meraldo-aliz
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| import numpy as np | |
| import scipy.stats as stats | |
| from matplotlib import pyplot as plt | |
| fig, (ax_gamma, ax_beta) = plt.subplots(ncols=1, nrows =2, figsize = (20, 16)) | |
| x_gamma = np.linspace(0, 10, 1000) | |
| y_gamma = stats.gamma.pdf(x_gamma, a=bgf.params_["alpha"], scale=bgf.params_["r"]) | |
| ax_gamma.plot(x_gamma, y_gamma, "-") | |
| ax_gamma.set_title(f'Gamma distribution (alpha = {bgf.params_["alpha"]:.2f}, scale (r) = {bgf.params_["r"]:.2f})') |
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| alive_prob = bgf.conditional_probability_alive(frequency=sample_customer['frequency_cal'], | |
| recency=sample_customer['recency_cal'], | |
| T=sample_customer['T_cal']) | |
| alive_prob # = 0.57089896 |
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| # This function calculates the conditional expected number of transactions in the given time length | |
| n_transactions_pred = bgf.predict(t=26, # we set it to 26 weeks (the length of the observation period) | |
| frequency=sample_customer['frequency_cal'], | |
| recency=sample_customer['recency_cal'], | |
| T=sample_customer['T_cal']) | |
| n_transactions_pred # = 0.7647440846242359 |
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| # First, we choose a sample customer. | |
| sample_customer = rfm_cal_holdout.iloc[20] | |
| # Let's inspect his frequency, recency and T both for the calibration and observation periods: | |
| sample_customer |
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| from lifetimes.plotting import plot_period_transactions | |
| _ = plot_period_transactions(bgf) |
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| bgf.summary |
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| from lifetimes import BetaGeoFitter | |
| # instantiation of BG-NBD model | |
| bgf = BetaGeoFitter(penalizer_coef=0.0) | |
| # fitting of BG-NBD model | |
| bgf.fit(frequency=rfm_cal_holdout['frequency_cal'], | |
| recency=rfm_cal_holdout['recency_cal'], | |
| T=rfm_cal_holdout['T_cal']) |
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| from lifetimes.utils import summary_data_from_transaction_data | |
| rfm = summary_data_from_transaction_data(transactions=transactions, | |
| customer_id_col='customer_id', | |
| datetime_col='date', | |
| monetary_value_col = 'amount', | |
| observation_period_end=pd.to_datetime('1998-06-30'), | |
| freq='W') | |
| rfm.head(3) |
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| from lifetimes.utils import calibration_and_holdout_data | |
| rfm_cal_holdout = calibration_and_holdout_data(transactions=transactions, | |
| customer_id_col='customer_id', | |
| datetime_col='date', | |
| monetary_value_col = 'amount', | |
| freq='W', | |
| calibration_period_end='1998-01-01', | |
| observation_period_end='1998-06-30' ) |
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| from lifetimes.utils import summary_data_from_transaction_data | |
| rfm = summary_data_from_transaction_data(transactions=transactions, | |
| customer_id_col='customer_id', | |
| datetime_col='date', | |
| monetary_value_col = 'amount', | |
| observation_period_end=pd.to_datetime('1998-06-30'), | |
| freq='W') | |
| rfm.head(3) |