python分析雙十一銷量

import numpy as np import matplotlib.pyplot as pltyear = np.array([year for year in range(2009,2021)]) sales = np.array([0.5, 9.36, 52, 191, 352, 571, 912, 1207, 1682.69, 2135, 2684, 4982])plt.plot(year, sales, 'ro')z = np.polyfit(year, sales, 1) z[0] z[1] yhat = z[0]* year + z[1]plt.plot(year, yhat, 'g-')plt.text(2010, 3000, "y={:0.2f}*year{:0.2f}".format(z[0], z[1]), size=18)

結果如下圖：

當然還可以用二次項回歸

z = np.polyfit(year, sales, 2) z z[0] z[1] z[2]yhat = z[0]*year*year + z[1]*year+ z[2]plt.plot(year, sales, 'ro') plt.plot(year, yhat, 'b-') plt.text(2010, 3000, "{:0.2f}*year^2 + {:0.2f}*year + {:0.2f}".format(z[0], z[1], z[2] ))

也可以用對數回歸

lnsales = np.log(sales)z = np.polyfit(year, lnsales, 1)yhat = z[0]*year + z[1]plt.plot(year, lnsales, 'ro') plt.plot(year, yhat) plt.text(2010, 1, "ln(sales)={:0.2f}*year+{:0.2f}".format(z[0], z[1]))

感覺多項式回歸比較好一些：

###一元線性回歸 from sklearn.linear_model import LinearRegression model = LinearRegression()X = year.reshape(-1, 1) model.fit(X, sales)model.intercept_ model.coef_model.get_params() model.score(X, sales)############多項式回歸 Xx2 = np.hstack([X, X**2]) x2L2 = LinearRegression() L2.fit(x2, sales) L2.intercept_ L2.coef_L2.score(x2,sales)

結果二次項回歸R2 =0.94513， 一元線性回歸，R2 = 0.7853
單純做統計分析的話還是statsmodel庫比較好一點。輸出美觀一些。

import numpy as np import statsmodels.api as sm import statsmodels.formula.api as smfimport pandas as pddf = pd.read_json('{"year ":{"0":2009,"1":2010,"2":2011,"3":2012,"4":2013,"5":2014,"6":2015,"7":2016,"8":2017,"9":2018,"10":2019,"11":2020}," sales":{"0":0.5,"1":9.36,"2":52.0,"3":191.0,"4":352.0,"5":571.0,"6":912.0,"7":1207.0,"8":1682.69,"9":2135.0,"10":2684.0,"11":4982.0}}') dfresults = smf.ols("sales ~ year ", data=df).fit() results.summary()results = smf.ols("sales ~ year + np.square(year) ", data=df).fit()results = smf.ols("np.log(sales) ~ year", data=df).fit() results.summary()

總結

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