diff --git a/RFM.py b/RFM.py
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+++ b/RFM.py
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+# 1.Data Understanding
+import pandas as pd
+import datetime as dt
+pd.set_option('display.max_rows', 10)
+pd.set_option('display.max_columns', None)
+pd.set_option('display.float_format', lambda x: '%.5f' % x)
+df_ = pd.read_excel("datasets/online_retail_II.xlsx",
+                    sheet_name="Year 2010-2011")
+df = df_.copy()
+df.isnull().sum()
+df.groupby("Description").agg({"Quantity": "sum"}).sort_values("Quantity", ascending=False)
+df = df[~df["Invoice"].str.contains("C", na=False)]
+df["TotalPrice"] = df["Quantity"] * df["Price"]
+df.sort_values("Price", ascending=False)
+df.groupby("Country").agg({"TotalPrice": "sum"}).sort_values("TotalPrice", ascending=False)
+
+
+# 2.Data Preparation
+df.isnull().sum()
+df.dropna(inplace=True)
+df.describe([0.01, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, 0.95, 0.99]).T
+
+
+# 3.Calculating RFM Metrics
+df["InvoiceDate"].max()
+
+today_date = dt.datetime(2011, 12, 10)
+
+rfm = df.groupby('Customer ID').agg({'InvoiceDate': lambda date: (today_date - date.max()).days,
+                                     'Invoice': lambda num: len(num),
+                                     'TotalPrice': lambda TotalPrice: TotalPrice.sum()})
+rfm.columns = ['Recency', 'Frequency', 'Monetary']
+rfm = rfm[(rfm["Monetary"] > 0) & (rfm["Frequency"] > 0)]
+
+
+# 4.Calculating RFM Scores
+rfm["RecencyScore"] = pd.qcut(rfm['Recency'], 5, labels=[5, 4, 3, 2, 1])
+rfm["FrequencyScore"] = pd.qcut(rfm['Frequency'], 5, labels=[1, 2, 3, 4, 5])
+rfm["MonetaryScore"] = pd.qcut(rfm['Monetary'], 5, labels=[1, 2, 3, 4, 5])
+
+rfm["RFM_SCORE"] = (rfm['RecencyScore'].astype(str) +
+                    rfm['FrequencyScore'].astype(str) +
+                    rfm['MonetaryScore'].astype(str))
+
+
+# 5.Naming & Analysing RFM Segments
+seg_map = {
+    r'[1-2][1-2]': 'Hibernating',
+    r'[1-2][3-4]': 'At_Risk',
+    r'[1-2]5': 'Cant_Loose',
+    r'3[1-2]': 'About_to_Sleep',
+    r'33': 'Need_Attention',
+    r'[3-4][4-5]': 'Loyal_Customers',
+    r'41': 'Promising',
+    r'51': 'New_Customers',
+    r'[4-5][2-3]': 'Potential_Loyalists',
+    r'5[4-5]': 'Champions'
+}
+
+rfm['Segment'] = rfm['RecencyScore'].astype(str) + rfm['FrequencyScore'].astype(str)
+rfm['Segment'] = rfm['Segment'].replace(seg_map, regex=True)
+
+rfm[["Segment", "Recency", "Frequency", "Monetary"]].groupby("Segment").agg({"mean", "count"})