case_study_fast_food.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1295f73d",
   "metadata": {},
   "source": [
    "# Case Study: Fast Food\n",
    "\n",
    "For this case study, the objective is to conduct market segmentation analysis using a dataset originally collected to assess the validity of various survey answer formats in brand image research. The data is described in prior studies by Dolnicar and others. In this scenario, we will assume the role of McDonald's and aim to identify distinct consumer segments with varying perceptions of the brand. This analysis will help us determine which market segments to prioritize and what messages to convey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "179e9ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4089a1b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import adjusted_rand_score\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from statsmodels.graphics.mosaicplot import mosaic\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249f8e04",
   "metadata": {},
   "source": [
    "\n",
    "# Step 2: Specifying the Ideal Target Segment\n",
    "\n",
    "## Knock_out criteria\n",
    "* Homogenous\n",
    "* Distinct\n",
    "* Matching the strength's of McDonald's\n",
    "* Identifiable\n",
    "* Reacheable\n",
    "\n",
    "## Attractivenss criteria\n",
    "* Positive perception of McDonald's\n",
    "* Frequently eats out\n",
    "* Likes fast food\n",
    "* Wish to learn more about market segments which are currently not fond of McDonald's "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa3b46cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/mcdonalds.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bacec945",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yummy</th>\n",
       "      <th>convenient</th>\n",
       "      <th>spicy</th>\n",
       "      <th>fattening</th>\n",
       "      <th>greasy</th>\n",
       "      <th>fast</th>\n",
       "      <th>cheap</th>\n",
       "      <th>tasty</th>\n",
       "      <th>expensive</th>\n",
       "      <th>healthy</th>\n",
       "      <th>disgusting</th>\n",
       "      <th>Like</th>\n",
       "      <th>Age</th>\n",
       "      <th>VisitFrequency</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>-3</td>\n",
       "      <td>61</td>\n",
       "      <td>Every three months</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>+2</td>\n",
       "      <td>51</td>\n",
       "      <td>Every three months</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>+1</td>\n",
       "      <td>62</td>\n",
       "      <td>Every three months</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>+4</td>\n",
       "      <td>69</td>\n",
       "      <td>Once a week</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>+2</td>\n",
       "      <td>49</td>\n",
       "      <td>Once a month</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  yummy convenient spicy fattening greasy fast cheap tasty expensive healthy  \\\n",
       "0    No        Yes    No       Yes     No  Yes   Yes    No       Yes      No   \n",
       "1   Yes        Yes    No       Yes    Yes  Yes   Yes   Yes       Yes      No   \n",
       "2    No        Yes   Yes       Yes    Yes  Yes    No   Yes       Yes     Yes   \n",
       "3   Yes        Yes    No       Yes    Yes  Yes   Yes   Yes        No      No   \n",
       "4    No        Yes    No       Yes    Yes  Yes   Yes    No        No     Yes   \n",
       "\n",
       "  disgusting Like  Age      VisitFrequency  Gender  \n",
       "0         No   -3   61  Every three months  Female  \n",
       "1         No   +2   51  Every three months  Female  \n",
       "2         No   +1   62  Every three months  Female  \n",
       "3        Yes   +4   69         Once a week  Female  \n",
       "4         No   +2   49        Once a month    Male  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01d7f3fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1453 entries, 0 to 1452\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   yummy           1453 non-null   object\n",
      " 1   convenient      1453 non-null   object\n",
      " 2   spicy           1453 non-null   object\n",
      " 3   fattening       1453 non-null   object\n",
      " 4   greasy          1453 non-null   object\n",
      " 5   fast            1453 non-null   object\n",
      " 6   cheap           1453 non-null   object\n",
      " 7   tasty           1453 non-null   object\n",
      " 8   expensive       1453 non-null   object\n",
      " 9   healthy         1453 non-null   object\n",
      " 10  disgusting      1453 non-null   object\n",
      " 11  Like            1453 non-null   object\n",
      " 12  Age             1453 non-null   int64 \n",
      " 13  VisitFrequency  1453 non-null   object\n",
      " 14  Gender          1453 non-null   object\n",
      "dtypes: int64(1), object(14)\n",
      "memory usage: 170.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8426c7f8",
   "metadata": {},
   "source": [
    "# Step 4: Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a6102fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lencoder(col):\n",
    "    le = LabelEncoder()\n",
    "    data[col] = le.fit_transform(data[col])\n",
    "    return data[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b2de84e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in data.columns[:11]:\n",
    "    data[col] = lencoder(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f469ab9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yummy         0.552650\n",
       "convenient    0.907777\n",
       "spicy         0.093599\n",
       "fattening     0.867171\n",
       "greasy        0.526497\n",
       "fast          0.900206\n",
       "cheap         0.598761\n",
       "tasty         0.644184\n",
       "expensive     0.357880\n",
       "healthy       0.198899\n",
       "disgusting    0.242946\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.columns[:11]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744a3de7",
   "metadata": {},
   "source": [
    "* The average values of the transformed binary numeric segmentation variables indicate that about half of the respondents (55%) perceive McDonald's as YUMMY, 91% believe that eating at McDonald's is CONVENIENT, but only 9% think that McDonald's food is SPICY "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e30da3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Principal Component Analysis\n",
    "pca = PCA(random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "28e020ab",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "PCA(random_state=42)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(data[data.columns[:11]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4911a43e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pca = pca.transform(data[data.columns[:11]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb47bdfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.29944723, 0.19279721, 0.13304535, 0.08309578, 0.05948052,\n",
       "       0.05029956, 0.0438491 , 0.03954779, 0.0367609 , 0.03235329,\n",
