scikit learn - Python Data Prediction -


i have data intel processors presenting in different types of graphics.

and want make regression show's exponential function can "predict" transistor count after max year have in data (max: 2014). eg: 2019, 2021, 2030.. 

import pandas pd import numpy np import matplotlib.pyplot plt  data = pd.read_csv('data.csv', delimiter=',')  x = np.array(data['year'], dtype=int) y = np.array(data['count'], dtype=int)  print(x) print(y)  plt.plot(x, y, 'bo')  plt.xlabel('year') plt.ylabel('transistor count')  plt.yscale('log')  plt.grid(true)  plt.show()

this code shows:

transistor count (image)

what have tried use scikit-learning create regression, end wrong settings exponential function. , have tried online regression tools, ain't accurate.

i have looked @ sklearn cheat sheet find currect classifier use. not well-know sklearn yet. have tried myself 2 days lot of searching on both google , stackoverflow, haven't found out works data.

[1971 1972 1974 1976 1978 1982 1985 1989 1993 1995 1997 1999 2000 2002 2006  2008 2010 2011 2012 2013 2014 2014] [      2300       3500       4400       6500      29000     134000      275000    1180235    3100000    5500000    7500000    9500000    42000000  220000000  291000000  731000000 1170000000 2270000000  3100000000 1860000000 4310000000 5560000000]

this data inside 2 arrays.

import pandas pd import numpy np import matplotlib.pyplot plt sklearn.svm import svc  data = pd.read_csv('data.csv', delimiter=',')  x = np.array(data['year'], dtype=int) y = np.array(data['count'], dtype=int)  x = np.reshape(x, (x.size, 1))  clf = svc()  clf.fit(x, y)  in range(1971, 2030, 1):     print(i,':', clf.predict([[i]]))

and code, program predict data after 2014 same value 2014. ( (2015-2030).value === 2014.value )

i not sure if there settings can add classifier, or if don't have enough knowledge ml this.

in general, can perform extrapolation should wary of results. wouldn't trust them blindly. example, if had 3 years of data, put lot of credence in extrapolation of 1000 years? it's possible.

but why use classifier if you're performing regression? try using linear regression instead since there's near linear fit. model log(y) = + bx.

enter image description here

import pandas pd import numpy np import matplotlib.pyplot plt sklearn.linear_model import linearregression  # linear regression   x = np.array(data['year'], dtype=int) y = np.array(data['count'], dtype=int)  x = np.reshape(x, (x.size, 1))  clf = linearregression(fit_intercept=false)  clf.fit(x, np.log(y))  # fit log of y  pred= [] in range(1971, 2030, 1):     pred.append(clf.predict([[i]]))  plt.plot(range(1971, 2030, 1), pred, 'bo')  plt.xlabel('year') plt.ylabel('transistor count')  plt.grid(true)  plt.show()

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