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  2. app.py +42 -4
  3. requirements.txt +3 -1
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@@ -0,0 +1,2912 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 2,
6
+ "metadata": {},
7
+ "outputs": [
8
+ {
9
+ "name": "stdout",
10
+ "output_type": "stream",
11
+ "text": [
12
+ "Requirement already satisfied: tqdm in c:\\python3.11.1\\lib\\site-packages (4.66.2)\n",
13
+ "Requirement already satisfied: colorama in c:\\python3.11.1\\lib\\site-packages (from tqdm) (0.4.6)\n",
14
+ "Note: you may need to restart the kernel to use updated packages.\n"
15
+ ]
16
+ },
17
+ {
18
+ "name": "stderr",
19
+ "output_type": "stream",
20
+ "text": [
21
+ "\n",
22
+ "[notice] A new release of pip is available: 24.1.2 -> 24.3.1\n",
23
+ "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
24
+ ]
25
+ }
26
+ ],
27
+ "source": [
28
+ "pip install tqdm"
29
+ ]
30
+ },
31
+ {
32
+ "cell_type": "code",
33
+ "execution_count": 3,
34
+ "metadata": {},
35
+ "outputs": [],
36
+ "source": [
37
+ "import pandas as pd\n",
38
+ "import numpy as np\n",
39
+ "import random\n",
40
+ "from tqdm import tqdm\n",
41
+ "import plotly.express as px\n",
42
+ "import matplotlib.pyplot as plt"
43
+ ]
44
+ },
45
+ {
46
+ "cell_type": "code",
47
+ "execution_count": 4,
48
+ "metadata": {},
49
+ "outputs": [
50
+ {
51
+ "data": {
52
+ "text/html": [
53
+ "<div>\n",
54
+ "<style scoped>\n",
55
+ " .dataframe tbody tr th:only-of-type {\n",
56
+ " vertical-align: middle;\n",
57
+ " }\n",
58
+ "\n",
59
+ " .dataframe tbody tr th {\n",
60
+ " vertical-align: top;\n",
61
+ " }\n",
62
+ "\n",
63
+ " .dataframe thead th {\n",
64
+ " text-align: right;\n",
65
+ " }\n",
66
+ "</style>\n",
67
+ "<table border=\"1\" class=\"dataframe\">\n",
68
+ " <thead>\n",
69
+ " <tr style=\"text-align: right;\">\n",
70
+ " <th></th>\n",
71
+ " <th>InvoiceNo</th>\n",
72
+ " <th>StockCode</th>\n",
73
+ " <th>Description</th>\n",
74
+ " <th>Quantity</th>\n",
75
+ " <th>InvoiceDate</th>\n",
76
+ " <th>UnitPrice</th>\n",
77
+ " <th>CustomerID</th>\n",
78
+ " <th>Country</th>\n",
79
+ " </tr>\n",
80
+ " </thead>\n",
81
+ " <tbody>\n",
82
+ " <tr>\n",
83
+ " <th>0</th>\n",
84
+ " <td>536365</td>\n",
85
+ " <td>85123A</td>\n",
86
+ " <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
87
+ " <td>6</td>\n",
88
+ " <td>2010-12-01 08:26:00</td>\n",
89
+ " <td>2.55</td>\n",
90
+ " <td>17850.0</td>\n",
91
+ " <td>United Kingdom</td>\n",
92
+ " </tr>\n",
93
+ " <tr>\n",
94
+ " <th>1</th>\n",
95
+ " <td>536365</td>\n",
96
+ " <td>71053</td>\n",
97
+ " <td>WHITE METAL LANTERN</td>\n",
98
+ " <td>6</td>\n",
99
+ " <td>2010-12-01 08:26:00</td>\n",
100
+ " <td>3.39</td>\n",
101
+ " <td>17850.0</td>\n",
102
+ " <td>United Kingdom</td>\n",
103
+ " </tr>\n",
104
+ " <tr>\n",
105
+ " <th>2</th>\n",
106
+ " <td>536365</td>\n",
107
+ " <td>84406B</td>\n",
108
+ " <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
109
+ " <td>8</td>\n",
110
+ " <td>2010-12-01 08:26:00</td>\n",
111
+ " <td>2.75</td>\n",
112
+ " <td>17850.0</td>\n",
113
+ " <td>United Kingdom</td>\n",
114
+ " </tr>\n",
115
+ " <tr>\n",
116
+ " <th>3</th>\n",
117
+ " <td>536365</td>\n",
118
+ " <td>84029G</td>\n",
119
+ " <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
120
+ " <td>6</td>\n",
121
+ " <td>2010-12-01 08:26:00</td>\n",
122
+ " <td>3.39</td>\n",
123
+ " <td>17850.0</td>\n",
124
+ " <td>United Kingdom</td>\n",
125
+ " </tr>\n",
126
+ " <tr>\n",
127
+ " <th>4</th>\n",
128
+ " <td>536365</td>\n",
129
+ " <td>84029E</td>\n",
130
+ " <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
131
+ " <td>6</td>\n",
132
+ " <td>2010-12-01 08:26:00</td>\n",
133
+ " <td>3.39</td>\n",
134
+ " <td>17850.0</td>\n",
135
+ " <td>United Kingdom</td>\n",
136
+ " </tr>\n",
137
+ " </tbody>\n",
138
+ "</table>\n",
139
+ "</div>"
140
+ ],
141
+ "text/plain": [
142
+ " InvoiceNo StockCode Description Quantity \\\n",
143
+ "0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 \n",
144
+ "1 536365 71053 WHITE METAL LANTERN 6 \n",
