advanceClientSegmentationKmodes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "beecb873",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kmodes.kmodes import KModes\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from matplotlib import pyplot as plt\n",
    "from kmodes.kprototypes import KPrototypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60ee6f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Settlement Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>124670</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100000002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>150773</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100000003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>89210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100000004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>171565</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>149031</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Sex  Marital Status  Age  Education  Income  Occupation  \\\n",
       "0  100000001    0               0   67          2  124670           1   \n",
       "1  100000002    1               1   22          1  150773           1   \n",
       "2  100000003    0               0   49          1   89210           0   \n",
       "3  100000004    0               0   45          1  171565           1   \n",
       "4  100000005    0               0   53          1  149031           1   \n",
       "\n",
       "   Settlement Size  \n",
       "0                2  \n",
       "1                2  \n",
       "2                0  \n",
       "3                1  \n",
       "4                1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"segmentation_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "acad67a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Settlement Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>100001996</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>123525</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>100001997</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>117744</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>100001998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>86400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>100001999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>97968</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>100002000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>68416</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Sex  Marital Status  Age  Education  Income  Occupation  \\\n",
       "1995  100001996    1               0   47          1  123525           0   \n",
       "1996  100001997    1               1   27          1  117744           1   \n",
       "1997  100001998    0               0   31          0   86400           0   \n",
       "1998  100001999    1               1   24          1   97968           0   \n",
       "1999  100002000    0               0   25          0   68416           0   \n",
       "\n",
       "      Settlement Size  \n",
       "1995                0  \n",
       "1996                0  \n",
       "1997                0  \n",
       "1998                0  \n",
       "1999                0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79d95696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                 0\n",
       "Sex                0\n",
       "Marital Status     0\n",
       "Age                0\n",
       "Education          0\n",
       "Income             0\n",
       "Occupation         0\n",
       "Settlement Size    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We don't have any null data, see:\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f4a41808",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>100000001</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100000002</td>\n",
       "      <td>22</td>\n",
       "      <td>150773</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100000003</td>\n",
       "      <td>49</td>\n",
       "      <td>89210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100000004</td>\n",
       "      <td>45</td>\n",
       "      <td>171565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000005</td>\n",
       "      <td>53</td>\n",
       "      <td>149031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>100001996</td>\n",
       "      <td>47</td>\n",
       "      <td>123525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>100001997</td>\n",
       "      <td>27</td>\n",
       "      <td>117744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>100001998</td>\n",
