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COVID-19 - B e a u t y    a t    a    P r i c e    The global beauty industry (comprising skin care, color cosmetics, hair care, fragrances, and personal care) has been shocked by the COVID-19 crisis. First-quarter sales have been weak, and there have been widespread store closures. But the industry has quickly adapted to the change by changing its product line to hand sanitizers and house cleaning products also offering free beauty services to front line workers to gain positive brand positioning. The global beauty industry generated $50 billion in sale a year and accounted to millions of jobs, directly and indirectly giving people in these tough times financial capabilities. Let’s be clear we are talking about an industry which even recession couldn’t kick to the ground. In 2008 financial crises, the spending fell slightly but it was regained by 2010. Figure 1: Even though  recession didn’t had stronger economic impact compared to COVID-19....

Prophet of The Future .             Prophet is open source software released by Facebook’s Core Data Science team. It is available for download on CRAN and PyPI. Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.       Accurate and fast.       Fully automatic.       Tunable forecasts.       Available in R or Python. Let’s explore this with an example.   Here we are using Air Passenger dataset and our jupyter workbook. (you can get the link to this dataset at the end) import warnings warnings . filterwarnings( "ignore" ) import numpy as np from d...

K Means Algorithm With Real Life Problem It has a complicated name but it is sample and is a popular unsupervised machine learning technique. It means to create k number of centroid and then allocate every data point to the nearest cluster, while keeping the number centroid.                 Let’s explore this technique with an example, Here we have an online tea store data where we have details of customer, their date of account created and purchase styles.   In this we are interested to know what makes the customer comeback to the store. Retention is the one of the biggest mystery in any industry. Let us quickly open our jupyter notebook. # Import Necessary Libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline import warnings warnings . filterwarnings('ignore') Read the file in Pandas, # Importing Data #Import Dataset cr = pd . rea...