145
+ "2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 \n",
146
+ "3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 \n",
147
+ "4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 \n",
148
+ "\n",
149
+ " InvoiceDate UnitPrice CustomerID Country \n",
150
+ "0 2010-12-01 08:26:00 2.55 17850.0 United Kingdom \n",
151
+ "1 2010-12-01 08:26:00 3.39 17850.0 United Kingdom \n",
152
+ "2 2010-12-01 08:26:00 2.75 17850.0 United Kingdom \n",
153
+ "3 2010-12-01 08:26:00 3.39 17850.0 United Kingdom \n",
154
+ "4 2010-12-01 08:26:00 3.39 17850.0 United Kingdom "
155
+ ]
156
+ },
157
+ "execution_count": 4,
158
+ "metadata": {},
159
+ "output_type": "execute_result"
160
+ }
161
+ ],
162
+ "source": [
163
+ "df = pd.read_csv(r\"D:\\Customer Segmentation\\retail_sales.csv\")\n",
164
+ "df.head()"
165
+ ]
166
+ },
167
+ {
168
+ "cell_type": "markdown",
169
+ "metadata": {},
170
+ "source": [
171
+ "Data Cleaning"
172
+ ]
173
+ },
174
+ {
175
+ "cell_type": "code",
176
+ "execution_count": 5,
177
+ "metadata": {},
178
+ "outputs": [
179
+ {
180
+ "data": {
181
+ "text/html": [
182
+ "<div>\n",
183
+ "<style scoped>\n",
184
+ " .dataframe tbody tr th:only-of-type {\n",
185
+ " vertical-align: middle;\n",
186
+ " }\n",
187
+ "\n",
188
+ " .dataframe tbody tr th {\n",
189
+ " vertical-align: top;\n",
190
+ " }\n",
191
+ "\n",
192
+ " .dataframe thead th {\n",
193
+ " text-align: right;\n",
194
+ " }\n",
195
+ "</style>\n",
196
+ "<table border=\"1\" class=\"dataframe\">\n",
197
+ " <thead>\n",
198
+ " <tr style=\"text-align: right;\">\n",
199
+ " <th></th>\n",
200
+ " <th>Column</th>\n",
201
+ " <th>dtype</th>\n",
202
+ " <th>unique sample</th>\n",
203
+ " <th>n uniques</th>\n",
204
+ " <th>num of missing</th>\n",
205
+ " <th>mean of missing</th>\n",
206
+ " </tr>\n",
207
+ " </thead>\n",
208
+ " <tbody>\n",
209
+ " <tr>\n",
210
+ " <th>0</th>\n",
211
+ " <td>InvoiceNo</td>\n",
212
+ " <td>object</td>\n",
213
+ " <td>[536365, 536366, 536367, 536368, 536369]</td>\n",
214
+ " <td>25900</td>\n",
215
+ " <td>0</td>\n",
216
+ " <td>0.000000</td>\n",
217
+ " </tr>\n",
218
+ " <tr>\n",
219
+ " <th>1</th>\n",
220
+ " <td>StockCode</td>\n",
221
+ " <td>object</td>\n",
222
+ " <td>[85123A, 71053, 84406B, 84029G, 84029E]</td>\n",
223
+ " <td>4070</td>\n",
224
+ " <td>0</td>\n",
225
+ " <td>0.000000</td>\n",
226
+ " </tr>\n",
227
+ " <tr>\n",
228
+ " <th>2</th>\n",
229
+ " <td>Description</td>\n",
230
+ " <td>object</td>\n",
231
+ " <td>[WHITE HANGING HEART T-LIGHT HOLDER, WHITE MET...</td>\n",
232
+ " <td>4223</td>\n",
233
+ " <td>1454</td>\n",
234
+ " <td>0.002683</td>\n",
235
+ " </tr>\n",
236
+ " <tr>\n",
237
+ " <th>3</th>\n",
238
+ " <td>Quantity</td>\n",
239
+ " <td>int64</td>\n",
240
+ " <td>[6, 8, 2, 32, 3]</td>\n",
241
+ " <td>722</td>\n",
242
+ " <td>0</td>\n",
243
+ " <td>0.000000</td>\n",
244
+ " </tr>\n",
245
+ " <tr>\n",
246
+ " <th>4</th>\n",
247
+ " <td>InvoiceDate</td>\n",
248
+ " <td>object</td>\n",
249
+ " <td>[2010-12-01 08:26:00, 2010-12-01 08:28:00, 201...</td>\n",
250
+ " <td>23260</td>\n",
251
+ " <td>0</td>\n",
252
+ " <td>0.000000</td>\n",
253
+ " </tr>\n",
254
+ " <tr>\n",
255
+ " <th>5</th>\n",
256
+ " <td>UnitPrice</td>\n",
257
+ " <td>float64</td>\n",
258
+ " <td>[2.55, 3.39, 2.75, 7.65, 4.25]</td>\n",
259
+ " <td>1630</td>\n",
260
+ " <td>0</td>\n",
261
+ " <td>0.000000</td>\n",
262
+ " </tr>\n",
263
+ " <tr>\n",
264
+ " <th>6</th>\n",
265
+ " <td>CustomerID</td>\n",
266
+ " <td>float64</td>\n",
267
+ " <td>[17850.0, 13047.0, 12583.0, 13748.0, 15100.0]</td>\n",
268
+ " <td>4372</td>\n",
269
+ " <td>135080</td>\n",
270
+ " <td>0.249267</td>\n",
271
+ " </tr>\n",
272
+ " <tr>\n",
273
+ " <th>7</th>\n",
274
+ " <td>Country</td>\n",
275
+ " <td>object</td>\n",
276
+ " <td>[United Kingdom, France, Australia, Netherland...</td>\n",
277
+ " <td>38</td>\n",
278
+ " <td>0</td>\n",
279
+ " <td>0.000000</td>\n",
280
+ " </tr>\n",
281
+ " </tbody>\n",
282
+ "</table>\n",
283
+ "</div>"
284
+ ],
285
+ "text/plain": [
