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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "21f9d0cd",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import plotly.express as px\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.cluster import KMeans\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "9934bfd7",
"metadata": {},
"source": [
"# 1. Load Data\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c4e347b2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading data... (this may take a moment)\n"
]
}
],
"source": [
"file_path = '../data/raw/online_retail_II.xlsx'\n",
"print(\"Loading data... (this may take a moment)\")\n",
"df = pd.read_excel(file_path, sheet_name='Year 2009-2010')\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "9c8000ed",
"metadata": {},
"source": [
"# 2. Cleaning Pipeline\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "98b2164b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cleaning data...\n"
]
}
],
"source": [
"print(\"Cleaning data...\")\n",
"df_clean = df.dropna(subset=['Customer ID']).copy()\n"
]
},
{
"cell_type": "markdown",
"id": "31088f01",
"metadata": {},
"source": [
"# Ensure positive quantity and price\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1d4453bb",
"metadata": {},
"outputs": [],
"source": [
"df_clean = df_clean[(df_clean['Quantity'] > 0) & (df_clean['Price'] > 0)]\n"
]
},
{
"cell_type": "markdown",
"id": "b50b497f",
"metadata": {},
"source": [
"# Type conversion\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cf93dd5f",
"metadata": {},
"outputs": [],
"source": [
"\n",
"df_clean['Customer ID'] = df_clean['Customer ID'].astype(int).astype(str)\n",
"df_clean['InvoiceDate'] = pd.to_datetime(df_clean['InvoiceDate'])\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "c23f23b3",
"metadata": {},
"source": [
"# Create Total Price (Revenue per line item)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "74959902",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cleaned Data Rows: 407664\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Invoice</th>\n",
" <th>StockCode</th>\n",
" <th>Description</th>\n",
" <th>Quantity</th>\n",
" <th>InvoiceDate</th>\n",
" <th>Price</th>\n",
" <th>Customer ID</th>\n",
" <th>Country</th>\n",
" <th>Total Price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>489434</td>\n",
" <td>85048</td>\n",
" <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
" <td>12</td>\n",
" <td>2009-12-01 07:45:00</td>\n",
" <td>6.95</td>\n",
" <td>13085</td>\n",
" <td>United Kingdom</td>\n",
" <td>83.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>489434</td>\n",
" <td>79323P</td>\n",
" <td>PINK CHERRY LIGHTS</td>\n",
" <td>12</td>\n",
" <td>2009-12-01 07:45:00</td>\n",
" <td>6.75</td>\n",
" <td>13085</td>\n",
" <td>United Kingdom</td>\n",
" <td>81.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>489434</td>\n",
" <td>79323W</td>\n",
" <td>WHITE CHERRY LIGHTS</td>\n",
" <td>12</td>\n",
" <td>2009-12-01 07:45:00</td>\n",
" <td>6.75</td>\n",
" <td>13085</td>\n",
" <td>United Kingdom</td>\n",
" <td>81.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>489434</td>\n",
" <td>22041</td>\n",
" <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
" <td>48</td>\n",
" <td>2009-12-01 07:45:00</td>\n",
" <td>2.10</td>\n",
" <td>13085</td>\n",
" <td>United Kingdom</td>\n",
" <td>100.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>489434</td>\n",
" <td>21232</td>\n",
" <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
" <td>24</td>\n",
" <td>2009-12-01 07:45:00</td>\n",
" <td>1.25</td>\n",
" <td>13085</td>\n",
" <td>United Kingdom</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Invoice StockCode Description Quantity \\\n",
"0 489434 85048 15CM CHRISTMAS GLASS BALL 20 LIGHTS 12 \n",
"1 489434 79323P PINK CHERRY LIGHTS 12 \n",
"2 489434 79323W WHITE CHERRY LIGHTS 12 \n",
"3 489434 22041 RECORD FRAME 7\" SINGLE SIZE 48 \n",
"4 489434 21232 STRAWBERRY CERAMIC TRINKET BOX 24 \n",
"\n",
" InvoiceDate Price Customer ID Country Total Price \n",
"0 2009-12-01 07:45:00 6.95 13085 United Kingdom 83.4 \n",
"1 2009-12-01 07:45:00 6.75 13085 United Kingdom 81.0 \n",
"2 2009-12-01 07:45:00 6.75 13085 United Kingdom 81.0 \n",
