Necessary files were added in the project

cltv_prediction.py

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+##############################################################
+# CLTV Prediction with BG-NBD and Gamma-Gamma Model
+##############################################################
+
+###############################################################
+# TASKS
+###############################################################
+
+import pandas as pd
+import datetime as dt
+from lifetimes import BetaGeoFitter
+from lifetimes import GammaGammaFitter
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+
+pd.set_option('display.max_columns', 50)
+pd.set_option('display.max_rows', 50)
+pd.set_option('display.float_format', lambda x: '%.2f' % x)
+pd.set_option('display.expand_frame_repr', False)
+pd.set_option('display.width', 500)
+
+###############################################################
+# Task 1: Preparing data
+###############################################################
+
+# 1. Read Flo_data_20K.CSV dataset. Create a copy of the DataFrame.
+
+main_df = pd.read_csv('CLTV/flo_data_20k.csv')
+df = main_df.copy()
+df.head()
+
+
+def missing_values_analysis(df):
+    na_columns = [col for col in df.columns if df[col].isnull().sum() > 0]
+    n_miss = df[na_columns].isnull().sum().sort_values(ascending=True)
+    ratio = (df[na_columns].isnull().sum() / df.shape[0] * 100).sort_values(ascending=True)
+    missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['Total Missing Values', 'Ratio'])
+    missing_df = pd.DataFrame(missing_df)
+    return missing_df
+
+
+def check_dataframe(df, row_num=5):
+    print("*************** Dataset Shape ***************")
+    print("No. of Rows:", df.shape[0], "\nNo. of Columns:", df.shape[1])
+    print("*************** Types of Columns ***************")
+    print(df.dtypes)
+    print(f"*************** First {row_num} Rows ***************")
+    print(df.head(row_num))
+    print(f"*************** Last {row_num} Rows ***************")
+    print(df.tail(row_num))
+    print("*************** Summary Statistics of The Dataset ***************")
+    print(df.describe().T)
+    print("*************** Dataset Missing Values Analysis ***************")
+    print(missing_values_analysis(df))
+
+
+check_dataframe(df)
+
+
+# # 2. Define Outlier_thresholds and Replace_with_thresholds functions that are required to suppress contradictory
+# values. Note: Frequency values must be integer when calculating CLTV. Therefore, round the upper and lower
+# limits with round ().
+
+
+def outlier_thresholds(dataframe, column):
+    quartile_1 = dataframe[column].quantile(0.01)
+    quartile_3 = dataframe[column].quantile(0.99)
+    iqr_range = quartile_3 - quartile_1
+    up_limit = quartile_3 + 1.5 * iqr_range
+    low_limit = quartile_1 - 1.5 * iqr_range
+    return low_limit, up_limit
+
+
+def replace_with_thresholds(dataframe, variable):
+    low_limit, up_limit = outlier_thresholds(dataframe, variable)
+    dataframe.loc[(dataframe[variable] < low_limit), variable] = round(low_limit, 0)
+    dataframe.loc[(dataframe[variable] > up_limit), variable] = round(up_limit, 0)
+
+
+# 3. Determine if variables "order_num_total_ever_online", "order_num_total_ever_offline",
+# "customer_value_total_ever_offline" and "customer_value_total_ever_online" have contradictory values
+
+
+threshold_columns = ["order_num_total_ever_online", "order_num_total_ever_offline",
+                     "customer_value_total_ever_offline", "customer_value_total_ever_online"]
+
+for col in threshold_columns:
+    replace_with_thresholds(df, col)
+
+check_dataframe(df)
+
+# 4. Omnichannel states that customers shop both online and offline platforms. Create new variables for
+# the total number of shopping and spending of each customer.
+
+df["order_num_total"] = df["order_num_total_ever_online"] + df["order_num_total_ever_offline"]
+df["customer_value_total"] = df["customer_value_total_ever_offline"] + df["customer_value_total_ever_online"]
+
+# 5. Examine the variable types. Turn the type of variables containing "date" to the datetime type.