286
+ " Column dtype unique sample \\\n",
287
+ "0 InvoiceNo object [536365, 536366, 536367, 536368, 536369] \n",
288
+ "1 StockCode object [85123A, 71053, 84406B, 84029G, 84029E] \n",
289
+ "2 Description object [WHITE HANGING HEART T-LIGHT HOLDER, WHITE MET... \n",
290
+ "3 Quantity int64 [6, 8, 2, 32, 3] \n",
291
+ "4 InvoiceDate object [2010-12-01 08:26:00, 2010-12-01 08:28:00, 201... \n",
292
+ "5 UnitPrice float64 [2.55, 3.39, 2.75, 7.65, 4.25] \n",
293
+ "6 CustomerID float64 [17850.0, 13047.0, 12583.0, 13748.0, 15100.0] \n",
294
+ "7 Country object [United Kingdom, France, Australia, Netherland... \n",
295
+ "\n",
296
+ " n uniques num of missing mean of missing \n",
297
+ "0 25900 0 0.000000 \n",
298
+ "1 4070 0 0.000000 \n",
299
+ "2 4223 1454 0.002683 \n",
300
+ "3 722 0 0.000000 \n",
301
+ "4 23260 0 0.000000 \n",
302
+ "5 1630 0 0.000000 \n",
303
+ "6 4372 135080 0.249267 \n",
304
+ "7 38 0 0.000000 "
305
+ ]
306
+ },
307
+ "execution_count": 5,
308
+ "metadata": {},
309
+ "output_type": "execute_result"
310
+ }
311
+ ],
312
+ "source": [
313
+ "def report(df):\n",
314
+ " col = []\n",
315
+ " d_type = []\n",
316
+ " uniques = []\n",
317
+ " n_uniques = []\n",
318
+ " missing_values = []\n",
319
+ " mean_of_missing = []\n",
320
+ " \n",
321
+ " for i in df.columns:\n",
322
+ " col.append(i)\n",
323
+ " d_type.append(df[i].dtypes)\n",
324
+ " uniques.append(df[i].unique()[:5])\n",
325
+ " n_uniques.append(df[i].nunique())\n",
326
+ " missing_values.append(df[i].isna().sum())\n",
327
+ " mean_of_missing.append(df[i].isna().sum()/len(df))\n",
328
+ " \n",
329
+ " return pd.DataFrame({'Column': col, 'dtype': d_type, 'unique sample': uniques, 'n uniques': n_uniques, 'num of missing': missing_values, 'mean of missing': mean_of_missing })\n",
330
+ "\n",
331
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332
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+ },
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+ "execution_count": 6,
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+ " <th>Quantity</th>\n",
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+ " <th>UnitPrice</th>\n",
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+ " <td>406829.000000</td>\n",
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+ " </tr>\n",
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+ " <th>mean</th>\n",
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+ " </tr>\n",
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+ " <th>std</th>\n",
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+ " <tr>\n",
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+ " <th>25%</th>\n",
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+ " <td>2.000000</td>\n",
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+ " Quantity UnitPrice CustomerID\n",
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+ "count 406829.000000 406829.000000 406829.000000\n",
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+ "mean 12.061303 3.460471 15287.690570\n",
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+ "std 248.693370 69.315162 1713.600303\n",
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+ "min -80995.000000 0.000000 12346.000000\n",
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+ "25% 2.000000 1.250000 13953.000000\n",
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+ "50% 5.000000 1.950000 15152.000000\n",
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+ "75% 12.000000 3.750000 16791.000000\n",
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+ "max 80995.000000 38970.000000 18287.000000"
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+ "metadata": {},
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+ "source": [
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+ "Remove Negative Values"
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+ "cell_type": "code",
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+ "execution_count": 8,
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+ "metadata": {},