"3 2009-12-01 07:45:00 2.10 13085 United Kingdom 100.8 \n",
"4 2009-12-01 07:45:00 1.25 13085 United Kingdom 30.0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_clean['Total Price'] = df_clean['Quantity'] * df_clean['Price']\n",
"\n",
"print(f\"Cleaned Data Rows: {df_clean.shape[0]}\")\n",
"df_clean.head()"
]
},
{
"cell_type": "markdown",
"id": "3ee2b989",
"metadata": {},
"source": [
"# 1. Calculate Reference Date\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "383e45a1",
"metadata": {},
"outputs": [],
"source": [
"reference_date = df_clean['InvoiceDate'].max() + pd.DateOffset(days=1)\n"
]
},
{
"cell_type": "markdown",
"id": "0306b779",
"metadata": {},
"source": [
"# 2. Aggregation (The RFM Table)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d0d2d1da",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"rfm = df_clean.groupby('Customer ID').agg({\n",
" 'InvoiceDate': lambda x: (reference_date - x.max()).days, # Recency\n",
" 'Invoice': 'nunique', # Frequency\n",
" 'Total Price': 'sum' # Monetary\n",
"}).reset_index()\n"
]
},
{
"cell_type": "markdown",
"id": "a51111e3",
"metadata": {},
"source": [
"# 3. Rename Columns (Fixing the typo here!)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c92104a8",
"metadata": {},
"outputs": [],
"source": [
"\n",
"rfm.rename(columns={\n",
" 'InvoiceDate': 'Recency',\n",
" 'Invoice': 'Frequency',\n",
" 'Total Price': 'Monetary' \n",
"}, inplace=True)\n"
]
},
{
"cell_type": "markdown",
"id": "afb4017a",
"metadata": {},
"source": [
"# 4. Remove zeros or negatives for Log Transformation stability\n",
"# (Log of 0 or negative numbers crashes the math)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "de32bc45",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RFM Shape: (4312, 4)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Customer ID</th>\n",
" <th>Recency</th>\n",
" <th>Frequency</th>\n",
" <th>Monetary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>12346</td>\n",
" <td>165</td>\n",
" <td>11</td>\n",
" <td>372.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12347</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1323.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>12348</td>\n",
" <td>74</td>\n",
" <td>1</td>\n",
" <td>222.16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>12349</td>\n",
" <td>43</td>\n",
" <td>3</td>\n",
" <td>2671.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>12351</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>300.93</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Customer ID Recency Frequency Monetary\n",
"0 12346 165 11 372.86\n",
"1 12347 3 2 1323.32\n",
"2 12348 74 1 222.16\n",
"3 12349 43 3 2671.14\n",
"4 12351 11 1 300.93"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"rfm = rfm[rfm['Monetary'] > 0]\n",
"rfm = rfm[rfm['Frequency'] > 0]\n",
"\n",
"print(f\"RFM Shape: {rfm.shape}\")\n",
"rfm.head()"
]
},
{
"cell_type": "markdown",
"id": "6b7458f7",
"metadata": {},
"source": [
"# 1. Log Transformation (Handling skewed data)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "92e502c7",
"metadata": {},
"outputs": [],
"source": [
"\n",
"rfm_log = np.log1p(rfm[['Recency', 'Frequency', 'Monetary']])\n"
]
},
{
"cell_type": "markdown",
"id": "1c876869",
"metadata": {},
"source": [
"\n",
"# 2. Scaling"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4851d297",
"metadata": {},
"outputs": [],
"source": [
"\n",
"scaler = StandardScaler()\n",
"rfm_normalized = scaler.fit_transform(rfm_log)\n"
]
},
{
"cell_type": "markdown",
"id": "da5765c2",
"metadata": {},
"source": [
"\n",
"# 3. Elbow Method"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "1e1e49cc",
"metadata": {},
"outputs": [],
"source": [
"\n",
"inertia = []\n",
"range_n_clusters = range(1, 11)\n",
"\n",
"for k in range_n_clusters:\n",
" kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
" kmeans.fit(rfm_normalized)\n",
" inertia.append(kmeans.inertia_)\n"
]
},
{
"cell_type": "markdown",
"id": "c53b1a8b",
"metadata": {},
"source": [
"\n",
"# 4. Plot (Fixed plt.figure issue)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6f5d473b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plt.figure(figsize=(8, 5))\n",
"plt.plot(range_n_clusters, inertia, marker='o')\n",
"plt.title('Elbow Method for Optimal k')\n",