       "      <td>31</td>\n",
       "      <td>86400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>100001999</td>\n",
       "      <td>24</td>\n",
       "      <td>97968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>100002000</td>\n",
       "      <td>25</td>\n",
       "      <td>68416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Age  Income\n",
       "0     100000001   67  124670\n",
       "1     100000002   22  150773\n",
       "2     100000003   49   89210\n",
       "3     100000004   45  171565\n",
       "4     100000005   53  149031\n",
       "...         ...  ...     ...\n",
       "1995  100001996   47  123525\n",
       "1996  100001997   27  117744\n",
       "1997  100001998   31   86400\n",
       "1998  100001999   24   97968\n",
       "1999  100002000   25   68416\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Income and Age Data Normalization\n",
    "#Before Scaling/Normalization we keep our normal values in temp variables\n",
    "df_temp = df[['ID', 'Age', 'Income']]\n",
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c082c599",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "scaler.fit(df[['Age']])\n",
    "df['Age'] = scaler.transform(df[['Age']])\n",
    "scaler.fit(df[['Income']])\n",
    "df['Income'] = scaler.transform(df[['Income']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3eba458f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop ID before analysis\n",
    "#Since ID is not used in analysis\n",
    "df = df.drop(['ID'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3936efc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mark_array = df.values\n",
    "mark_array[:, 2] = mark_array[:, 2].astype(float)\n",
    "mark_array[:, 4] = mark_array[:, 4].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55d943e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
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       "      <th>Settlement Size</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.844828</td>\n",
       "      <td>2</td>\n",
       "      <td>0.324781</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>1</td>\n",
       "      <td>0.420210</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.534483</td>\n",
       "      <td>1</td>\n",
       "      <td>0.195144</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.465517</td>\n",
       "      <td>1</td>\n",
       "      <td>0.496223</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.603448</td>\n",
       "      <td>1</td>\n",
       "      <td>0.413842</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Sex  Marital Status       Age  Education    Income  Occupation  \\\n",
       "0    0               0  0.844828          2  0.324781           1   \n",
       "1    1               1  0.068966          1  0.420210           1   \n",
       "2    0               0  0.534483          1  0.195144           0   \n",
       "3    0               0  0.465517          1  0.496223           1   \n",
       "4    0               0  0.603448          1  0.413842           1   \n",
       "\n",
       "   Settlement Size  \n",
       "0                2  \n",
       "1                2  \n",
       "2                0  \n",
       "3                1  \n",
       "4                1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c8875b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/20, moves: 749, ncost: 165.55715598110737\n",
      "Run: 1, iteration: 2/20, moves: 364, ncost: 159.86552969695256\n",
      "Run: 1, iteration: 3/20, moves: 47, ncost: 159.64466107428962\n",
      "Run: 1, iteration: 4/20, moves: 4, ncost: 159.64166840806612\n",
      "Run: 1, iteration: 5/20, moves: 1, ncost: 159.64165243166184\n",
      "Run: 1, iteration: 6/20, moves: 0, ncost: 159.64165243166184\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/20, moves: 729, ncost: 186.48454531741933\n",
      "Run: 2, iteration: 2/20, moves: 371, ncost: 179.4153572685592\n",
      "Run: 2, iteration: 3/20, moves: 198, ncost: 172.16474187305715\n",
      "Run: 2, iteration: 4/20, moves: 70, ncost: 171.91755338817984\n",
      "Run: 2, iteration: 5/20, moves: 12, ncost: 171.87225530762726\n",
      "Run: 2, iteration: 6/20, moves: 5, ncost: 171.8596899875025\n",
      "Run: 2, iteration: 7/20, moves: 7, ncost: 171.8231007311186\n",
      "Run: 2, iteration: 8/20, moves: 8, ncost: 171.7827858611016\n",
      "Run: 2, iteration: 9/20, moves: 6, ncost: 171.75204123997696\n",
      "Run: 2, iteration: 10/20, moves: 2, ncost: 171.7512691958265\n",
      "Run: 2, iteration: 11/20, moves: 0, ncost: 171.7512691958265\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/20, moves: 811, ncost: 187.77223580031304\n",