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+ "df = df[df['Quantity'] > 0]"
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+ ]
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+ "cell_type": "code",
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+ "execution_count": 9,
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532
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+ " <tr>\n",
536
+ " <th>23843</th>\n",
537
+ " <th>PAPER CRAFT , LITTLE BIRDIE</th>\n",
538
+ " <td>80995</td>\n",
539
+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>23166</th>\n",
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+ " <th>MEDIUM CERAMIC TOP STORAGE JAR</th>\n",
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+ " <td>77916</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>84077</th>\n",
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+ " <th>WORLD WAR 2 GLIDERS ASSTD DESIGNS</th>\n",
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+ " <td>54415</td>\n",
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+ " <th>85099B</th>\n",
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+ " </tr>\n",
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+ " <th>85123A</th>\n",
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+ " <th>84879</th>\n",
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+ " <th>21212</th>\n",
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+ " <th>22197</th>\n",
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+ "23843 PAPER CRAFT , LITTLE BIRDIE 80995\n",
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+ "TopProducts= df.pivot_table(\n",
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+ " index=['StockCode','Description'],\n",
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+ " values='Quantity',\n",
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+ " aggfunc='sum').sort_values(\n",
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+ " by='Quantity', ascending=False)\n",
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+ "gridcolor": "white",
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+ "linecolor": "white",
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+ "ticks": ""
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+ }
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+ "title": {
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+ "x": 0.05
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+ "xaxis": {
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+ "automargin": true,
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+ "gridcolor": "white",
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+ "linecolor": "white",
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+ "ticks": "",
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+ "title": {
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+ "standoff": 15
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+ "title": {
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+ "text": "Top 10 Products by Quantity Sold"
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+ "xaxis": {
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+ "anchor": "y",
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+ "metadata": {},
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+ "output_type": "display_data"
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+ }
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+ ],
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+ "source": [
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+ "TopProducts.reset_index(inplace=True)\n",
1528
+ "\n",
1529
+ "px.bar(TopProducts.head(10), y='Description', x='Quantity',\n",
1530
+ " orientation='h',\n",
1531
+ " title='Top 10 Products by Quantity Sold')"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "The product with the highest quantity sold is \"PAPER CRAFT, LITTLE BIRDIE,\" with approximately 80,000 units."
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
1545
+ "Let’s check out the number of unique customers:"
1546
+ ]
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+ },
1548
+ {
1549
+ "cell_type": "code",
1550
+ "execution_count": 13,
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+ "metadata": {},
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+ "outputs": [
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+ {
1554
+ "data": {
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+ "text/plain": [
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+ "4339"
1557
+ ]
1558
+ },
1559
+ "execution_count": 13,
1560
+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
1565
+ "customers = df[\"CustomerID\"].unique().tolist()\n",
1566
+ "len(customers)"
1567
+ ]
1568
+ },
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+ {
1570
+ "cell_type": "markdown",
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+ "metadata": {},
1572
+ "source": [
1573
+ "Top Products by Number of Customers"
1574
+ ]
1575
+ },
1576
+ {
1577
+ "cell_type": "code",
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+ "execution_count": 14,
1579
+ "metadata": {},
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+ "outputs": [],
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+ "source": [
1582
+ "CustomersBoughts = df.pivot_table(index=['StockCode','Description'],\n",
1583
+ " values='CustomerID',\n",
1584
+ " aggfunc=lambda x: len(x.unique())).sort_values(by='CustomerID', ascending=False)"
1585
+ ]
1586
+ },
1587
+ {
1588
+ "cell_type": "code",
1589
+ "execution_count": 15,
1590
+ "metadata": {},
1591
+ "outputs": [
1592
+ {
1593
+ "data": {
1594
+ "text/html": [
1595
+ "<div>\n",
1596
+ "<style scoped>\n",
1597
+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
1599
+ " }\n",
1600
+ "\n",
1601
+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
1605
+ " .dataframe thead th {\n",
1606
+ " text-align: right;\n",
1607
+ " }\n",
1608
+ "</style>\n",
1609
+ "<table border=\"1\" class=\"dataframe\">\n",
1610
+ " <thead>\n",
1611
+ " <tr style=\"text-align: right;\">\n",
1612
+ " <th></th>\n",
1613
+ " <th></th>\n",
1614
+ " <th>CustomerID</th>\n",
1615
+ " </tr>\n",
1616
+ " <tr>\n",
1617
+ " <th>StockCode</th>\n",
1618
+ " <th>Description</th>\n",
1619
+ " <th></th>\n",
1620
+ " </tr>\n",
1621
+ " </thead>\n",
1622
+ " <tbody>\n",
1623
+ " <tr>\n",
1624
+ " <th>22423</th>\n",
1625
+ " <th>REGENCY CAKESTAND 3 TIER</th>\n",
1626
+ " <td>881</td>\n",
1627
+ " </tr>\n",
1628
+ " <tr>\n",
1629
+ " <th>85123A</th>\n",
1630
+ " <th>WHITE HANGING HEART T-LIGHT HOLDER</th>\n",
1631
+ " <td>856</td>\n",
1632
+ " </tr>\n",
1633
+ " <tr>\n",
1634
+ " <th>47566</th>\n",
1635
+ " <th>PARTY BUNTING</th>\n",
1636
+ " <td>708</td>\n",
1637
+ " </tr>\n",
1638
+ " <tr>\n",
1639
+ " <th>84879</th>\n",
1640
+ " <th>ASSORTED COLOUR BIRD ORNAMENT</th>\n",
1641
+ " <td>678</td>\n",
1642
+ " </tr>\n",
1643
+ " <tr>\n",
1644
+ " <th>22720</th>\n",
1645
+ " <th>SET OF 3 CAKE TINS PANTRY DESIGN</th>\n",
1646
+ " <td>640</td>\n",
1647
+ " </tr>\n",
1648
+ " <tr>\n",
1649
+ " <th>21212</th>\n",
1650
+ " <th>PACK OF 72 RETROSPOT CAKE CASES</th>\n",
1651
+ " <td>635</td>\n",
1652
+ " </tr>\n",
1653
+ " <tr>\n",
1654
+ " <th>85099B</th>\n",
1655
+ " <th>JUMBO BAG RED RETROSPOT</th>\n",
1656
+ " <td>635</td>\n",
1657
+ " </tr>\n",
1658
+ " <tr>\n",
1659
+ " <th>22086</th>\n",
1660
+ " <th>PAPER CHAIN KIT 50'S CHRISTMAS</th>\n",
1661
+ " <td>613</td>\n",
1662
+ " </tr>\n",
1663
+ " <tr>\n",
1664
+ " <th>22457</th>\n",
1665
+ " <th>NATURAL SLATE HEART CHALKBOARD</th>\n",
1666
+ " <td>587</td>\n",
1667
+ " </tr>\n",
1668
+ " <tr>\n",
1669
+ " <th>22138</th>\n",
1670
+ " <th>BAKING SET 9 PIECE RETROSPOT</th>\n",
1671
+ " <td>581</td>\n",
1672
+ " </tr>\n",
1673
+ " </tbody>\n",
1674
+ "</table>\n",
1675
+ "</div>"
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+ ],
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+ "text/plain": [
1678
+ " CustomerID\n",
1679
+ "StockCode Description \n",
1680
+ "22423 REGENCY CAKESTAND 3 TIER 881\n",
1681
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+ "gridcolor": "white",
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+ "linecolor": "white",
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+ "ticks": ""
