Data scien 2020 - Study guides, Class notes & Summaries

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...

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 ordered PHONE : Phone of the custome...

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 an appropr...

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 ...

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...

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...

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...

MARKETING & RETAIL ANALYTICS MILESTONE - 2 SANDYA VB 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 Exploratory ...

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...

1. Read the data as an appropriate Time Series data and plot the data. The two datasets: Rose and Sparkling are imported using the read command. And convert to time series data using date_range function: date = _range(start='01/01/1980', end='08/01/1995', freq='M')date df['Time_Stamp'] = pd.DataFrame(date,columns=['Month']) () o/p: ROSE WINE YEAR WISE SALES • From the above plot we observe that there is a decreasing trend in the initial years and stabilizes over the years...