      "Run: 3, iteration: 2/20, moves: 177, ncost: 186.39335252116692\n",
      "Run: 3, iteration: 3/20, moves: 75, ncost: 186.06667349131644\n",
      "Run: 3, iteration: 4/20, moves: 43, ncost: 185.918707821339\n",
      "Run: 3, iteration: 5/20, moves: 11, ncost: 185.9122320039196\n",
      "Run: 3, iteration: 6/20, moves: 0, ncost: 185.9122320039196\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/20, moves: 872, ncost: 181.67367947384915\n",
      "Run: 4, iteration: 2/20, moves: 221, ncost: 178.54026163558638\n",
      "Run: 4, iteration: 3/20, moves: 102, ncost: 177.75730525066933\n",
      "Run: 4, iteration: 4/20, moves: 51, ncost: 177.61662843115255\n",
      "Run: 4, iteration: 5/20, moves: 11, ncost: 177.60973958583054\n",
      "Run: 4, iteration: 6/20, moves: 1, ncost: 177.60957376776025\n",
      "Run: 4, iteration: 7/20, moves: 0, ncost: 177.60957376776025\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/20, moves: 744, ncost: 171.65300413814037\n",
      "Run: 5, iteration: 2/20, moves: 226, ncost: 166.1077299221962\n",
      "Run: 5, iteration: 3/20, moves: 95, ncost: 162.58214612626955\n",
      "Run: 5, iteration: 4/20, moves: 34, ncost: 161.19279398629158\n",
      "Run: 5, iteration: 5/20, moves: 21, ncost: 161.05997998727128\n",
      "Run: 5, iteration: 6/20, moves: 3, ncost: 161.05803649209074\n",
      "Run: 5, iteration: 7/20, moves: 0, ncost: 161.05803649209074\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/20, moves: 594, ncost: 194.7977338716362\n",
      "Run: 6, iteration: 2/20, moves: 215, ncost: 187.59578796354756\n",
      "Run: 6, iteration: 3/20, moves: 31, ncost: 187.5753639838463\n",
      "Run: 6, iteration: 4/20, moves: 8, ncost: 187.5656527615227\n",
      "Run: 6, iteration: 5/20, moves: 1, ncost: 187.56530586125672\n",
      "Run: 6, iteration: 6/20, moves: 0, ncost: 187.56530586125672\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/20, moves: 747, ncost: 175.38010053539062\n",
      "Run: 7, iteration: 2/20, moves: 158, ncost: 174.70785794070318\n",
      "Run: 7, iteration: 3/20, moves: 36, ncost: 174.60337141364312\n",
      "Run: 7, iteration: 4/20, moves: 17, ncost: 174.5891992929143\n",
      "Run: 7, iteration: 5/20, moves: 6, ncost: 174.58292221213708\n",
      "Run: 7, iteration: 6/20, moves: 4, ncost: 174.5809163604445\n",
      "Run: 7, iteration: 7/20, moves: 4, ncost: 174.57780263985873\n",
      "Run: 7, iteration: 8/20, moves: 1, ncost: 174.57734577565952\n",
      "Run: 7, iteration: 9/20, moves: 0, ncost: 174.57734577565952\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/20, moves: 612, ncost: 169.31928271089794\n",
      "Run: 8, iteration: 2/20, moves: 212, ncost: 168.0945480966685\n",
      "Run: 8, iteration: 3/20, moves: 57, ncost: 167.96561786596513\n",
      "Run: 8, iteration: 4/20, moves: 50, ncost: 167.61466190355594\n",
      "Run: 8, iteration: 5/20, moves: 81, ncost: 167.04257810113356\n",
      "Run: 8, iteration: 6/20, moves: 15, ncost: 167.02485866216176\n",
      "Run: 8, iteration: 7/20, moves: 2, ncost: 167.02406035570826\n",
      "Run: 8, iteration: 8/20, moves: 0, ncost: 167.02406035570826\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/20, moves: 868, ncost: 176.84410075792456\n",
      "Run: 9, iteration: 2/20, moves: 196, ncost: 171.1473961017008\n",
      "Run: 9, iteration: 3/20, moves: 52, ncost: 171.06373373969032\n",
      "Run: 9, iteration: 4/20, moves: 9, ncost: 171.06187085277443\n",
      "Run: 9, iteration: 5/20, moves: 3, ncost: 171.0612519806422\n",
      "Run: 9, iteration: 6/20, moves: 0, ncost: 171.0612519806422\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/20, moves: 760, ncost: 187.63503183058978\n",
      "Run: 10, iteration: 2/20, moves: 286, ncost: 174.85393270851418\n",
      "Run: 10, iteration: 3/20, moves: 171, ncost: 174.11146243899532\n",
      "Run: 10, iteration: 4/20, moves: 39, ncost: 173.99668120452284\n",
      "Run: 10, iteration: 5/20, moves: 16, ncost: 173.93434528108747\n",
      "Run: 10, iteration: 6/20, moves: 22, ncost: 173.87325454361397\n",
      "Run: 10, iteration: 7/20, moves: 12, ncost: 173.86359248421545\n",
      "Run: 10, iteration: 8/20, moves: 4, ncost: 173.861852153267\n",
      "Run: 10, iteration: 9/20, moves: 0, ncost: 173.861852153267\n",
      "Best run was number 1\n",
      "[[0.52827586 0.39550003 0.         0.         1.         1.\n",
      "  2.        ]\n",
      " [0.20922647 0.18630272 1.         1.         1.         0.\n",
      "  0.        ]\n",
      " [0.1939178  0.28943809 1.         1.         1.         1.\n",
      "  0.        ]\n",