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+ }
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+ },
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+ "title": {
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+ "x": 0.05
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+ },
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+ "xaxis": {
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+ "automargin": true,
2563
+ "gridcolor": "white",
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+ "linecolor": "white",
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+ "ticks": "",
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+ "title": {
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+ "standoff": 15
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+ },
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+ "zerolinecolor": "white",
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+ "zerolinewidth": 2
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+ "zerolinewidth": 2
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+ }
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+ },
2585
+ "title": {
2586
+ "text": "Top 10 Products by Number of Customers"
2587
+ },
2588
+ "xaxis": {
2589
+ "anchor": "y",
2590
+ "domain": [
2591
+ 0,
2592
+ 1
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+ "title": {
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+ "text": "CustomerID"
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+ }
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+ }
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+ }
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+ }
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+ }
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+ },
2611
+ "metadata": {},
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+ "output_type": "display_data"
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+ }
2614
+ ],
2615
+ "source": [
2616
+ "CustomersBoughts.reset_index(inplace=True)\n",
2617
+ "\n",
2618
+ "px.bar(CustomersBoughts.head(10), y='Description', x='CustomerID',\n",
2619
+ " orientation='h',\n",
2620
+ " title='Top 10 Products by Number of Customers')"
2621
+ ]
2622
+ },
2623
+ {
2624
+ "cell_type": "markdown",
2625
+ "metadata": {},
2626
+ "source": [
2627
+ "Prepare Data For Modelling"
2628
+ ]
2629
+ },
2630
+ {
2631
+ "cell_type": "markdown",
2632
+ "metadata": {},
2633
+ "source": [
2634
+ "Splitting Data::::\n",
2635
+ " We will use 90% data of the customers as a training dataset to create word2vec embeddings."
2636
+ ]
2637
+ },
2638
+ {
2639
+ "cell_type": "code",
2640
+ "execution_count": 17,
2641
+ "metadata": {},
2642
+ "outputs": [],
2643
+ "source": [
2644
+ "random.shuffle(customers)\n",
2645
+ "\n",
2646
+ "# extract 90% of customer ID's\n",
2647
+ "customers_train = [customers[i] for i in range(round(0.9*len(customers)))]\n",
2648
+ "\n",
2649
+ "# split data into train and validation set\n",
2650
+ "train_df = df[df['CustomerID'].isin(customers_train)]\n",
2651
+ "validation_df = df[~df['CustomerID'].isin(customers_train)]"
2652
+ ]
2653
+ },
2654
+ {
2655
+ "cell_type": "markdown",
2656
+ "metadata": {},
2657
+ "source": [
2658
+ "Creating Sequence of Purchases for training dataset::::"
2659
+ ]
2660
+ },
2661
+ {
2662
+ "cell_type": "code",
2663
+ "execution_count": 18,
2664
+ "metadata": {},
2665
+ "outputs": [
2666
+ {
2667
+ "name": "stderr",
2668
+ "output_type": "stream",
2669
+ "text": [
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+ "100%|██████████| 3905/3905 [00:01<00:00, 1954.11it/s]\n"
2671
+ ]
2672
+ }
2673
+ ],
2674
+ "source": [
2675
+ "purchases_train = []\n",
2676
+ "\n",
2677
+ "for i in tqdm(customers_train):\n",
2678
+ " temp = train_df[train_df[\"CustomerID\"] == i][\"StockCode\"].tolist()\n",
2679
+ " purchases_train.append(temp)"
2680
+ ]
2681
+ },
2682
+ {
2683
+ "cell_type": "code",
2684
+ "execution_count": 19,
2685
+ "metadata": {},
2686
+ "outputs": [
2687
+ {
2688
+ "name": "stderr",
2689
+ "output_type": "stream",
2690
+ "text": [
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+ "100%|██████████| 434/434 [00:00<00:00, 2451.86it/s]\n"
2692
+ ]
2693
+ }
2694
+ ],
2695
+ "source": [
2696
+ "purchases_val = []\n",
2697
+ "\n",
