of direct materials, direct labor, and variable 
manufacturing overhead. 
TRUE 
4. The costs assigned to units in inventory are typically lower under variable c...
DATA SCIEN 2020Predictive Modeling Project
In [205]: from ets import load_boston 
import pandas as pd 
import numpy as np 
import seaborn as sns 
import t as plt 
import as sm 
from _selection import train_test_split 
from r_model import LinearRegression 
from er import KMeans 
from cs import mean_squared_error 
from ers_influence import variance_inflation_fac 
tor 
import math 
1.1. Read the data and do exploratory data analysis. Describe the data 
briefly. (Check the null values, Data types, shape, EDA). Perform 
Univariate and Bivari...
- Presentation
- • 82 pages •
In [205]: from ets import load_boston 
import pandas as pd 
import numpy as np 
import seaborn as sns 
import t as plt 
import as sm 
from _selection import train_test_split 
from r_model import LinearRegression 
from er import KMeans 
from cs import mean_squared_error 
from ers_influence import variance_inflation_fac 
tor 
import math 
1.1. Read the data and do exploratory data analysis. Describe the data 
briefly. (Check the null values, Data types, shape, EDA). Perform 
Univariate and Bivari...
ACNT 2402Chapter 08 - Study Guide Questions and Answers(2022/2023)
Chapter 8 – Flexible Budgets, Standard Costs, and Variance 
Analysis Study Guide 
1. Comparing a static planning budget to actual costs is a good way to assess 
whether variable costs are under control. 
FALSE 
2. Directly comparing a static planning budget to actual costs helps to distinguish 
between differences in costs that are due to changes in activity and differences 
that are due to how well costs were controlled. 
FALSE 
3. Fixed costs should be ignored when evaluating how well a mana...
- Other
- • 28 pages •
Chapter 8 – Flexible Budgets, Standard Costs, and Variance 
Analysis Study Guide 
1. Comparing a static planning budget to actual costs is a good way to assess 
whether variable costs are under control. 
FALSE 
2. Directly comparing a static planning budget to actual costs helps to distinguish 
between differences in costs that are due to changes in activity and differences 
that are due to how well costs were controlled. 
FALSE 
3. Fixed costs should be ignored when evaluating how well a mana...
DATA SCIEN 2020Machine Learning Project
In [1]: import pandas as pd 
import numpy as np 
from sklearn import preprocessing 
from _selection import train_test_split 
from _bayes import GaussianNB 
from cs import accuracy_score 
import seaborn as sns 
import t as plt 
from import zscore 
import warnings 
rwarnings( "ignore") 
from r_model import LinearRegression 
from er import KMeans 
from cs import mean_squared_error 
from ers_influence import variance_inflation_fac 
tor 
import math 
from r_model import LogisticRegression 
from sk...
- Presentation
- • 72 pages •
In [1]: import pandas as pd 
import numpy as np 
from sklearn import preprocessing 
from _selection import train_test_split 
from _bayes import GaussianNB 
from cs import accuracy_score 
import seaborn as sns 
import t as plt 
from import zscore 
import warnings 
rwarnings( "ignore") 
from r_model import LinearRegression 
from er import KMeans 
from cs import mean_squared_error 
from ers_influence import variance_inflation_fac 
tor 
import math 
from r_model import LogisticRegression 
from sk...
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