      " [0.25907441 0.4400196  0.         1.         1.         2.\n",
      "  2.        ]\n",
      " [0.14544213 0.16265482 0.         0.         0.         0.\n",
      "  0.        ]\n",
      " [0.17155172 0.36284656 0.         0.         0.         1.\n",
      "  2.        ]\n",
      " [0.15419365 0.31258227 1.         1.         1.         1.\n",
      "  1.        ]\n",
      " [0.43191252 0.22005063 0.         0.         1.         0.\n",
      "  0.        ]\n",
      " [0.39271082 0.37330251 0.         0.         1.         1.\n",
      "  1.        ]\n",
      " [0.66146842 0.42952742 1.         1.         2.         1.\n",
      "  1.        ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build our model\n",
    "kproto = KPrototypes(n_clusters=10, verbose=2, max_iter=20)\n",
    "clusters = kproto.fit_predict(mark_array, categorical = [0, 1, 3, 5, 6])\n",
    "print(kproto.cluster_centroids_)\n",
    "len(kproto.cluster_centroids_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c6a38e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_dict = []\n",
    "for c in clusters:\n",
    "    cluster_dict.append(c)\n",
    "\n",
    "df['Cluster'] = cluster_dict\n",
    "\n",
    "#Put original coumns from temp to df:\n",
    "df[['ID', 'Age', 'Income']] =  df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed6a1aec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Settlement Size</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>ID</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>100000073</td>\n",
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       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>157299</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>100000076</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sex  Marital Status  Age  Education  Income  Occupation  Settlement Size  \\\n",
       "0     0               0   67          2  124670           1                2   \n",
       "16    0               0   53          1  125550           1                0   \n",
       "23    0               0   44          1  134918           1                2   \n",
       "29    0               0   63          2  175882           1                0   \n",
       "56    0               0   52          1  202575           2                2   \n",
       "68    0               0   37          1  106978           1                2   \n",
       "72    0               0   51          1  141858           2                2   \n",
       "75    0               0   66          2  157299           2                2   \n",
       "77    0               0   51          1  155486           1                2   \n",
       "94    0               0   54          1  165230           1                2   \n",
       "\n",
       "    Cluster         ID  \n",
       "0         0  100000001  \n",
       "16        0  100000017  \n",
       "23        0  100000024  \n",
       "29        0  100000030  \n",
       "56        0  100000057  \n",
       "68        0  100000069  \n",
       "72        0  100000073  \n",
       "75        0  100000076  \n",
       "77        0  100000078  \n",
       "94        0  100000095  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Cluster']== 0].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "317a5702",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df[df.Cluster == 0]\n",
    "df2 = df[df.Cluster == 1]\n",
    "df3 = df[df.Cluster == 2]\n",
    "df4 = df[df.Cluster == 3]\n",
    "df5 = df[df.Cluster == 4]\n",
    "df6 = df[df.Cluster == 5]\n",
    "df7 = df[df.Cluster == 6]\n",
    "df8 = df[df.Cluster == 7]\n",
    "df9 = df[df.Cluster == 8]\n",
    "df10 = df[df.Cluster == 9]\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Income')\n",
    "\n",
    "plt.scatter(df1.Age, df1['Income'], color = 'green', alpha = 0.4)\n",
    "plt.scatter(df2.Age, df2['Income'], color = 'red', alpha = 0.4)\n",
    "plt.scatter(df3.Age, df3['Income'], color = 'gray', alpha = 0.4)\n",
    "plt.scatter(df4.Age, df4['Income'], color = 'orange', alpha = 0.4)\n",
    "plt.scatter(df5.Age, df5['Income'], color = 'yellow', alpha = 0.4)\n",
    "plt.scatter(df6.Age, df6['Income'], color = 'cyan', alpha = 0.4)\n",
    "plt.scatter(df7.Age, df7['Income'], color = 'magenta', alpha = 0.4)\n",
    "plt.scatter(df8.Age, df8['Income'], color = 'black', alpha = 0.4)\n",
    "plt.scatter(df9.Age, df9['Income'], color = 'purple', alpha = 0.4)\n",
    "plt.scatter(df10.Age, df10['Income'], color = 'blue', alpha = 0.4)\n",
    "\n",
    "plt.legend()\n",
    "plt.show"
   ]
  }
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