2698
+ "for i in tqdm(validation_df['CustomerID'].unique()):\n",
2699
+ " temp = validation_df[validation_df[\"CustomerID\"] == i][\"StockCode\"].tolist()\n",
2700
+ " purchases_val.append(temp)"
2701
+ ]
2702
+ },
2703
+ {
2704
+ "cell_type": "markdown",
2705
+ "metadata": {},
2706
+ "source": [
2707
+ "Building a Recommendation System"
2708
+ ]
2709
+ },
2710
+ {
2711
+ "cell_type": "markdown",
2712
+ "metadata": {},
2713
+ "source": [
2714
+ "Building word2vec Embeddings for products"
2715
+ ]
2716
+ },
2717
+ {
2718
+ "cell_type": "code",
2719
+ "execution_count": 20,
2720
+ "metadata": {},
2721
+ "outputs": [
2722
+ {
2723
+ "name": "stdout",
2724
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2725
+ "text": [
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+ "Collecting gensim\n",
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+ " Downloading gensim-4.3.3-cp311-cp311-win_amd64.whl.metadata (8.2 kB)\n",
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+ "Requirement already satisfied: numpy<2.0,>=1.18.5 in c:\\python3.11.1\\lib\\site-packages (from gensim) (1.26.4)\n",
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+ "Requirement already satisfied: scipy<1.14.0,>=1.7.0 in c:\\python3.11.1\\lib\\site-packages (from gensim) (1.12.0)\n",
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+ "Requirement already satisfied: smart-open>=1.8.1 in c:\\python3.11.1\\lib\\site-packages (from gensim) (7.0.4)\n",
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+ "Requirement already satisfied: wrapt in c:\\python3.11.1\\lib\\site-packages (from smart-open>=1.8.1->gensim) (1.16.0)\n",
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+ " ---------------------------------------- 24.0/24.0 MB 4.8 MB/s eta 0:00:00\n",
2841
+ "Installing collected packages: gensim\n",
2842
+ "Successfully installed gensim-4.3.3\n",
2843
+ "Note: you may need to restart the kernel to use updated packages.\n"
2844
+ ]
2845
+ },
2846
+ {
2847
+ "name": "stderr",
2848
+ "output_type": "stream",
2849
+ "text": [
2850
+ "\n",
2851
+ "[notice] A new release of pip is available: 24.1.2 -> 24.3.1\n",
2852
+ "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
2853
+ ]
2854
+ }
2855
+ ],
2856
+ "source": [
2857
+ "pip install gensim"
2858
+ ]
2859
+ },
2860
+ {
2861
+ "cell_type": "code",
2862
+ "execution_count": 21,
2863
+ "metadata": {},
2864
+ "outputs": [],
2865
+ "source": [
2866
+ "from gensim.models import Word2Vec"
2867
+ ]
2868
+ },
2869
+ {
2870
+ "cell_type": "markdown",
2871
+ "metadata": {},
2872
+ "source": [
2873
+ "The parameters i will use:/n/n\n",
2874
+ "\n",
2875
+ "window = 15: Defines the maximum distance between the current and predicted word within a sentence./n\n",
2876
+ "sg = 1: Means the model will use the Skip-gram approach/n\n",
2877
+ "hs = 0: Indicates that hierarchical softmax is not used because there arn't large vocabularies./n\n",
2878
+ "negative=10: Sets the number of negative samples to 10./n\n",
2879
+ "alpha=0.03: Set learning rate for the process to 0.03./n\n",
2880
+ "min_alpha=0.0007: Sets the minimum learning rate to 0.0007./n"
2881
+ ]
2882
+ },
2883
+ {
2884
+ "cell_type": "code",
2885
+ "execution_count": null,
2886
+ "metadata": {},
2887
+ "outputs": [],
2888
+ "source": []
2889
+ }
2890
+ ],
2891
+ "metadata": {
2892
+ "kernelspec": {
2893
+ "display_name": "Python 3",
2894
+ "language": "python",
2895
+ "name": "python3"
2896
+ },
2897
+ "language_info": {
2898
+ "codemirror_mode": {
2899
+ "name": "ipython",
2900
+ "version": 3
2901
+ },
2902
+ "file_extension": ".py",
2903
+ "mimetype": "text/x-python",
2904
+ "name": "python",
2905
+ "nbconvert_exporter": "python",
2906
+ "pygments_lexer": "ipython3",
2907
+ "version": "3.11.4"
2908
+ }
2909
+ },
2910
+ "nbformat": 4,
2911
+ "nbformat_minor": 2
2912
+ }
app.py CHANGED
@@ -6,15 +6,17 @@ from sklearn.cluster import KMeans
6
  import matplotlib.pyplot as plt
7
  import seaborn as sns
8
  import plotly.express as px
 
9
 
10
  # Set the page configuration
11
- st.set_page_config(page_title="Customer Segmentation", layout="wide")
12
 
13
  # Title and Description
14
- st.title("🛒 Advanced Customer Segmentation App")
15
  st.markdown("""
16
- This application allows you to perform **Customer Segmentation** using RFM analysis and clustering.