       "       0.02932326])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8496ac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.29944723, 0.49224445, 0.6252898 , 0.70838558, 0.7678661 ,\n",
       "       0.81816566, 0.86201476, 0.90156255, 0.93832345, 0.97067674,\n",
       "       1.        ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8f8312d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca = pd.DataFrame(data_pca, columns = [f'PC{x}' for x in range(1, 12)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ad3afe1",
   "metadata": {},
   "outputs": [
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       "      <td>-0.094724</td>\n",
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       "      <td>-0.054284</td>\n",
       "      <td>-0.231477</td>\n",
       "      <td>-0.028003</td>\n",
       "      <td>-0.250678</td>\n",
       "      <td>-0.051034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.187057</td>\n",
       "      <td>-0.807610</td>\n",
       "      <td>0.028537</td>\n",
       "      <td>0.548332</td>\n",
       "      <td>0.854074</td>\n",
       "      <td>-0.097305</td>\n",
       "      <td>-0.457043</td>\n",
       "      <td>0.171758</td>\n",
       "      <td>-0.074409</td>\n",
       "      <td>0.031897</td>\n",
       "      <td>0.082245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
       "0  0.425367 -0.219079  0.663255 -0.401300  0.201705 -0.389767 -0.211982   \n",
       "1 -0.218638  0.388190 -0.730827 -0.094724  0.044669 -0.086596 -0.095877   \n",
       "2  0.375415  0.730435 -0.122040  0.692262  0.839643 -0.687406  0.583112   \n",
       "3 -0.172926 -0.352752 -0.843795  0.206998 -0.681415 -0.036133 -0.054284   \n",
       "4  0.187057 -0.807610  0.028537  0.548332  0.854074 -0.097305 -0.457043   \n",
       "\n",
       "        PC8       PC9      PC10      PC11  \n",
       "0  0.163235  0.181007  0.515706 -0.567074  \n",
       "1 -0.034756  0.111476  0.493313 -0.500440  \n",
       "2  0.364379 -0.322288  0.061759  0.242741  \n",
       "3 -0.231477 -0.028003 -0.250678 -0.051034  \n",
       "4  0.171758 -0.074409  0.031897  0.082245  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4fc7d163",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_summary = pd.DataFrame({'Standard Deviation':df_pca.std(),\n",
    "                           'Proportion of Variance': pca.explained_variance_ratio_,\n",
    "                           'Cumultive Proportion': pca.explained_variance_ratio_.cumsum()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cda03cb6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Proportion of Variance</th>\n",
       "      <th>Cumultive Proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>0.757050</td>\n",
       "      <td>0.299447</td>\n",
       "      <td>0.299447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>0.607456</td>\n",
       "      <td>0.192797</td>\n",
       "      <td>0.492244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>0.504619</td>\n",
       "      <td>0.133045</td>\n",
       "      <td>0.625290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>0.398799</td>\n",
       "      <td>0.083096</td>\n",
       "      <td>0.708386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC5</th>\n",
       "      <td>0.337405</td>\n",
       "      <td>0.059481</td>\n",
       "      <td>0.767866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC6</th>\n",
       "      <td>0.310275</td>\n",
       "      <td>0.050300</td>\n",
       "      <td>0.818166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC7</th>\n",
       "      <td>0.289697</td>\n",
       "      <td>0.043849</td>\n",
       "      <td>0.862015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC8</th>\n",
       "      <td>0.275122</td>\n",
       "      <td>0.039548</td>\n",
       "      <td>0.901563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC9</th>\n",
       "      <td>0.265251</td>\n",
       "      <td>0.036761</td>\n",
       "      <td>0.938323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC10</th>\n",
       "      <td>0.248842</td>\n",
       "      <td>0.032353</td>\n",
       "      <td>0.970677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC11</th>\n",
       "      <td>0.236903</td>\n",
       "      <td>0.029323</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      Standard Deviation  Proportion of Variance  Cumultive Proportion\n",
       "PC1             0.757050                0.299447              0.299447\n",
       "PC2             0.607456                0.192797              0.492244\n",
       "PC3             0.504619                0.133045              0.625290\n",
       "PC4             0.398799                0.083096              0.708386\n",
       "PC5             0.337405                0.059481              0.767866\n",
       "PC6             0.310275                0.050300              0.818166\n",
       "PC7             0.289697                0.043849              0.862015\n",
       "PC8             0.275122                0.039548              0.901563\n",
       "PC9             0.265251                0.036761              0.938323\n",
       "PC10            0.248842                0.032353              0.970677\n",
       "PC11            0.236903                0.029323              1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56ca54a5",
   "metadata": {},
   "source": [
    " From Principal Components Analysis indicate that the first two components capture about 50% of the information contained in the segmentation variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1fec7fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Factor Loadings\n",
    "factor_loadings = pd.DataFrame(pca.components_, columns = data.columns[:11], index = df_pca.columns).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63e68abf",
   "metadata": {},
   "outputs": [
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       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5a7ff_row10_col3 {\n",
       "  background-color: #3c8cc3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_5a7ff_row10_col5 {\n",
       "  background-color: #74b3d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5a7ff_row10_col6 {\n",
       "  background-color: #add0e6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5a7ff_row10_col8 {\n",
       "  background-color: #79b5d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_5a7ff_row10_col9 {\n",
       "  background-color: #abd0e6;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5a7ff\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5a7ff_level0_col0\" class=\"col_heading level0 col0\" >PC1</th>\n",
       "      <th id=\"T_5a7ff_level0_col1\" class=\"col_heading level0 col1\" >PC2</th>\n",
       "      <th id=\"T_5a7ff_level0_col2\" class=\"col_heading level0 col2\" >PC3</th>\n",
       "      <th id=\"T_5a7ff_level0_col3\" class=\"col_heading level0 col3\" >PC4</th>\n",
       "      <th id=\"T_5a7ff_level0_col4\" class=\"col_heading level0 col4\" >PC5</th>\n",
       "      <th id=\"T_5a7ff_level0_col5\" class=\"col_heading level0 col5\" >PC6</th>\n",
       "      <th id=\"T_5a7ff_level0_col6\" class=\"col_heading level0 col6\" >PC7</th>\n",