"plt.xlabel('Number of Clusters')\n",
"plt.ylabel('Inertia')\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "9a730d39",
"metadata": {},
"source": [
"# 1. Train Model with k=3"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "387ea370",
"metadata": {},
"outputs": [],
"source": [
"\n",
"kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)\n",
"rfm['Cluster'] = kmeans.fit_predict(rfm_normalized)\n"
]
},
{
"cell_type": "markdown",
"id": "89e8e351",
"metadata": {},
"source": [
"# 2. Interpret Segments (Calculate means per cluster)\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "1616dd83",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cluster Centroids (Interpret this!):\n",
" Cluster Recency Frequency Monetary\n",
"0 0 54.355077 3.428815 1300.115946\n",
"1 1 17.919951 13.427340 7360.172990\n",
"2 2 162.782959 1.303972 324.374435\n"
]
}
],
"source": [
"\n",
"cluster_summary = rfm.groupby('Cluster').agg({\n",
" 'Recency': 'mean',\n",
" 'Frequency': 'mean',\n",
" 'Monetary': 'mean'\n",
"}).reset_index()\n",
"\n",
"print(\"Cluster Centroids (Interpret this!):\")\n",
"print(cluster_summary)\n"
]
},
{
"cell_type": "markdown",
"id": "8ac19462",
"metadata": {},
"source": [
"\n",
"# 3. Dynamic Logic to define Segments\n",
"# We sort clusters by 'Monetary' value to define who is who.\n",
"# The Cluster with HIGHEST Monetary mean is 'Champions'\n",
"# The Cluster with LOWEST Monetary mean is 'Hibernating'"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "105b4816",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Generated Mapping based on Data: {2: 'Hibernating', 0: 'Potential Loyalists', 1: 'Champions'}\n",
"Segment\n",
"Potential Loyalists 1763\n",
"Hibernating 1737\n",
"Champions 812\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"\n",
"def get_segment_dict(summary_df):\n",
" summary_df = summary_df.sort_values(by='Monetary', ascending=True)\n",
" summary_df['Segment_Name'] = ['Hibernating', 'Potential Loyalists', 'Champions']\n",
" # Create a dictionary {ClusterID: Name}\n",
" return dict(zip(summary_df['Cluster'], summary_df['Segment_Name']))\n",
"\n",
"segment_map = get_segment_dict(cluster_summary)\n",
"print(\"\\nGenerated Mapping based on Data:\", segment_map)\n",
"\n",
"# 4. Apply the mapping\n",
"rfm['Segment'] = rfm['Cluster'].map(segment_map)\n",
"\n",
"print(rfm['Segment'].value_counts())"
]
},
{
"cell_type": "markdown",
"id": "e1ffb986",
"metadata": {},
"source": [
"# Calculate Log columns strictly for plotting\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f716edd5",
"metadata": {},
"outputs": [],
"source": [
"rfm['Recency_Log'] = np.log1p(rfm['Recency'])\n",
"rfm['Frequency_Log'] = np.log1p(rfm['Frequency'])\n",
"rfm['Monetary_Log'] = np.log1p(rfm['Monetary'])\n"
]
},
{
"cell_type": "markdown",
"id": "3ab1dfd8",
"metadata": {},
"source": [
"# 3D Scatter Plot\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "41b14a84",
"metadata": {},
"outputs": [],
"source": [
"\n",
"fig = px.scatter_3d(rfm, \n",
" x='Recency_Log', \n",
" y='Frequency_Log', \n",
" z='Monetary_Log',\n",
" color='Segment', \n",
" opacity=0.7,\n",
" title='3D Customer Segments (Log Scaled)',\n",
" labels={\n",
" 'Recency_Log': 'Recency (Log)', \n",
" 'Frequency_Log': 'Frequency (Log)', \n",
" 'Monetary_Log': 'Monetary (Log)'\n",
" },\n",
" color_discrete_map={\n",
" 'Champions': 'green', \n",
" 'Potential Loyalists': 'blue', \n",
" 'Hibernating': 'red'}\n",
" )\n"
]
},
{
"cell_type": "markdown",
"id": "7ab7c5dc",
"metadata": {},
"source": [
"# Ensure the colors apply even if map naming differs slightly\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "c0f1351b",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "Segment=Potential Loyalists<br>Recency (Log)=%{x}<br>Frequency (Log)=%{y}<br>Monetary (Log)=%{z}<extra></extra>",
"legendgroup": "Potential Loyalists",
"marker": {
"color": "blue",
"opacity": 0.7,
"symbol": "circle"
},
"mode": "markers",
"name": "Potential Loyalists",
"scene": "scene",
"showlegend": true,
"type": "scatter3d",
"x": {
"bdata": 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",
"dtype": "f8"
},
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",
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",
"dtype": "f8"
}
