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Drinks Sales & Marketing Analysis : Communicate Data Findings

Table of Contents

  • Background (Invalid URL)
  • Data Overview (Invalid URL)
  • Data Wrangling & Cleansing (Invalid URL)
  • Exploratory Data Analysis & Visualization (Invalid URL)
  • Explanatory Visulaization (Invalid URL)
  • Final Report (StroyTelling) + Recommendations (Invalid URL)

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📖 Background

Your company owns a chain of stores across Russia that sell a variety of alcoholic drinks. The company recently ran a wine promotion in Saint Petersburg that was very successful. Due to the cost to the business, it isn’t possible to run the promotion in all regions. The marketing team would like to target 10 other regions that have similar buying habits to Saint Petersburg where they would expect the promotion to be similarly successful.

The data

The marketing team has sourced you with historical sales volumes per capita for several different drinks types.

  • "year" - year (1998-2016)
  • "region" - name of a federal subject of Russia. It could be oblast, republic, krai, autonomous okrug, federal city and a single autonomous oblast
  • "wine" - sale of wine in litres by year per capita
  • "beer" - sale of beer in litres by year per capita
  • "vodka" - sale of vodka in litres by year per capita
  • "champagne" - sale of champagne in litres by year per capita
  • "brandy" - sale of brandy in litres by year per capita

✅ Checklist before publishing into the competition

(hint: Press "share" to publish your workbook. Make sure your workbook is set to Public Access)

  • Rename the title of this workbook to make it descriptive of your work.
  • Remove redundant cells like the judging criteria so the workbook is focussed on your story.
  • Make sure the workbook reads well and explains how you found your insights.

⌛️ Time is ticking. Good luck!

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Data Overview

# Import the needed libraries for analysis & visualizations 
import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt 
import seaborn as sns 
%matplotlib inline 
# Import other libraries (may be used) 
import os, time, warnings
# Get an overview of the data 
df = pd.read_csv(r'./data/russian_alcohol_consumption.csv')
df.head()
df.info()
df.describe()
# Check unique values in region column 
df['region'].unique()
# Get No. of customer regions from our company 
print (f"No of Customer Regions are: {len(df['region'].unique())}")

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Data Wrangling & Cleansing

# replace all null values with zeros
for column in df.columns:
    df[column] = df[column].fillna(0)
df.info()
# Add Total Sales column 
df['total_sales'] = df['wine'] + df['beer'] + df['vodka'] + df['champagne'] + df['brandy']
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