       "      <th id=\"T_5a7ff_level0_col7\" class=\"col_heading level0 col7\" >PC8</th>\n",
       "      <th id=\"T_5a7ff_level0_col8\" class=\"col_heading level0 col8\" >PC9</th>\n",
       "      <th id=\"T_5a7ff_level0_col9\" class=\"col_heading level0 col9\" >PC10</th>\n",
       "      <th id=\"T_5a7ff_level0_col10\" class=\"col_heading level0 col10\" >PC11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row0\" class=\"row_heading level0 row0\" >yummy</th>\n",
       "      <td id=\"T_5a7ff_row0_col0\" class=\"data row0 col0\" >-0.476933</td>\n",
       "      <td id=\"T_5a7ff_row0_col1\" class=\"data row0 col1\" >0.363790</td>\n",
       "      <td id=\"T_5a7ff_row0_col2\" class=\"data row0 col2\" >-0.304444</td>\n",
       "      <td id=\"T_5a7ff_row0_col3\" class=\"data row0 col3\" >0.055162</td>\n",
       "      <td id=\"T_5a7ff_row0_col4\" class=\"data row0 col4\" >-0.307535</td>\n",
       "      <td id=\"T_5a7ff_row0_col5\" class=\"data row0 col5\" >0.170738</td>\n",
       "      <td id=\"T_5a7ff_row0_col6\" class=\"data row0 col6\" >-0.280519</td>\n",
       "      <td id=\"T_5a7ff_row0_col7\" class=\"data row0 col7\" >0.013041</td>\n",
       "      <td id=\"T_5a7ff_row0_col8\" class=\"data row0 col8\" >0.572403</td>\n",
       "      <td id=\"T_5a7ff_row0_col9\" class=\"data row0 col9\" >-0.110284</td>\n",
       "      <td id=\"T_5a7ff_row0_col10\" class=\"data row0 col10\" >0.045439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row1\" class=\"row_heading level0 row1\" >convenient</th>\n",
       "      <td id=\"T_5a7ff_row1_col0\" class=\"data row1 col0\" >-0.155332</td>\n",
       "      <td id=\"T_5a7ff_row1_col1\" class=\"data row1 col1\" >0.016414</td>\n",
       "      <td id=\"T_5a7ff_row1_col2\" class=\"data row1 col2\" >-0.062515</td>\n",
       "      <td id=\"T_5a7ff_row1_col3\" class=\"data row1 col3\" >-0.142425</td>\n",
       "      <td id=\"T_5a7ff_row1_col4\" class=\"data row1 col4\" >0.277608</td>\n",
       "      <td id=\"T_5a7ff_row1_col5\" class=\"data row1 col5\" >-0.347830</td>\n",
       "      <td id=\"T_5a7ff_row1_col6\" class=\"data row1 col6\" >-0.059738</td>\n",
       "      <td id=\"T_5a7ff_row1_col7\" class=\"data row1 col7\" >-0.113079</td>\n",
       "      <td id=\"T_5a7ff_row1_col8\" class=\"data row1 col8\" >-0.018465</td>\n",
       "      <td id=\"T_5a7ff_row1_col9\" class=\"data row1 col9\" >-0.665818</td>\n",
       "      <td id=\"T_5a7ff_row1_col10\" class=\"data row1 col10\" >-0.541616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row2\" class=\"row_heading level0 row2\" >spicy</th>\n",
       "      <td id=\"T_5a7ff_row2_col0\" class=\"data row2 col0\" >-0.006356</td>\n",
       "      <td id=\"T_5a7ff_row2_col1\" class=\"data row2 col1\" >0.018809</td>\n",
       "      <td id=\"T_5a7ff_row2_col2\" class=\"data row2 col2\" >-0.037019</td>\n",
       "      <td id=\"T_5a7ff_row2_col3\" class=\"data row2 col3\" >0.197619</td>\n",
       "      <td id=\"T_5a7ff_row2_col4\" class=\"data row2 col4\" >0.070620</td>\n",
       "      <td id=\"T_5a7ff_row2_col5\" class=\"data row2 col5\" >-0.355087</td>\n",
       "      <td id=\"T_5a7ff_row2_col6\" class=\"data row2 col6\" >0.707637</td>\n",
       "      <td id=\"T_5a7ff_row2_col7\" class=\"data row2 col7\" >0.375934</td>\n",
       "      <td id=\"T_5a7ff_row2_col8\" class=\"data row2 col8\" >0.400280</td>\n",
       "      <td id=\"T_5a7ff_row2_col9\" class=\"data row2 col9\" >-0.075634</td>\n",
       "      <td id=\"T_5a7ff_row2_col10\" class=\"data row2 col10\" >0.141730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row3\" class=\"row_heading level0 row3\" >fattening</th>\n",
       "      <td id=\"T_5a7ff_row3_col0\" class=\"data row3 col0\" >0.116232</td>\n",
       "      <td id=\"T_5a7ff_row3_col1\" class=\"data row3 col1\" >-0.034094</td>\n",
       "      <td id=\"T_5a7ff_row3_col2\" class=\"data row3 col2\" >-0.322359</td>\n",
       "      <td id=\"T_5a7ff_row3_col3\" class=\"data row3 col3\" >-0.354139</td>\n",
       "      <td id=\"T_5a7ff_row3_col4\" class=\"data row3 col4\" >-0.073405</td>\n",
       "      <td id=\"T_5a7ff_row3_col5\" class=\"data row3 col5\" >-0.406515</td>\n",
       "      <td id=\"T_5a7ff_row3_col6\" class=\"data row3 col6\" >-0.385943</td>\n",
       "      <td id=\"T_5a7ff_row3_col7\" class=\"data row3 col7\" >0.589622</td>\n",
       "      <td id=\"T_5a7ff_row3_col8\" class=\"data row3 col8\" >-0.160512</td>\n",
       "      <td id=\"T_5a7ff_row3_col9\" class=\"data row3 col9\" >-0.005338</td>\n",
       "      <td id=\"T_5a7ff_row3_col10\" class=\"data row3 col10\" >0.250910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row4\" class=\"row_heading level0 row4\" >greasy</th>\n",
       "      <td id=\"T_5a7ff_row4_col0\" class=\"data row4 col0\" >0.304443</td>\n",
       "      <td id=\"T_5a7ff_row4_col1\" class=\"data row4 col1\" >-0.063839</td>\n",
       "      <td id=\"T_5a7ff_row4_col2\" class=\"data row4 col2\" >-0.802373</td>\n",
       "      <td id=\"T_5a7ff_row4_col3\" class=\"data row4 col3\" >0.253960</td>\n",
       "      <td id=\"T_5a7ff_row4_col4\" class=\"data row4 col4\" >0.361399</td>\n",
       "      <td id=\"T_5a7ff_row4_col5\" class=\"data row4 col5\" >0.209347</td>\n",
       "      <td id=\"T_5a7ff_row4_col6\" class=\"data row4 col6\" >0.036170</td>\n",
       "      <td id=\"T_5a7ff_row4_col7\" class=\"data row4 col7\" >-0.138241</td>\n",
       "      <td id=\"T_5a7ff_row4_col8\" class=\"data row4 col8\" >-0.002847</td>\n",
       "      <td id=\"T_5a7ff_row4_col9\" class=\"data row4 col9\" >0.008707</td>\n",
       "      <td id=\"T_5a7ff_row4_col10\" class=\"data row4 col10\" >0.001642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row5\" class=\"row_heading level0 row5\" >fast</th>\n",
       "      <td id=\"T_5a7ff_row5_col0\" class=\"data row5 col0\" >-0.108493</td>\n",
       "      <td id=\"T_5a7ff_row5_col1\" class=\"data row5 col1\" >-0.086972</td>\n",
       "      <td id=\"T_5a7ff_row5_col2\" class=\"data row5 col2\" >-0.064642</td>\n",
       "      <td id=\"T_5a7ff_row5_col3\" class=\"data row5 col3\" >-0.097363</td>\n",
       "      <td id=\"T_5a7ff_row5_col4\" class=\"data row5 col4\" >0.107930</td>\n",
       "      <td id=\"T_5a7ff_row5_col5\" class=\"data row5 col5\" >-0.594632</td>\n",
       "      <td id=\"T_5a7ff_row5_col6\" class=\"data row5 col6\" >-0.086846</td>\n",
       "      <td id=\"T_5a7ff_row5_col7\" class=\"data row5 col7\" >-0.627799</td>\n",
       "      <td id=\"T_5a7ff_row5_col8\" class=\"data row5 col8\" >0.166197</td>\n",
       "      <td id=\"T_5a7ff_row5_col9\" class=\"data row5 col9\" >0.239532</td>\n",
       "      <td id=\"T_5a7ff_row5_col10\" class=\"data row5 col10\" >0.339265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row6\" class=\"row_heading level0 row6\" >cheap</th>\n",
       "      <td id=\"T_5a7ff_row6_col0\" class=\"data row6 col0\" >-0.337186</td>\n",
       "      <td id=\"T_5a7ff_row6_col1\" class=\"data row6 col1\" >-0.610633</td>\n",
       "      <td id=\"T_5a7ff_row6_col2\" class=\"data row6 col2\" >-0.149310</td>\n",
       "      <td id=\"T_5a7ff_row6_col3\" class=\"data row6 col3\" >0.118958</td>\n",
