LucaMell

## University_admission.ipynb

      
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                University_admission
              
          
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## S&P 500 STOCK MARKET PRICE PREDICTION.ipynb

      
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              September 1, 2019 10:24
            
              
                S&P 500 STOCK MARKET PRICE PREDICTION.ipynb
              
          
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## Salary_Prediction.ipynb

      
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## Fuel_Consumption.ipynb

      
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## Icecream_Revenue_Simple_Reg.ipynb

      
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## Simple+Linear+Regression+-+Ice+Cream+Revenue+Prediction-Copy1.ipynb

      
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              August 29, 2019 15:15
            
          
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## flower_detector.py
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np

# each point is length, width, type (0, 1)
data = [[3,   1.5, 1],
        [2,   1,   0],
        [4,   1.5, 1],
        [3,   1,   0],
        [3.5, .5,  1],

## Visualising Volatility of Single Assets.py
import pandas as pd
from pandas_datareader import data as web
import matplotlib.pyplot as plt
from datetime import datetime

start = datetime(2016,1,1)
end = datetime(2017,1,1)

assets = ['AAPL', 'FB', 'TSLA']

## Riskiness of a Portfolio.py
# import python's number crunchers
from pandas_datareader import data as web
import pandas as pd
import numpy as np

assets =  ['AAPL', 'GM', 'GE', 'FB', 'WMT']

df = pd.DataFrame()

for stock in assets:

## clean data output
			adj_close
ticker		AAPL		MSFT		WMT
date
2015-12-31	101.696810	53.096499	58.379766
2016-01-04	101.783763	52.445713	58.532144
2016-01-05	99.233131	52.684973	59.922592
2016-01-06	97.291172	51.727934	60.522580
2016-01-07	93.185040	49.928701	61.932075
	%matplotlib inline
	from matplotlib import pyplot as plt
	import numpy as np

	# each point is length, width, type (0, 1)
	data = [[3, 1.5, 1],
	[2, 1, 0],
	[4, 1.5, 1],
	[3, 1, 0],
	[3.5, .5, 1],
	import pandas as pd
	from pandas_datareader import data as web
	import matplotlib.pyplot as plt
	from datetime import datetime

	start = datetime(2016,1,1)
	end = datetime(2017,1,1)

	assets = ['AAPL', 'FB', 'TSLA']
	# import python's number crunchers
	from pandas_datareader import data as web
	import pandas as pd
	import numpy as np

	assets = ['AAPL', 'GM', 'GE', 'FB', 'WMT']

	df = pd.DataFrame()

	for stock in assets:
	adj_close
	ticker AAPL MSFT WMT
	date
	2015-12-31 101.696810 53.096499 58.379766
	2016-01-04 101.783763 52.445713 58.532144
	2016-01-05 99.233131 52.684973 59.922592
	2016-01-06 97.291172 51.727934 60.522580
	2016-01-07 93.185040 49.928701 61.932075