Sadaf Azad sfaz

## Venues - Linked Data.ipynb

      
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                psychemedia
                / Venues - Linked Data.ipynb
            
            
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              July 23, 2020 01:52
            
              
                Venue map
              
          
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## scale.py
def normalize(df):
  """
  Function for min-max Scaling a pandas DataFrame
  @param:
  Takes a pandas DataFrame: df
  Returns: a normalized DataFrame
  along with a dict containing rescaling
  coef which can be used in below function.
  """
    result = df.copy()

## Basic timeseries exploration.ipynb

      
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                cast42
                / Basic timeseries exploration.ipynb
            
            
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              August 10, 2018 01:16
            
              
                Exploration of basic timeseries approach to forecast sales in the Rossman Kaggle competition
              
          
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## kmeansExample.py
#############################################################################
# Full Imports

from __future__ import division
import math
import random

"""
This is a pure Python implementation of the K-means Clustering algorithmn. The
original can be found here:

## check_redis.py
#!/usr/bin/python

#
# LICENSE: MIT
#
# Copyright (C) 2014 Samuel Stauffer
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation
	def normalize(df):
	"""
	Function for min-max Scaling a pandas DataFrame
	@param:
	Takes a pandas DataFrame: df
	Returns: a normalized DataFrame
	along with a dict containing rescaling
	coef which can be used in below function.
	"""
	result = df.copy()
	#############################################################################
	# Full Imports

	from __future__ import division
	import math
	import random

	"""
	This is a pure Python implementation of the K-means Clustering algorithmn. The
	original can be found here:
	#!/usr/bin/python

	#
	# LICENSE: MIT
	#
	# Copyright (C) 2014 Samuel Stauffer
	#
	# Permission is hereby granted, free of charge, to any person obtaining a copy
	# of this software and associated documentation files (the "Software"), to
	# deal in the Software without restriction, including without limitation