       "      <td id=\"T_5a7ff_row6_col4\" class=\"data row6 col4\" >-0.128973</td>\n",
       "      <td id=\"T_5a7ff_row6_col5\" class=\"data row6 col5\" >-0.103241</td>\n",
       "      <td id=\"T_5a7ff_row6_col6\" class=\"data row6 col6\" >-0.040449</td>\n",
       "      <td id=\"T_5a7ff_row6_col7\" class=\"data row6 col7\" >0.140060</td>\n",
       "      <td id=\"T_5a7ff_row6_col8\" class=\"data row6 col8\" >0.076069</td>\n",
       "      <td id=\"T_5a7ff_row6_col9\" class=\"data row6 col9\" >0.428087</td>\n",
       "      <td id=\"T_5a7ff_row6_col10\" class=\"data row6 col10\" >-0.489283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row7\" class=\"row_heading level0 row7\" >tasty</th>\n",
       "      <td id=\"T_5a7ff_row7_col0\" class=\"data row7 col0\" >-0.471514</td>\n",
       "      <td id=\"T_5a7ff_row7_col1\" class=\"data row7 col1\" >0.307318</td>\n",
       "      <td id=\"T_5a7ff_row7_col2\" class=\"data row7 col2\" >-0.287265</td>\n",
       "      <td id=\"T_5a7ff_row7_col3\" class=\"data row7 col3\" >-0.002547</td>\n",
       "      <td id=\"T_5a7ff_row7_col4\" class=\"data row7 col4\" >-0.210899</td>\n",
       "      <td id=\"T_5a7ff_row7_col5\" class=\"data row7 col5\" >-0.076914</td>\n",
       "      <td id=\"T_5a7ff_row7_col6\" class=\"data row7 col6\" >0.360453</td>\n",
       "      <td id=\"T_5a7ff_row7_col7\" class=\"data row7 col7\" >-0.072792</td>\n",
       "      <td id=\"T_5a7ff_row7_col8\" class=\"data row7 col8\" >-0.639086</td>\n",
       "      <td id=\"T_5a7ff_row7_col9\" class=\"data row7 col9\" >0.079184</td>\n",
       "      <td id=\"T_5a7ff_row7_col10\" class=\"data row7 col10\" >0.019552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row8\" class=\"row_heading level0 row8\" >expensive</th>\n",
       "      <td id=\"T_5a7ff_row8_col0\" class=\"data row8 col0\" >0.329042</td>\n",
       "      <td id=\"T_5a7ff_row8_col1\" class=\"data row8 col1\" >0.601286</td>\n",
       "      <td id=\"T_5a7ff_row8_col2\" class=\"data row8 col2\" >0.024397</td>\n",
       "      <td id=\"T_5a7ff_row8_col3\" class=\"data row8 col3\" >0.067816</td>\n",
       "      <td id=\"T_5a7ff_row8_col4\" class=\"data row8 col4\" >-0.003125</td>\n",
       "      <td id=\"T_5a7ff_row8_col5\" class=\"data row8 col5\" >-0.261342</td>\n",
       "      <td id=\"T_5a7ff_row8_col6\" class=\"data row8 col6\" >-0.068385</td>\n",
       "      <td id=\"T_5a7ff_row8_col7\" class=\"data row8 col7\" >0.029539</td>\n",
       "      <td id=\"T_5a7ff_row8_col8\" class=\"data row8 col8\" >0.066996</td>\n",
       "      <td id=\"T_5a7ff_row8_col9\" class=\"data row8 col9\" >0.454399</td>\n",
       "      <td id=\"T_5a7ff_row8_col10\" class=\"data row8 col10\" >-0.490069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row9\" class=\"row_heading level0 row9\" >healthy</th>\n",
       "      <td id=\"T_5a7ff_row9_col0\" class=\"data row9 col0\" >-0.213711</td>\n",
       "      <td id=\"T_5a7ff_row9_col1\" class=\"data row9 col1\" >0.076593</td>\n",
       "      <td id=\"T_5a7ff_row9_col2\" class=\"data row9 col2\" >0.192051</td>\n",
       "      <td id=\"T_5a7ff_row9_col3\" class=\"data row9 col3\" >0.763488</td>\n",
       "      <td id=\"T_5a7ff_row9_col4\" class=\"data row9 col4\" >0.287846</td>\n",
       "      <td id=\"T_5a7ff_row9_col5\" class=\"data row9 col5\" >-0.178226</td>\n",
       "      <td id=\"T_5a7ff_row9_col6\" class=\"data row9 col6\" >-0.349616</td>\n",
       "      <td id=\"T_5a7ff_row9_col7\" class=\"data row9 col7\" >0.176303</td>\n",
       "      <td id=\"T_5a7ff_row9_col8\" class=\"data row9 col8\" >-0.185572</td>\n",
       "      <td id=\"T_5a7ff_row9_col9\" class=\"data row9 col9\" >-0.038117</td>\n",
       "      <td id=\"T_5a7ff_row9_col10\" class=\"data row9 col10\" >0.157608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5a7ff_level0_row10\" class=\"row_heading level0 row10\" >disgusting</th>\n",
       "      <td id=\"T_5a7ff_row10_col0\" class=\"data row10 col0\" >0.374753</td>\n",
       "      <td id=\"T_5a7ff_row10_col1\" class=\"data row10 col1\" >-0.139656</td>\n",
       "      <td id=\"T_5a7ff_row10_col2\" class=\"data row10 col2\" >-0.088571</td>\n",
       "      <td id=\"T_5a7ff_row10_col3\" class=\"data row10 col3\" >0.369539</td>\n",
       "      <td id=\"T_5a7ff_row10_col4\" class=\"data row10 col4\" >-0.729209</td>\n",
       "      <td id=\"T_5a7ff_row10_col5\" class=\"data row10 col5\" >-0.210878</td>\n",
       "      <td id=\"T_5a7ff_row10_col6\" class=\"data row10 col6\" >-0.026792</td>\n",
       "      <td id=\"T_5a7ff_row10_col7\" class=\"data row10 col7\" >-0.167181</td>\n",
       "      <td id=\"T_5a7ff_row10_col8\" class=\"data row10 col8\" >-0.072483</td>\n",
       "      <td id=\"T_5a7ff_row10_col9\" class=\"data row10 col9\" >-0.289592</td>\n",
       "      <td id=\"T_5a7ff_row10_col10\" class=\"data row10 col10\" >-0.040662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22346f3d3d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor_loadings.style.background_gradient(cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d2ff7d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate centroid\n",
    "index_names = factor_loadings.index\n",
    "\n",
    "# Plot data points\n",
    "sns.scatterplot(df_pca, x = 'PC1', y = 'PC2', color = 'grey')\n",
    "\n",
    "# Plot arrows from centroid to provided coordinates with index names\n",
    "for i in range(len(factor_loadings['PC1'])):\n",
    "    plt.arrow(0, 0, factor_loadings['PC1'][i] * 1.5, factor_loadings['PC2'][i] * 1.5, head_width=0.05, head_length=0.1, fc='red', ec='red')\n",
    "    plt.text(factor_loadings['PC1'][i] * 1.5, factor_loadings['PC2'][i]* 1.5, index_names[i], fontsize=12, ha='right', color = 'red')\n",
    "\n",
    "# Set labels and legend\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.title('A Principal components analysis of the fast food data set')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6eff48d",
   "metadata": {},
   "source": [
    "# Step 5: Extracting Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4f390aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_km28 = [KMeans(n_clusters = k, random_state = 42).fit(data[data.columns[:11]]) for k in range(2, 9)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bb752703",
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = [model.inertia_ for model in data_km28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fa9ba85b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (6,4))\n",
    "sns.barplot(x = list(range(2, 9)), y = wcss, color = 'grey')\n",
    "sns.lineplot(x = list(range(0, 7)), y = wcss, color = 'red', marker = 'o')\n",
    "plt.arrow(3, 2000, -1, -400)\n",
    "plt.text(3, 2000, 'Elbow')\n",
    "plt.xlabel(\"Number of Segments\")\n",
    "plt.ylabel('Sum of Within Cluster Distances')\n",
    "plt.title(\"Scree plot for the fast food data set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a90bbe74",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = data_km28[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e52fb211",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clust = data.copy()\n",