},
{
"hovertemplate": "Segment=Hibernating<br>Recency (Log)=%{x}<br>Frequency (Log)=%{y}<br>Monetary (Log)=%{z}<extra></extra>",
"legendgroup": "Hibernating",
"marker": {
"color": "red",
"opacity": 0.7,
"symbol": "circle"
},
"mode": "markers",
"name": "Hibernating",
"scene": "scene",
"showlegend": true,
"type": "scatter3d",
"x": {
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",
"dtype": "f8"
},
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",
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",
"dtype": "f8"
}
},
{
"hovertemplate": "Segment=Champions<br>Recency (Log)=%{x}<br>Frequency (Log)=%{y}<br>Monetary (Log)=%{z}<extra></extra>",
"legendgroup": "Champions",
"marker": {
"color": "green",
"opacity": 0.7,
"symbol": "circle"
},
"mode": "markers",
"name": "Champions",
"scene": "scene",
"showlegend": true,
"type": "scatter3d",
"x": {
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",
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}
}
],
"layout": {
"legend": {
"title": {
"text": "Segment"
},
"tracegroupgap": 0
},
"scene": {
"domain": {
"x": [
0,
1
],
"y": [
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1
]
},
"xaxis": {
"title": {
"text": "Recency (Days)"
}
},
"yaxis": {
"title": {
"text": "Frequency (Count)"
}
},
"zaxis": {
"title": {
"text": "Monetary ($)"
}
}
},
"template": {
"data": {
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{
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},
"error_y": {
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},
"marker": {
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"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
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"width": 0.5
},
"pattern": {
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"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
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"aaxis": {
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"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
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"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
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"colorbar": {
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"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
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"ticks": ""
},
"colorscale": [
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[
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],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
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"ticks": ""
},
"colorscale": [
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"type": "heatmap"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
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"ticks": ""
},
"colorscale": [
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"type": "histogram2d"
}
],
"histogram2dcontour": [
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"ticks": ""
},
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"gridwidth": 2,
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"gridcolor": "white",
"gridwidth": 2,
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"gridwidth": 2,
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"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
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"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
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},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "3D Customer Segments (Log Scaled)"
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"fig.update_layout(scene = dict(\n",
" xaxis_title='Recency (Days)',\n",
" yaxis_title='Frequency (Count)',\n",
" zaxis_title='Monetary ($)'))\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "0d58bdab",
"metadata": {},
"source": [
"# Save the segmented data for the next step"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "fff9d9e2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Segments saved successfully.\n"
]
}
],
"source": [
"\n",
"rfm.to_csv('../data/processed/rfm_segments.csv', index=False)\n",
"print(\"Segments saved successfully.\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|