17
- Upload your dataset, analyze the metrics, and visualize customer behaviors interactively.
 
18
  """)
19
 
20
  # Sidebar for uploading data
@@ -128,6 +130,42 @@ fig_cluster = px.scatter_3d(
128
  )
129
  st.plotly_chart(fig_cluster)
130
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
131
  # Export Data
132
  st.header("📤 Export Processed Data")
133
  if st.button("Export RFM Data"):
 
6
  import matplotlib.pyplot as plt
7
  import seaborn as sns
8
  import plotly.express as px
9
+ from mlxtend.frequent_patterns import apriori, association_rules
10
 
11
  # Set the page configuration
12
+ st.set_page_config(page_title="Customer Segmentation and Product Recommendation", layout="wide")
13
 
14
  # Title and Description
15
+ st.title("🛒Customer Segmentation & Product Recommendation App")
16
  st.markdown("""
17
+ This application performs **Customer Segmentation** using RFM analysis and clustering,
18
+ and provides **Product Recommendations** based on purchase patterns.
19
+ Upload your dataset, analyze customer behavior, and visualize results interactively.
20
  """)
21
 
22
  # Sidebar for uploading data
 
130
  )
131
  st.plotly_chart(fig_cluster)
132
 
133
+ # Product Recommendation
134
+ st.header("🛍️ Product Recommendation")
135
+ st.sidebar.subheader("Recommendation Parameters")
136
+ cluster_to_recommend = st.sidebar.selectbox("Select Cluster", rfm["Cluster"].unique())
137
+
138
+ # Filter data by cluster
139
+ customers_in_cluster = rfm[rfm["Cluster"] == cluster_to_recommend]["CustomerID"]
140
+ df_cluster = df[df["CustomerID"].isin(customers_in_cluster)]
141
+
142
+ # Association Rule Mining for Recommendations
143
+ basket = (
144
+ df_cluster.groupby(["InvoiceNo", "Description"])["Quantity"]
145
+ .sum()
146
+ .unstack()
147
+ .fillna(0)
148
+ .applymap(lambda x: 1 if x > 0 else 0)
149
+ )
150
+
151
+ frequent_itemsets = apriori(basket, min_support=0.05, use_colnames=True)
152
+ if not frequent_itemsets.empty:
153
+ rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)
154
+
155
+ # Display top recommendations
156
+ st.write(f"### Recommendations for Cluster {cluster_to_recommend}")
157
+ top_recommendations = rules.sort_values(by="confidence", ascending=False).head(10)
158
+ st.write(top_recommendations[["antecedents", "consequents", "support", "confidence", "lift"]])
159
+ else:
160
+ st.write("No significant patterns found for this cluster.")
161
+
162
+ st.write(f"### Recommendations for Cluster {cluster_to_recommend}")
163
+ if not rules.empty:
164
+ top_recommendations = rules.sort_values(by="confidence", ascending=False).head(10)
165
+ st.write(top_recommendations[["antecedents", "consequents", "support", "confidence", "lift"]])
166
+ else:
167
+ st.write("No significant patterns found for this cluster.")
168
+
169
  # Export Data
170
  st.header("📤 Export Processed Data")
171
  if st.button("Export RFM Data"):
requirements.txt CHANGED
@@ -4,4 +4,6 @@ pandas
4
  seaborn
5
  streamlit
6
  scikit-learn
7
- plotly
 
 
 
4
  seaborn
5
  streamlit
6
  scikit-learn
7
+ plotly
8
+ tqdm
9
+ mlxtend