+
+date_columns = df.columns[df.columns.str.contains("date")]
+df[date_columns] = df[date_columns].apply(pd.to_datetime)
+df.info()
+
+###############################################################
+# Task 2: Creation of CLTV data structure
+###############################################################
+
+# 1.Take 2 days after the last shopping on the data set as an analysis date.
+
+df["last_order_date"].max()
+last_date = dt.datetime(2021, 6, 1)
+
+# 2.Create a new cltv dataframe with customer_id, recency_cltv_weekly, T_weekly, Frequency and Monetary_CLTV_AVG values.
+
+cltv_df = pd.DataFrame()
+cltv_df["customer_id"] = df["master_id"]
+cltv_df["recency_cltv_weekly"] = ((df["last_order_date"] - df["first_order_date"]).astype('timedelta64[D]')) / 7
+cltv_df["T_weekly"] = ((last_date - df["first_order_date"]).astype('timedelta64[D]')) / 7
+cltv_df["frequency"] = df["order_num_total"]
+cltv_df["monetary_cltv_avg"] = df["customer_value_total"] / df["order_num_total"]
+
+cltv_df.head()
+
+###############################################################
+# Task 3: Establishing BG/NBD and Gamma-Hamma models, calculation of 6 months of CLTV
+###############################################################
+
+# 1. Create the BG/NBD model
+
+bgf = BetaGeoFitter(penalizer_coef=0.001)
+bgf.fit(cltv_df['frequency'],
+        cltv_df['recency_cltv_weekly'],
+        cltv_df['T_weekly'])
+
+# Estimate the expected purchases from customers in 3 months and add to the CLTV DataFrame as exp_sales_3_month.
+
+cltv_df["exp_sales_3_month"] = bgf.predict(4 * 3,
+                                           cltv_df['frequency'],
+                                           cltv_df['recency_cltv_weekly'],
+                                           cltv_df['T_weekly'])
+
+# Estimate the expected purchases from customers in 6 months and add to the CLTV DataFrame as exp_sales_6_month.
+
+cltv_df["exp_sales_6_month"] = bgf.predict(4 * 6,
+                                           cltv_df['frequency'],
+                                           cltv_df['recency_cltv_weekly'],
+                                           cltv_df['T_weekly'])
+
+# We examine the 10 people who will make the most purchase in the 3rd and 6th month.
+
+cltv_df.groupby('customer_id').agg({'exp_sales_3_month': 'sum',
+                                    'exp_sales_6_month': 'sum'}).sort_values(by='exp_sales_6_month',
+                                                                             ascending=False).head(10)
+
+# 2. Fit the gamma-gamma model. Estimates the average value of customers and add to CLTV DataFrame as exp_average_value.
+
+ggf = GammaGammaFitter(penalizer_coef=0.01)
+ggf.fit(cltv_df['frequency'], cltv_df['monetary_cltv_avg'])
+cltv_df["exp_average_value"] = ggf.conditional_expected_average_profit(cltv_df['frequency'],
+                                                                       cltv_df['monetary_cltv_avg'])
+cltv_df.head()
+
+# 3.Calculate 6-month CLTV and add dataframe with the name 'cltv'.
+
+cltv_df['cltv'] = ggf.customer_lifetime_value(bgf,
+                                              cltv_df['frequency'],
+                                              cltv_df['recency_cltv_weekly'],
+                                              cltv_df['T_weekly'],
+                                              cltv_df['monetary_cltv_avg'],
+                                              time=6,
+                                              freq="W",
+                                              discount_rate=0.01)
+
+# Observe 20 people at the highest value of CLTV.
+
+cltv_df.sort_values("cltv", ascending=False).head(20)
+
+###############################################################
+# Task 4: Creation of segments according to CLTV
+###############################################################
+
+# 1. According to 6 -month CLTV, separate all your customers into 4 groups (segment) and add the group names to
+# the data set. Assign with the name CLTV_segment.
+
+cltv_df["cltv_segment"] = pd.qcut(cltv_df["cltv"], 4, labels=["D", "C", "B", "A"])
+cltv_df.head()
+
+# 2.Check out the averages of the segments of the Recency, Frequency and Monetary.
+
+cltv_df.groupby("cltv_segment").agg({"count", "mean", "sum"})