    "data_clust['cluster'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94cc01ec",
   "metadata": {},
   "outputs": [
    {
     "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>yummy</th>\n",
       "      <th>convenient</th>\n",
       "      <th>spicy</th>\n",
       "      <th>fattening</th>\n",
       "      <th>greasy</th>\n",
       "      <th>fast</th>\n",
       "      <th>cheap</th>\n",
       "      <th>tasty</th>\n",
       "      <th>expensive</th>\n",
       "      <th>healthy</th>\n",
       "      <th>disgusting</th>\n",
       "      <th>Like</th>\n",
       "      <th>Age</th>\n",
       "      <th>VisitFrequency</th>\n",
       "      <th>Gender</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "      <td>61</td>\n",
       "      <td>Every three months</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>+2</td>\n",
       "      <td>51</td>\n",
       "      <td>Every three months</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>+1</td>\n",
       "      <td>62</td>\n",
       "      <td>Every three months</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>+4</td>\n",
       "      <td>69</td>\n",
       "      <td>Once a week</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>+2</td>\n",
       "      <td>49</td>\n",
       "      <td>Once a month</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yummy  convenient  spicy  fattening  greasy  fast  cheap  tasty  expensive  \\\n",
       "0      0           1      0          1       0     1      1      0          1   \n",
       "1      1           1      0          1       1     1      1      1          1   \n",
       "2      0           1      1          1       1     1      0      1          1   \n",
       "3      1           1      0          1       1     1      1      1          0   \n",
       "4      0           1      0          1       1     1      1      0          0   \n",
       "\n",
       "   healthy  disgusting Like  Age      VisitFrequency  Gender  cluster  \n",
       "0        0           0   -3   61  Every three months  Female        2  \n",
       "1        0           0   +2   51  Every three months  Female        3  \n",
       "2        1           0   +1   62  Every three months  Female        3  \n",
       "3        0           1   +4   69         Once a week  Female        1  \n",
       "4        1           0   +2   49        Once a month    Male        2  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clust.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66daccb2",
   "metadata": {},
   "source": [
    "# Step 6: Profiling Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b343d444",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_segment = ['yummy', 'convenient', 'spicy', 'fattening', 'greasy', 'fast', 'cheap','tasty', 'expensive', 'healthy', 'disgusting', 'cluster']\n",
    "data_pivot = data_clust[columns_segment].groupby('cluster').sum().T\n",
    "data_pivot_percentage = data_pivot.copy()\n",
    "for i in range(4):\n",
    "    data_pivot_percentage[i] = data_pivot_percentage[i]/data_clust['cluster'].value_counts()[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "291f4b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pivot_mean = data_pivot_percentage.mean(axis = 1).reset_index()\n",
    "data_pivot_mean.columns = ['Variable', 'Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1fa50389",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Segment Profile Plot\n",
    "plt.figure(figsize = (12, 12))\n",
    "for i in range(4):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    sns.barplot(data_pivot_percentage, x = i, y = data_pivot_percentage.index)\n",
    "    sns.scatterplot(data_pivot_mean, x = 'Value', y = 'Variable', color = 'red')\n",
    "    for index, row in data_pivot_mean.iterrows():\n",
    "        plt.hlines(y=row['Variable'], xmin=0, xmax=row['Value'], colors='red', alpha = 0.2)\n",
    "    plt.ylabel(\"\")\n",
    "    plt.xlabel(\"\")\n",
    "    plt.title(f\"Cluster {i}: {data_clust['cluster'].value_counts()[i]} ({data_clust['cluster'].value_counts()[i]*100/len(data_clust):.0f}%)\")\n",
    "plt.suptitle(\"Segment profile plot for the four-segment solution for the fast food data set\", fontsize = 15)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "954c8cae",
   "metadata": {},
   "source": [
    "* Cluster 0 thinks McDonald's is expensive and disgusting\n",
    "* Cluster 1 views McDonald's as yummy, cheap, tasty and healthy\n",
    "* Members of cluster 2 share the view that McDonald's is cheap, but also think that the food served at McDonald's is greasy and disgusting.\n",
    "* Finally, cluster 3 members of this cluster believe that McDonald's food is expensive, yummy and tasty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8bd3b434",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pca = df_pca.copy()\n",
    "data_pca['cluster'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d759c275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate centroid\n",
    "index_names = factor_loadings.index\n",
    "\n",
    "# Plot data points\n",
    "sns.scatterplot(data_pca, x = 'PC1', y = 'PC2', hue = 'cluster', palette = 'viridis')\n",
    "\n",
    "# Plot arrows from centroid to provided coordinates with index names\n",
    "for i in range(len(factor_loadings['PC1'])):\n",
    "    plt.arrow(0, 0, factor_loadings['PC1'][i] * 1.5, factor_loadings['PC2'][i] * 1.5, head_width=0.05, head_length=0.1, fc='red', ec='red')\n",
    "    plt.text(factor_loadings['PC1'][i] * 1.5, factor_loadings['PC2'][i]* 1.5, index_names[i], fontsize=12, ha='right', color = 'red')\n",
    "\n",
    "# Set labels and legend\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.title('Segment separation plot using principal components 1 and 2')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0227b3b6",
   "metadata": {},
   "source": [
    "# Step 7 Describing Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "24f91bd7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5t6XI7eFNuJ40LHzN6ggATmOGM0meUddbHaPP8RsJQFw4cIXGgKZc4JHTZWhIqUsZOXZVbAlYHQ0AgAhGrgHEhaaGkFxuQ0neg2MCmXl2NewPWZgqtuUtvtDqCABOQM3Cl62OgChg5BpAXAj4TbncRrdtLrehQP9fMhUAEEco1wDigtNlyO8zu23z+0w5+/+SqQCAOEK5BhAX0jLs8vtMdbSGI9vqa0LKyLZbmAoAgO4o1wDigtNtqKjEqfdWdyoYMFW+2a+G2pCKSliKDwBimRnsUvt7P1Pnpv+zOkqfoFwDiBvnXZ6otuawHru/SW+91KmZ1ySxDB8AxDj/7ldkJGRYHaPPsFoIgLjhSbLp8uu9VscAABynUPs+hVp3y5k7WcH6jVbH6ROUa1gmaJpa4fNpRygkv2kqz2bTbLdbuXbm0AIA0B/4y5+Xu+hShbsarI7SZ3g/FZYJS0q32fRVj0c/SEpSicOhJV1dVscCAABREKjbIMOZJHtKodVR+hQj17CMyzB0vuvgOmpTnU695PerwzSVaBhHeSYAAIg1gf3r5duxXJLkyBqrUMsueUZ/xeJUfY9yjZixJxxWsmFQrAEAiEPO7HFyZo+TJIXaqxXcv1ad638rSTLDQSkcVMdH/6PEif9pZcxeR7lGTOgyTS3v6tKFLq4IAgBAvLMl5ijxjNsj94N1GxVq2Cz3iC9YmKpvUK5huYBpaklXl0Y4HJrkZM1iAADinWHYZbgOru5kONySzS6bK9nCVH2Dcg1LhUxTT3V1yWsYmsmoNQAA/ZIzZ6KcOROtjtEnWC0Ellru8yko6Sq3WwZzrQEAQJyjXEfJN77xDRmGoQcffNDqKHGjKRzW2mBQu0Ih3dferrvb2nR3W5t2hUJWRwMAADgpTAuJgqefflrvvPOOBgwYYHWUuJJms2lRcv+fewUAAE4fjFyfoqqqKt1666168skn5eRkPAAAgNMaI9enIBwO64YbbtD3vvc9jR49+rie4/P55PP5Ivfb2tp6Kx4AAAD6GCPXp+D++++Xw+HQt771reN+zr333qvU1NTIx9SpU3sxIQAAAPoS5fo4Pfnkk0pOTo58vPrqq3rooYf0+OOPn9AqF3fccYeam5sjH++++24vpgYAAEBfYlrIcbryyit15plnRu7//e9/V21trQYPHhzZFgqF9J3vfEcPPvigKioqenwdt9stt9sduZ/MCX0AAAD9BuX6OHm9Xnm9B6809PWvf11z5szpts+ll16qG264QTfddFNfx+sXukxTv+roUJ7Nphs8HqvjAAAAnDDK9UnKzMxUZmZmt21Op1N5eXkqKSmxKFV8e8XvVwYXkgEAAHGMOdeICftCIe0OhTSR5QwBAEAcY+Q6io40zxrH9rzfr0vdbjWEw1ZHAQAAOGmMXMNyGwIBJRmGCu12q6MAAACcEkau0efWBwJa/smFdMY6HNoVCukrnMDYzSMLG6yOAAAATgLlGn1unNOpcZ/Mra4OhbQ2GNRvOzslSUHTVFDS/3R06D8TEy1MCQAAcOIo17BUjs2m2w8p0RuDQW0OhfSFQ9YCPx3dujjP6giIUw8vrLE6AgCc1ijXsJTdMOQ9ZPk9t2HILinZxukAAAAg/lCuEVMmOp0sxwcAAOIWw4MAAABAlFCuAQAAgCihXAMAAABRwpxrAACAGNP6xkKrI+AQZWVlGjXqkePal5FrAAAAIEoYuQYAAIgxGecttjoCDhFqrz3ufRm5BgAAAKKEcg0AAABECeUaAAAAiBLKNQAAABAllGsAAAAgSijXAAAAQJRQrgEAAIAooVwDAAAAUUK5BgAAAKKEcg0AAABECeUaAAAAiBLKNQAAABAllGsAAAAgSijXAAAAQJRQrgEAAIAooVwDAAAAUeKwOgAOuPWDD1RaWmp1DAAAAJwCRq4BAACAKGHkOkY8PG2acux2q2PAQgsbGqyOAAAAThEj1wAAAECUUK4BAACAKKFcAwAAAFHCnGsAAOJA2yvl6tpYq1Bdh1KvHqWEsblWRwLQA0auAQCIA/YMj7yXDZezIMXqKACOgnINAEAc8IzPk3tohuTgVzcQy/gXCgAAAEQJ5RoAAACIEso1AAAAECWUawAAACBKWIoPAIA4YIbCUtiUTFNm2JQZCEl2mwybYXU0AIegXAMAEAdalm1R17oaSVKgslktS8uUfuMEuYakW5wMwKEo1wAAxIHUuaVKnVtqdQwAx0C5BgAAQNSF/e1q3/q0gs3lsrlTlTj0cjnTi62O1es4oREAAABR17HjWdncXqVN+748RRerffPfFA50Wh2r11GuAQAAEFVmyKdA/RZ5Ci+QYXfJlVUqe2KOAg1brI7W6yjXAAAAiKpQZ4MMh1s2lzeyzZ6Up1DHfgtT9Q3KNQAAAKLKDPll2N3dthkOtxTyW5So73BCIwAAcSbsC6rpT+sVrGuXTMmZnyzv7BFyZCdZHQ2QJBl2l8yQr9s2M+iT7C6LEvUdyjUAADGseWmZXEVp8kzMj2wzHDalXFkie2aiJKnz/So1P12mzK9Ntiom0I3dkyEz6FPY3xqZGhJqr5Erb5LFyXof00IAAIgzht0mR3bSwaszGoZCjV3WhgIOYdjdcmaWqHPXapmhgPz1mxXqqJUzo8TqaL2OkWsAceOVZS2q2OpTwG/Km2bXWRclq6jEfewnAv1U/SPvKljXIZmmki8ZanUcoJvEoZerfetSNb19v2zuFCWNvEY2p8fqWL2Ocg0gbkw4K1HnzfbK7jC0ryqgZU806oYFWUrw8CYcTk+Zt0yVGQipc/0+2b38oYnYYnMlyTvmeqtj9DnKNYC4kZ598EeWISkUNNXRGqZco99pfHK9ApXNkiQzEFLXxlq1vrBdkpR07mAlTS+M7Gs47fJMylfdA28q89apsnmclmQGcADlGkBceXVFi8rWdioUlIaUuJWRw48x9D/p88ZFbvd0QuNhTMn0hxRu9VOuAYvxWwlAXDn/ihRNn+1VVblf9bVBq+MAlgjUtMnsCso5MEUKm2p7tUJGgkP2zP4/nxWIdZRrAHHHZjM0aKhb697uUEa2Q4OHMdcUp5lQWK0vbFOooVOyG3IOSFHavHEy7EyRAqxGuQYQt0xTam4IWR0D6FWpc0sP2+YsSFHmzVMsSAPgWCjXAOKC3xdW+RafhpS45XAYKt/iU1WFX2dfkmx1NABAD8L+drVvfVrB5nLZ3KlKHHq5nOnFVsfqdZRrAHHBMAyVfdip155tlSSlZtg18+pUZeZy8hYAxKKOHc/K5vYqbdr3FWjcofbNf1PK5Nv6/VrXlGsAccHpMnTV/AyrYwAAjoMZ8ilQv0WpUxfIsLvkyiqVr+otBRq2yJ07wep4vYozHwAAABBVoc4GGQ63bC5vZJs9KU+hjv0WpuoblGsAAABElRnyy7B3X8nJcLilkN+iRH2Hcg0AAICoMuwumSFft21m0CfZXRYl6juUawAAAESV3ZMhM+hT2N8a2RZqr5E9MdvCVH2Dcg0AAICoMuxuOTNL1LlrtcxQQP76zQp11MqZUWJ1tF7HaiEA4o7fF9aKPzWpoS4omVJWvlPnz/YqPZsfaQAQKxKHXq72rUvV9Pb9srlTlDTymn6/DJ9EuQYQ41YtbdaAIpdKJx78gWx3GJpxZYrSM+2SpI/f79S/nm7WNV/LtComAOAzbK4kecdcb3WMPke5hmWCpqkVHR3aEQjIb5rKczg0OzFRuXa71dEQ4+x2QxmfjFKHw6YMQ2pt5DLoAADrUa5hmbCkdJtNX01Jkdcw9LbPpyVtbbotNdXqaIgTSx6pV2NdUKYpncVl0AEAMYByDcu4DEPnew6+1T/V7dZLnZ3qCIeVaONcWxzbl27JVDBgasv6TiV5eccDAGA9yjVixp5gUMmGQbGGVjzZqOrKgCQpGDC1fWOXXn/hwHJOk85N0hnTkyL7OpyGRk3y6LEH6vTlWzOV4OH7BwBgHco1YkKXaWp5R4cu9PT/s4hxbFfMS4/c7umExsOYUsBvqqM1TLkGAFiKcg3LBUxTS9raNMLp1CS3+9hPwGmvriYgf5ep3IFOhcPS+6+2yZ1gKDWTqSEAAGtRrmGpkGnqqfZ2eW02zWTUGscpHJLWvNCq5oaQbHYpd4BTV8xLk91uWB0NAHCao1zDUss7OhQ0TV2blCTDoBjhcBfNPXz1mJwCp754M2taAwBiD+UalmkKhbTW75dD0n1NTZHt1ycnq9DptCwXAADAyaJcR8Ejjzyin/3sZ6qurtbo0aP14IMPavr06VbHinlpdrsWpacfe0cAAIA4wWn1p+ivf/2rFixYoDvvvFMfffSRpk+frssuu0yVlZVWRwMAAEAfo1yfol/84hf693//d331q19VaWmpHnzwQQ0aNEi//vWvrY4GAACAPka5PgV+v18ffPCBZs6c2W37zJkz9eabb/b4HJ/Pp5aWlshHW1tbX0QFAABAH6Bcn4K6ujqFQiHl5uZ2256bm6uampoen3PvvfcqNTU18jF16tS+iAoAAIA+QLmOgs8uIWea5hGXlbvjjjvU3Nwc+Xj33Xf7IiIAAAD6AKuFnIKsrCzZ7fbDRqlra2sPG83+lNvtlvuQqxAmJyf3asZY1x4O6+n2dpUHg0q12XR5YqKKWYYPAADEKUauT4HL5dIZZ5yhlStXdtu+cuVKnX322Ralii/PdnTIa7Pp+2lputjj0d/a29UZDlsdCwAA4KRQrk/Rt7/9bf3+97/XH/7wB5WVlen2229XZWWlbr75ZqujxTyfaWpLIKALPB65DEOlLpdy7HZtCQSsjgYAAHBSmBZyir74xS+qvr5eP/nJT1RdXa0xY8boueeeU2FhodXRYl5DKCS3YchrO/g3Xp7drv2hkIWpAAAATh7lOgpuueUW3XLLLVbHiDt+05T7Myd+ug1DXaZpUSIAAIBTw7QQWMZlGPJ9pkj7TFMui/IAAACcKso1LJNht8tnmmo95ATGmlBI2Xa7hakAAABOHuUalnEbhkqcTq3u7FTANLXZ71dtKKQSluIDAABxinINS12emKjmcFj3NzXppc5OXZOUJI+Nb0sAABCfOKERlkqy2XS912t1DAAAgKhgiBAAAACIEso1AAAAECWUawAAACBKKNcAAABAlFCuAQAAgCihXAMAAABRQrkGAAAAooRyDQAAAEQJF5EBAABA1IX97Wrf+rSCzeWyuVOVOPRyOdOLrY7V6yjXAAAAMabhtYVWRzhl1157rVLHnq+HHlqrl156Sf/2b/+mHa/tUHp6utXRTlhZWZlGjXrkuPZlWggAAACiqq2tTc8884x+8pOfKDExUVdddZXGjBmj5cuXWx2t1zFyDQAAEGPyLlxsdYRTEmitVsB0aOK830a2tez06YMf/Vbff7zcwmQnx99We9z7MnINAACAqDJDftkc7m7bbA63zJDfokR9h3INAACAqDLsLoWDvm7bwkGfDLvLokR9h3INAACAqLJ7MmQGfQr5WiPbgm01ciRlW5iqb1CuAQAAEFU2h1vurBK1V6yWGQqoq26zgu21cmeWWB2t11GuAQAAEHUpIy5XqKtZta/fr7btLyl19DWyOT1Wx+p1rBYCAACAqLO5kpQ+/nqrY/Q5Rq4BAACAKGHkGgCAOND2Srm6NtYqVNeh1KtHKWFsrtWRAPSAkWsAAOKAPcMj72XD5SxIsToKgKOgXAMAEAc84/PkHpohOfjVDcQy/oUCAAAAUcKcawAAAPSacNCnpvV/UrC9TpIpZ3K+vCNm99sLylCuAQAAEBXNZUvlSiuSJ39iZJthcyil5ErZEzMlSZ1V76u57GllTv6aVTF7FdNCAAAA0GsMm12OpGwZxie10zAU6mq0NlQvYuQaAPqpmoUvWx0BUXTXXXfpqaee0r5dTZp3+Zf18x/8XC6XSzYb42SID/XvPqJgR51kmkoeeonVcXoN/yIBAIgDr776qrZs2SJJeuSRR+TxePTaa69ZnAo4fplTb1HO9DvkLblCjqQsq+P0GkauAcSNV5a1qGKrTwG/KW+aXWddlKyiErfVsWLW4MX9d2TotHRxggZdfLFqH3tfyZMHKnFsnr7y6t3Sq3dbnQxRUrlwpdURTkrj+icVaK6UJJmhgLpqN6p1+wuSpKTB5yqpcHpkX8PulCd/kurefECZU2+VzemxJHNvolwDiBsTzkrUebO9sjsM7asKaNkTjbphQZYSPLwJBwBWSR83L3K7pxMaD2fKDPkV9rdSrgHASunZB39kGZJCQVMdrWHKNYB+p+blhVZHOCnz55drxowZmj9/fmTbunXr1NzcrGnTpikQCOinP/2p/rQtR+WvPyin02ld2BNQVlamUaMeOa59KdcA4sqrK1pUtrZToaA0pMStjBx+jAFALAsEArrtttu0fft2uVwuTZkyRc8991zcFOsTxW8lAHHl/CtSNH22V1XlftXXBq2OAwC9YvAli62OcJKG6OUnd+nHT34mf9ZVSv/kHMZNIemK7yyVtLTP050sf1vtce9LuQZi0MMLa6yOEBeuuOIKzZn2TV166aVWRwF6nRkKS2FTMiUzbMoMhCS7TYbNsDoagENQrgHErXA4rB07dlgdI2bF68oD6Nn8+fP1xz/+UZLkq2xSw9KNWrx4sX784x9bnAzAoSjXQAz65uLBVkeIOX5fWOVbOjWkxCOHw9DOLZ1aubJBqUUfqmzxfVbHixm/WlhpdQT0kscff1xjxozRpZdeqtGjR+v111/XnDlzNG/ePA0dOtTqeAA+QbkGEBcMQ9r0YbteffbAJXNTMxyaeXWGsnJdFieLXcMWX2Z1BPSCR/+5Svrngdv+VJvO+a8vKHlUnrWhEBXbFz5vdQREAeUaQFxwumyaOz/H6hhAzAj7gvLXtsmVnWx1FACHYHFYAADi0P7lG5VYkkO5BmIMI9cx4ta331ZpaanVMYCY9vKyBlVs7fzk8ucOnXVRqoaU9L+rewGfal2/V/uXb5QkeccNUPac0ZKk+pVbFGzuVP5XplgZD0APGLmGZXw+n2666SYNHDhQqampmjFjhjZs2GB1LMSwiWd5deOCAfrGfw3URZ9L18p/1qurM2x1LKBHOxa9oGCr75RewztugIrvvETFd14SKdaNb5Srfet+5X35DNmc9sOes/PulQq2ndpxAZw8Rq5jxMPnnKMc1+l1YpbfNLWrvV3XejzyJiTo7Q8+0PkTJ+q2rCyro1liYQ1rWx9LevahV/MyPrn8eYjLnx8BJ0f1vqKiIi1ZskTTpk077DFjkaG3v/tn5eWd2MmGjz/+uJYsWaIXXnjhsMf+8Ic/aPGWxVr3YZkGDhzY8wscctXsGTNm6Oabb9aXvvSlyLb77rtPmzdv1uOPP97j0ysqKjRkyBAlJSVFtv3mN7/RvHnzTujzAE5XlGtYxmUYOj/54FzBqYmJeqmtTR3hsBJtlCX0bPWKBpWt7VAoaGpIiUcZOf3z8rlAT37605+qurpaI0eOjGz7r//6L/3Xf/1XVI/jdrvV1tYW1dfE6Sfkb1fDxqflayyXPSFV6SMvV0JGsdWxeh3lGjFjTyCgZJuNYo0j+uD1Fu0p98lmkzypdrk9XJnuaEYtvsLqCP3e3qZaXff778jzYnqPj0/82kzVv75dYV9QGecMVda5B9aj7tjdqH3Pb5S/rl02j1OZZw9RxplD5G/o0M7/eVVm2JTN5ZArI1HF/3GeQh1+1Ty3Ue21dbIlOZQyY5jSJhwYuf5T4E396ZD/12WLntXw71ykxg93q+61rXrt9TX68leuV/b5w5V57lDVrdkuf137Eb8//I0d8gcDfP9YYNPCFVZHiKrGzc/K7vZqwPnfV1f9DtWv/5vyz7lNNmf/PleGco2Y0BUOa3lLiy5M5qx3HJlhSLOuzVRmtlN7K316+on9GlDo1qiJfN8gNnVU1Kv4lvPkb+xUxe9eV8rofLnSE2XYDOVdMVYJeSnqqm5W5RPvKHFwhhLyU5V3xRi1fFytwTdMjbxO1dK1Ssjxati3L1SgsVOVf3xbCXkpSshLOeKxs88fro6ddUqbXKjUsQNOKLcZCmvbA/+S7DZ5R+Yq56KRsrkOn98NHEk46FPn/i0acO4C2ewuJeaUqq3yLXXu36KkAROsjterKNewXMA0taSpSSPcbk3y9O+/Zo8XV9k7PulPp6s4+2r9auHvrI4C9Cjz3KGyuRxKyPXKneOVr7ZVrvREeQrSIvt4CtKUPDxHHZWNSshPPew1gq1d6tzVqEHXTZFhM+TOTlbK2AFqLas5ark+WY5El4q+fq4S8lIUbOnU3qXrVLtqs/IuGx31Y6H/CnY0yOZwy+72RrY5k/MUaN9vYaq+QbmGpUKmqaeam+W12zWTUWscRVtbm5555hl97nOfU0JCgpYsWaKmpiZdcQVvXSN2OZIOnqhuc9oV9ockSb7aVu17YZO6alpkhsIyg2G5spJ6fI1Ac5fCgaC23v/SwY2mqZSxBVHJuPnugydOjrxzlmxuhzwDDpR8Z1qici4eqT1//5ByjRNihvyyOdzdthkOt8xgl0WJ+g7lGpZa3tKioGnq2tRUGQbzZz/1rcXDrI4QcwL+sFb8uVr/9u9fkXRgikhBYYJWrf2OVq39jsXpYsf/W7jd6gg4DjXPfazEokwNvG6ybE67qp76SDI/efAzPwsdKQmyJzg14gczT/xAPfxYzZre/efLyDtnHeM1+NmME2fYXQoHuy8JaQZ9Muz9f2U0yvUp+MY3vqF//etf2rt3r5KTk3X22Wfr/vvv73YWN46sKRTS2q4uOSTdt//g20TXp6Wp8DRblhA927K+Va8sP/C9UTLOq7nzD4zUvbGyXtWVXbry+nwr4wEnLewLye52ynDY1LGrQW1ba5Vx1hBJB0a7gy2dMsOmDJshZ0qCEgakaf/LW5R57jAZdkNd+1plc9jkzvEe9Tj2JLcCTR0nlK1zT5PsHqecGYkKtvpUu2qzkkfknPTnitOTIzFD4aBPIV9rZGpIoK1GSQMmWZys91Guj2HGjBmaP3++5s+ff9hjZ5xxhubNm6fBgweroaFBixYt0syZM1VeXi67nRM/jiXNbtei3FyrYyCGlYzzqmRc9/Lw4RuNqtjarqv/rUAOJyvLID7lXDJS1cs3aP8rW5Q0NFvJJQfLa+KQTDlSPNr63y/JmZao4puna8DVE1T74iZtf+gVmaGw3Dle5c4adczjZEwt0t6n16n+jR3Kmj5MmecMVd1r2xVo7lT+nLE9Psff0K79q7Yo2OGX3eOMnNAInAibwy1Pdomad65W2ohZ8jXsUKCtVp7sEquj9TrK9Sn4+te/HrldVFSk/+//+/80fvx4VVRUaOjQoRYmA/qnTR+2aP27LfrCvxUowcMfsLDesNsvPOJjpYsu73a/8KazIreTirM07LYLenyezWHvtlKIdOAkwwFzJxxXpkOPm1iYcdhxss47+rSz1HEFSh0XnfncOL2lj7xcDRuXau+r98uekKLMsdf0+2X4JMp11LS3t+uxxx7TkCFDNGjQoCPu5/P55PMdnIPEIv3A8XvvtUa1twb1p4cPrqZyxvR0TTmv5zWGAQDWsbuSlD3xeqtj9DnK9Sl65JFH9P3vf1/t7e0aOXKkVq5cKddR5gvfe++9Wrx4cR8mBPqPGxcUWh0BAICjYsLiZ9xzzz1KTk6OfKxZs0Y333zzYds+NW/ePH300Ud69dVXNXz4cF177bXq6jryMjN33HGHmpubIx/vvvtuX3xaQJ/71aIdam8N9vlxH717pzra+v64AABIjFwf5uabb9a1114buT9v3jxdffXV+vznPx/ZVlBwcC5aamqqUlNTNXz4cE2bNk3p6elaunSprrvuuh5f3+12y+0+uO5jMms7I449/stdmvWFXOUNSojaa5Z91KKtH7fpczec2BXlPnXzncWR2/98rEpjJqdoxNiDJ0W+v6ZRjXUBXTK359UPWhoD+uNDlXI6Dy4/dsGc7MNOrAQAoCeU68/IyMhQRkZG5L7H41FOTo6GDTu+dYdN0+w2pxrHzxcO609NTaoLBmVKync6NdvrVbaDb1P0Lbvd6FbSAQA4XrSWk7Rz50799a9/1cyZM5Wdna2qqirdf//98ng8mj17ttXxYt7S5mYVuVyaeMjlzh2GoStTUpT5yTKG73d26unmZn0tM9OqmJaJlwuBLHusSN/+6hJNmzbtsMd+tcjQmSP+S/fee69aW1v1ve99Tz/4wQ8kSW+99Za+9a1vacuWLUpPT9d3v/tdffOb39TOnTs16t5RCgaD+sPPajR06FCtW7dODQ0NuvXWW/Wvf/1LycnJWrhwoW688cYeMxmGoerqav3ud79T9e5Fqqtp1usvtOvHP/6xvv/97+u+++7T5s2b9f8WPt7j8ysqKrTk0ZFx8/8AABBbKNcnKSEhQWvWrNGDDz6oxsZG5ebm6rzzztObb76pnBwW2z8ZdsOIjFKHTVOGpMZQyNpQOCWrV6/Wxx9/rPLyck2ZMkVf/OIXVVRUJKfTqd/85jeaMGGCPvzwQ1100UU699xzNXHiRD366KNasmSJXnjh4CWZb7jhBo0ZM0a7d+9WeXm5LrzwQk2YMEHjx48/4rF/9KMfadWqVbr55pv1pS996YRy+/1+DRgwQE6nU3PnztU999yjxMTEk/46AMDpqKXidbXv/ejAhWQSUpQ69EIl5hx7ffZ4R7k+htWrV/e4fcCAAXruuef6Nsxp4pH6+sjUkEtO0znpty2Ojx8+DU179YvfX6f8F3tet9RIfVv/9cAUSVJqhvSt/5quISUH5y7/cfmB/+YNlm6/8zKNPzNDmz5q0tbtLZGvQXtrUCv/tUPFk3fq+/cdeEJekU9fv+1iTbsgu8fj3vXAeUryOrS9Ypf++NQ2vbXlJ5HH3l9Tp8Y6/xG/xn5fWF/8WqGy8hLU1hLUU0t/p9VvP6HzL8s7sS+ORR5auMnqCABwgGEoc9y1ciZly9dUqbq1f5ErOU+OxIxjPzeOUa4Rc27JzFTANLW+s1NernQZ1zxJB3/EOJw2BfxhSVJ9rU+vvbBP+2u6FA6ZCgZNpWf1vIRla3NAwUBYv71/a2SbaUolY1OikvHXd2+O3P6PO0fK5bYpZ8CBPxZS0pw6++IcPf/3PXFTrgEgVqQUnhO5nZBeJGdyjvytNZRrIFqebGxUZSAgSQqYpjZ2demF1lZJ0rlJSZqelBTZ12kYmuTx6IG6Ot2amSmPjVUj+5PVz9VoYFGi5lw3UA6nTS88VSXTPPCYYXTfNznFIXeCXV//wYgTP5Bx+KbJ07O63f+PO49+WefP5gEAnLhw0KdAe62cyT2/49ifUK7RZ+alH7yKXk8nNH6WKclvmmoNhynX/UzAF5bLbZfdYahqV4fKt7Zp4lkHRjI8SQ61tQQVDpuy2QwlpziVMyBBb7+8X2ecmym73VDdvi7ZHTZl5riPepzEJLtamgInlK1mT6cSPHalZjjV3hrUm6tqNWTE6Tk9CQCipbFsuTxZJXImUa6BPlMTCKjLNDXQ6VRY0qttbUowjMjqIeg/zrkkR6uWV+vtV/Zr8NAkFZccLK8DhyQqOcWh3/33VqWkOXXdzcW69OoBWvNirf740HaFQqYyc9w6b1buMY8zfmqGVj69Vx+8Ua8p07M06ZxMvfdanVqbA7pwTn6Pz2lu8Ov5VfvV2RFUgseu4pFenX0RJykDwLG0V69X4+YD58Yk5o1TRukcSVLTtpUKdjUre9JXrIzXZyjXiBkhSS+0tqohFJJd0gCnU/PS0mTnffmYddPtR17//VuLSrvdv/qmg5cuH1ScpPm39fxch8Omq24Y3G2bJ9GhmXOP76Iyhx53QGGibvzMcaacl/XZp3RTMi5VJeNSj+tYAICDkvLHKSl/XLdtLRVvqLNuq3In/5tsdqdFyfoW5TpG3PrGGyotLT32jv3EQqsDAACAXtVW9aHa9ryrnMn/JpvzyNNA+xvKNQAAAKKupfw1hXytqnnr4ci2lKLpShlynoWpeh/lOkY8fMEFynH1vBQZTg8LKyutjgAAQNQMOHeB1REswRIMAAAAQJRQrgEAAIAooVwDAAAAUUK5BgAAAKKEcg0AAABECeUaAAAAiBLKNQAAABAllGsAAAAgSijXAAAAQJRQrgEAAIAo4fLnAAAAMaZy5UKrI+AQZWVlGjXqkePal5FrAAAAIEoYuQYAAIgxwy5bbHUEHMLfVnvc+zJyDQAAAEQJI9cA4saqZdUq39qqgN9USppTZ1+UrSElXqtjAQAQQbmGZYKmqRUNDdrh88kfDivP5dLstDTlulxWR0OMmnhWhs6fnSuHw6Z9VZ1a+kSl5i8YpgSP3epoAABIYloILBQ2TaU7HPpqTo5+UFCgEo9HS+rqrI6FGJaR7ZbDcfDHVihoqr01aGEiAAC6Y+QalnHZbDo/NTVyf2pysl5qalJHKKREOyOR6NkrK6q1aW2zQkFTxSXJysxxWx0J6BP7X9mqlo3V8te1acDVE5U6doDVkQD0gHKNmLHH51OyzUaxxlFdcEW+zp+dpz3lHaqv7bI6DtBnXBmJyr1slPa/vNXqKACOgmkhiAld4bCWNzbqwkNGsoEjsdkMDR6apN07O7Rre5vVcYA+kTp+oJKHZsvm4Fc3EMv4FwrLBUxTS+rqNCIhQZOSk62OgzhimqaaG/xWxwAAIIJyDUuFTFNP1dfLa7drZlqa1XEQw/y+sDavb5bfF1Y4ZGr7phbtqejQgMJEq6MBABDBnGtYanlDg4KmqWszM2UYhtVxEMMMQ9r0YZNWP1sjSUrNcGnW1QOUlZtgcTIAAA6iXJ+kQCCgu+66S88995x27typ1NRUXXzxxbrvvvs0YABncB+PpmBQazs65DAM3VdVFdl+fXa2Ct2sAIHunC6bPj+/0OoYAAAcFeX6JHV0dOjDDz/Uj370I40fP16NjY1asGCBrrzySr3//vtWx4sLaQ6HFg0aZHUMAIgLZigsM2zKNCWFwwoHQjLsNhk23vUDYgnl+iSlpqZq5cqV3bb96le/0tSpU1VZWanBgwdblAwA0B9VL9ug5nV7JEmdlQ3S0nUafOM0JQ3JtDgZgENRrqOoublZhmEo7Sgn5vl8Pvl8vsj9tjaWEQMAHNuAueM1YO54q2MAOAZWC4mSrq4u/fCHP9SXv/xlpaSkHHG/e++9V6mpqZGPqVOn9mFKAAAA9CbK9XF68sknlZycHPlYs2ZN5LFAIKAvfelLCofDeuSRR476OnfccYeam5sjH++++25vRwf61GO/3K7q3Z19drw//c8O7avqu+MBAHA0TAs5TldeeaXOPPPMyP2CggJJB4r1tddeq/Lycr388stHHbWWJLfbLfchK2Ekc9EUxKHHfrlds75QoPxBHquj6Pr/HBq5vXLpXqVnuTR5elZk29YNzdrwfpOuvunEVhp5+5X9en9Nnez2gyeL/cedI089MACgX6NcHyev1yuv19tt26fFetu2bXrllVeUmclJJSfj9ZYWfdTertZQSCl2uy5MTdWoRC4Mgt7X0ujXPx6v1E23D+vx8VET03ThnPw+TgUAiGeU65MUDAb1hS98QR9++KFWrFihUCikmpoDF7fIyMiQy+WyOGH8MCRdm5mpbKdTlT6f/lJXpzyXSxkOvj3jXVdHSK88W6PdO9vlTrDpjHMzNeaMdLU2BfSnR3bqa98bLofzwOy0jz9o1M7Nbbpy3iAFA2G9/lKtdpS1SoY0/sx0TT43q8djfDqK3lTv05YNzZJh6L3X6lQ6IU0zLs/ry08XkCRt/+XLKvjCRHkGpffJ8Xb8z6sacNV4eQrS+uR4AI6O9nKS9uzZo2XLlkmSJkyY0O2xV155RTNmzOj7UHHqnEOm0hQlJCjH6VSN30+57gdWP1cjm0266fZham7wa+kTlcrIcmtAYaIyc9wq39qm4aMP/P/ftrFVI8enSpLWvFQrvy+kG745VH5fSE//X6Uyst0qLvEe8VilE9K0p7zjsGkhp2Lrxy3atrFF3jSnpp6XpWGjjj7tC6ePvi7QRzP0P8+P3N67dJ1cWUnKmn7w3ZjmDXvV9P4uFd501gm97v5XtqpuzXYZ9oOnZ428c9apBwb6OdrLSSoqKpJpmlbH6Hd84bBqAwFlO51WR8EpCodNbd/UohtvGyany6asvASNmpSmLR+3aEBhokaMSdG2jS0aPjpFne1B1ezp1OxrC2Sapso+atL8BcPkctvkcts0dnK6tm9qPWq5jrbho1M0dnK6PIl27S5v1/N/r5I31ancAuvnmQPR4m/sUOXjb2vY7Rf2+HjaxEHKnzO2j1MB8Y1yjZiyvLFRJR4P5bof6GwPyTSl5JSDP2ZSUp3atePA2u7DRnn11sv7FfCHtb2sVYOKE+VOsKujLahg0NQTv9oReZ5pSgMGR6fU7t3VoWV/3h153YA/rEfv3RJ5/OY7SiRJmTkHTzwuHJasEWNSVL6ljXKNExLq8Kvm2Y/VvrNOtgSnMs8dqvQzBivQ1Kmdj7yq4d+7RDanXZLU+EGl2jbv06B5UxQOhFT7Uplay2okw1D6mUXKOndoj8f4dBTdV9+u5g1VMgxDda9tV9qEgcq7fExffroARLmGBda3t2t5Y6MkaVxiouZkZEiSVjY1qTkY1Feys62MhyjxJNllGFJbS1De1AN/LLU2B5TkPXA7OcWp7PwEVWxr07aNLRozKe3A8xLtsjsM3XT7MLkT7Cd20B6uAj1ibKpGjE2N3B9QmBgp0Mc6obHbS3OFaZyEmuc2SjZDw26/SP6GdlU+8Y7cWclKLMyQO8ertq21Shl94KTZ1o3VSh1/YCWq2pfKFPIFNfSbMxTyBVX5f+/InZ0sb0nuEY+VNmGgOsrrD5sWcipaPt6rlo3VcqZ5lHXeMKWM4gRf4FhY5xp9blxSku4cOFB3DhwYKdZvtLRoa2envpydLaeNb8v+wGYzNLTUq7dW7VcwEFb9vi5t/KhJI8YcnLc8YrRX699t1L6qLg35ZMqHYTNUOj5Vr79UK19nSGbYVMN+n2r2HHst68Qkh1qaAlHJv3Nzq3xdB46/e2e7tmxoUeFwls7E8TPDplo2VSvn4pGyuexKyEtR2qRBavl4ryQpZcwAtWysliQF2/3q3NOo5JJcmaappo92K3dmqWxuh5wpCUqfXKjWTdV9mj9ldL6G3nq+RnzvYuVcPFLVz6xXZ1VTn2YA4hEj17Dch21teretTf+WkyMPxbpfmTE7T688W6M//GK7XG6bpl2QrYLCg8ssDhuVolef36ehpV45XQf/30+/NFdvrarVk7/eKb8vrLRMl8668NjvaIyamKrn/lalR+/dotLxqTp/dp42r2/WlvXN+tz1g08o+5YNLVr59F6Fw1JKmlMXzsmPiXW9ET9C7T7JNOVISYhsc6Z61LZjvyTJOypf+1/eorA/pNayaiUWZ8me4FSwzSczGNaOX60++GKm5BkcnZMnO3Y1aPef34u8btgf1JZ7X4w8XnLHpZIkd87BcxySh2UrZcwAtW3Zx6okwDFQrmG511pa1BoK6eFPljKUpOkpKTrvGBfkgXWONo3i0Mc8SQ7NvnbgEfdNTHbomwtLD9vudNl03mV5Ou+yYy+ld+jx0rPcmndLcbfHR45L1chxqZ99miQpJd11xM/lsmsKjnls4GjsSW7JMBRs6ZIz9cAfZoHmTjm9B8q2MyVBCfmpattWq5aN1UqbdOAPQHuiS4bDpmG3Xyh7wgmef9LD9KXUsQOUOnZA5H5iYUakQB/rhMZjvTaAw1GuYbkFAwYceycAiDOGzZC3NE/7V21R3pyx8jd0qOmj3Rp47RmRfbyj89X4boW6qlvkvS4n8rzU8QNV+1KZci45MDXEX9+msC8kz8C0ox7TkeRSoOnYU6iOR+vmfUosypDN5VBHRb1aNuzV4OunRuW1gf6Mcg0AQC/Jmz1GNc9+rO2/WCWb26HsC0YosTAj8njKqHzte36TvKW5srkO/krOvXSUaldt1s5fv6awLyhXZpKyLyw55vFSJw5S1d8+1JZ7X1Tq+IHKmz1azeur1Ly+6oSLccuGKu19ep0UDsuZlqj8OWNjYl1vINZRrgEAOEFHm0Zx6GOOJJcGXjvpiPs6kt0qXTj7sO02l115l41W3mWjTyiLOytZxbec1+3x1HEFSh3X8zQnV3riET+XgmuOnBvAkXH2GAAAABAllGsAAAAgSijXAAAAQJRQrgEAAIAooVwDAAAAUUK5BgAAAKKEcg0AAABECeUaAAAAiBLKNQAAABAllGsAAAAgSijXAAAAQJRQrgEAAIAooVwDAAAAUeKwOgAOuPWVV1RaWmp1DAAAAJwCRq4BAACAKGHkOkY8fOmlynG5rI4BCy3cvt3qCAAA4BQxcg0AAABECeUaAAAAiBLKNQAAABAllGsAAAAgSjihEYhBDy3cZHUEAABwEhi5BgAAAKKEkWsgBi1YPN7qCIhTDy5cZ3UEADitMXINAAAARAnlGgAAAIgSyjUAAAAQJZRrAAAAIEoo1wAAAECUUK4BAACAKKFcAwAAAFFCuQYAAACihHINAAAARAnlGgAAAIgSyjUAAAAQJZRrAAAAIEoo1wAAAECUUK4BAACAKKFcAwAAAFFCuQYAAACihHINAAAARAnlGgAAAIgSyjUAAAAQJQ6rAwAAesemhSusjgAApx1GrgEAAIAoYeQaAPqp8YuvsjoCgBOwbuHTVkdAFDByDQAAAEQJ5RoAAACIEso1AAAAECWUawAAACBKKNcAAABAlFCuAQAAgCihXAMAAABRQrkGAAAAooSLyACIG6uW7dHOrS0K+sPyprl09kV5Ki5JsToWAAARlGsAcWPiWVk6f/YAORw21VR1aOkTO3XTgpFK8PCjDAAQG/iNBMsETVMr9u/Xjo4O+U1TeS6XZmdlKdfttjoaYlRGdkLktiEpFDTV3hqkXAMAYga/kWCZsGkq3eHQVwcOlNdu19vNzVpSU6PbCgutjoYY9vKKKm1a26BQ0FRxSYoycxKO/SQAAPoI5RqWcdlsOj8jI3J/amqqXqqvV0copES73cJkiGUXXlGgGbMHaE95m+pqu6yOAwBAN6wWgpixp6tLyXY7xRrHZLMZGjzUq90727Rre6vVcQAAiKBcIyZ0hUJavn+/LjxkJBs4FtOUmhp8VscAACCCcg3LBcJhLamp0YjERE1KYVk19MzvC2nz+kb5fSGFQ6a2bWrWnoo2FRQmWR0NAIAI5lzDUiHT1FP79snrcGhmZqbVcRDDDMPQxg8b9MqzVZKktAy3Zl09WFm5HouTAQBwUK+X68cff1wLFixQU1NTbx/quMyfP19NTU16+umnrY4CScv371fQNHVtTo4Mw7A6DmKY02XT1fOHWh0DAICjOuVpIfPnz9dVV10VhShHZxhGVArxQw89pMcffzxyf8aMGVqwYMFh+z3++ONKS0s75uvNnz9fhmF0+5g2bdop5zwdNAUCWtvaql1dXbqvvFx379ypu3fu1K7OTqujAQAAnJTTblpIampq1F9z1qxZeuyxxyL3XS5X1I/RH6U5nVo0lJFIAADQf/TZCY0vvviiSktLlZycrFmzZqm6ujry2HvvvadLLrlEWVlZSk1N1fnnn68PP/ww8nhRUZEkae7cuTIMI3JfkpYvX64zzjhDCQkJKi4u1uLFixUMBo+Y49CR9vnz5+vVV1/VQw89FBl1rqioOOHPze12Ky8vL/KRwYoXAAAAp6U+KdcdHR164IEH9H//93967bXXVFlZqe9+97uRx1tbW3XjjTdqzZo1evvttzV8+HDNnj1bra0H1q997733JEmPPfaYqqurI/dffPFFXX/99frWt76lTZs26Te/+Y0ef/xx3X333ceV66GHHtJZZ52lr33ta6qurlZ1dbUGDRp0wp/f6tWrlZOToxEjRuhrX/uaamtrj7ivz+dTS0tL5KOtre2EjwcAAIDY1CfTQgKBgB599FEN/WQKwK233qqf/OQnkccvvPDCbvv/5je/UXp6ul599VVdccUVys7OliSlpaUpLy8vst/dd9+tH/7wh7rxxhslScXFxfrpT3+q73//+1q4cOExc6WmpsrlcikxMbHb60qSzWaTzXbsvz0uu+wyXXPNNSosLFR5ebl+9KMf6cILL9QHH3wgt9t92P733nuvFi9efMzXBQAAiGchf7tqNzytzvpyORJSlTX6ciVmFlsdq9f1ych1YmJipFhLUn5+frfR3draWt18880aMWKEUlNTlZqaqra2NlVWVh71dT/44AP95Cc/UXJycuTj01Hojo6OU8r86et96sknn+x2nDVr1kiSvvjFL+ryyy/XmDFjNGfOHD3//PPaunWrnn322R5f94477lBzc3Pk49133z2lnMDp7r01tXpo0XpV7263OgoA4BD7Nz0ru9uroou+r8ySi7Vv7d8UCvT/RQv6ZOTa6XR2u28YhkzTjNyfP3++9u/frwcffFCFhYVyu90666yz5Pf7j/q64XBYixcv1uc///nDHktISDilzKmpqd3K9ZVXXqkzzzwzcr+goKDH5+Xn56uwsFDbtm3r8XG3291tRPvQY8S6RTt26DuFhfI6+vY82Lt37tRtgwcruY+Pi9jw0tLdGliUpFETDz+Xoa0loC0bmpSYzPcGAMSScNCn9n1bVHj+AtnsLiXllspV8ZY6arfIWzDB6ni9KiZ+I61Zs0aPPPKIZs+eLUnavXu36urquu3jdDoVCoW6bZs0aZK2bNmiYcOGnfSxXS7XYa8rSRdddJE2bdoUue/1euX1eo/5evX19dq9e7fy8/NPOpOVfrlrl76Qm6tBp/jHyaE+amnRx21tumHAgJN6/p3FB99CeqyqSpNTUjT2kP8XaxobVRcIaG5OTo/PbwwE9FBlpZyHrKM9Jztb447j/ydi25oX92raBbl67YW9VkcBABwi0NEgm9MtR8LB37Uub578bfstTNU3YuLy58OGDdP//d//qaysTO+8847mzZsnj6f7VdeKioq0atUq1dTUqLGxUZL04x//WE888YQWLVqkjRs3qqysTH/961911113Hfexi4qK9M4776iiokJ1dXUKh8OSpKVLl2rq1KlHfW5bW5u++93v6q233lJFRYVWr16tOXPmKCsrS3Pnzj3BrwJ6k90wdGdxceSDYh3/9pS3qbMjpGGl0V9eEwBwasJBv2yO7uee2RxuhUNHn5XQH8REuf7DH/6gxsZGTZw4UTfccIO+9a1vKeczo5A///nPtXLlSg0aNEgTJ06UJF166aVasWKFVq5cqSlTpmjatGn6xS9+ocLCwuM+9ne/+13Z7XaNGjVK2dnZkXnezc3N2rp161Gfa7fbtWHDBn3uc5/TiBEjdOONN2rEiBF66623jmuUOx5t6ejQg7t26f7ycr3+yR85krS7q0u/3bNH9+zcqV/u2qV3mpslSQ2BgFbU1WlnZ6fu3rlTv969W5LUEQrpqX379N/l5Xpw1y6tbWk54jEX7dih1mBQrzY0qLKrS0/v36+7d+7sdnycXsIhU6+9uFfnzTq5d0MAAL3L5nApHPR12xYO+mSz9/9rgZzytJBDr3bYk/nz52v+/Pndtl111VXd5lxPnDgxsrzep77whS90uz9nzhzNmTPnsNe/9NJLdemll5503k/L8PHk/iyPx6MXX3zxuI/dH1R0duqWQYPUGAjod1VVGp2crHSnUzZJV2RnK8/lUrXPpyeqqzU4IUH5breuyMo6bFrI0tpa5bhc+nZRkRoDAf1x717lud3K62GFlU+dn5GhnZ2dh00LOR4h09QDFRWyG4ZGJiXpoowMuY5jNRhY45kny7W38sAJisGAqW0bm/TqJ1M/Jp+bI4fTpgGDk5SVG73pSwCA6HEmZigc8Cnoa5XDfeB3tr+1RikDJ1mcrPfFxJxrxI9z09LkstmU63Yrx+VSrd+vdKdTBYfM0S5ISNDwxERVdnUpv4ey3BoMaldnp67Ly5PNMJTtcmlscrLK2tuPWq5PVqLdrq8PHKg8l0stwaCW1tZqVUODLsvKivqxEB2fmzckcrunExqX/6VCVbvatW3jgXdIOjuCWvbnCp07M1+jezjxEQDQt2wOt5JyStS4bbUyS2eps36H/G21SswpsTpar6Nc44Qk2e2R207DkP+TOeq1fr9eqKtTjc+nkKRgOKysz6wS86nmYFAB09T9h1wN0zTNEx6NPpK7d+6M3L6zuFhum00DPintaU6nLs7M1N/37aNcx7GZVw1SMBiO3F/yu+268IoCDSyKn9V3AKC/yx59uWrXL1XFqvvlSEhR7vhrZHd6jv3EOEe5RlQ8t3+/ijweXZeXJ6fNpqf27dOnE3+Mz+yb4nAowWbTD4YM+ezLHNNnX0uSpqend7t/6Ooix/saiC9uj11uHfxDzzCkBI9dThdTfQAgVthdScqffL3VMfocv4kQFT7TlNtmk8MwtKuzU1vbD17QI8luV0swqPAn8+xTHA4NcLv1ckOD/OGwQqapvT6fao+xrvmnr9UUDJ5Qtj1dXar3+2WaplqCQa1qaNCIxMQT+wRhmZlzB/W4xvWh/u32UuUPSuqjRAAAHBkj14iKSzIytHz/fr3S0KChiYkqSTpYdIYkJiqluVn/XVGhNIdDNw8apKtzc/ViXZ0eqqxUyDSV43Jp1nFM05iamqqna2v1RlOTpqen65y0NL3W2KjmYFBzsrN7fE5DIKBVDQ3qCIXksdkOnNCYmRm1zx0AAOBTlGt0c/tRljFcdMgl7CXppkOuUlmcmKjbjvBch2EcdgGZRLtdc3NzjyvTocct9HgOO855n5kW8lnjvF7WtQYAAH2CaSEAAABAlFCuAQAATnM7XlikoK+1z4+7c+XdCvra+vy4vYlpIQAAAKeBXat/qdwJX1BC2qCovWbLno/UVvOxBky+4aSeX3zJnZHbVe88ppTBk+XNHxvZ1rhzjQJtdcoZN7fH5wc6GlX52kMy7AeX/80ePUfeAeNOKk80UK4BAAAQtwybvVtJtxrlGgAAAOqo3aLGna8rHPQpbcg5Si8+V5LU1bhbdWXPy99eJ7vTo7QhZyu18EwFOhpUt2mFTDOsnSvvljMxQ4PO+Q+F/B2qK3tOHfU7ZbO7lD5shlIKJvR4zB0vLFLhBd9Ry+4P1dVYKV/zHu3/eJnSh54fOX68oVwDAABAnQ0VGnTOLQp0Nqrqrd8pOW+0nInpks2m7NFXyJWSJ19Ltarfe0IJ6YPlTslX1qgrDpsWUrthqVzJOSqa8W0FOhq1970/yu3Nkzsl74jHzhh2vjrrdx42LeR4mOGQKl55QIZhV1LuSGWMuEg2u+ukvw6nihMaAQAAoLTic2VzuOT25srlzZG/rVaSlJBaIHfqABmGTQmpBUrMGq6uxsoeXyPoa1Vnwy5ljLhIhs0hV3K2kvPHqn1fWa9ktrsSNfCsr6twxrc14Myb5GupUcPWVb1yrOPFyDUAAABkdx28AJxhcyocOnDlZH9rreo2vyBfS41khhQOBeVM7vnCb8HOZpmhgCpW3R/ZZpqmvANObDT6SHauvDtyu/iSO2VzuOVOPXAtDacnTZklF2vf2r8rq/SyqBzvZFCuAQCIAzWvbFbzxir56to0+OozlDZ2oNWRcJrYX/acPBlFypt0nWx2p/atfUoyzQMPGka3fR0JKbI5EzTkoh+c+IE+81qSlF48vdv9Y5+4ePhr9DWmhQAAEAfcGUkacNlYJRYc/aq0QLSZQZ9sDrcMm0OdDbvUvn9r5DG7K0nBrhaZZljSgXLtTh2ghm0vKxzyywyH5GveG5licjR2V5KCnU0nlK2raY/87fUyTVPBrhY1bF2lxOwRJ/Qa0cbINQAAcSB9/IG1iWtf23qMPYHoyii5RPs/Xq6Gba8oMWuoknJKIo8lZg5Rc0KKKlb9txyJaRp09s3KHXe16ja/qMpXH5JphuRKzlHWyFnHPE5q4VTVbnhaTTvfUPrQ6Uobco4ad7ymYFezskfP6fE5gY4GNWxdpVCgQzanR0k5I5U54qKofe4ng3INAABwGiiccfsRHxs6a1G3+wVn3hS5nZhZrMLzb+vxeYbNcdgFZOyuROUe4aIvRzuuJ71Qhed1P0760POO+nzvgHGWXjCmJ0wLAQAAAKKEcg0AAABECeUaAAAAiBLKNQAAccAMhRUOhCRTMsOmwoGQzLBpdSwAn8EJjQAAxIE9y9aqcd1uSVJ7Zb12L/1QxTeeo+QhPV/MA4A1KNcAAMSBQXMnadDcSVbHAHAMTAsBAAAAooRyDQAAAEQJ5RoAAACIEso1AAAAECWUawAAACBKKNcAAMSx2jVbtX7RM2rf3WB1FACiXAMAENN2L/1QDR9V9vhYoKVTTRuq5Eh293EqAEdCuQYAIE7tfXGjci8okWHn1zkQK/jXCABAHGorr1Oow6fU0gFWRwFwCMo1AABxxgyFtffFjzVg1lirowD4DC5/DgBAjCl/8m21V9ZLksxASE0b92rvCxskSTnnDpfNaVfS4Awl5KZYGRNADyjXsEzQNLVi/37t6OiQ3zSV53JpdlaWct2cmAPg9DZk3rTI7d1LP1RSUZYyJg6ObKv4yztq31Wv5o17JUnBDp8q/vy28meOVsbEwj7PC+AgyjUsEzZNpTsc+urAgfLa7Xq7uVlLamp0WyG/GB5cuM7qCABi2KCrJikcDEXub//dqyq4YoKSizItTAVAYs41LOSy2XR+RoZSHQ7ZDENTU1PVGAyqIxQ69pMB4DRm9zjl9CZEPmQYsnucsrkYMwOsxr9CxIw9XV1KttuVaLdbHcVyty+eYnUExKlfLnzP6giIskFzJx1zn9LbZ/ZBEgDHg3INyxw659oXDiss6dy0NKtjAQAAnDSmhcAyn865nl9QoHy3WwVut9a1tlodCwAA4KRRrmEZl82mc9PT9VJdnVIcDl2fn8+cawAAENco17DU8v37FTRNXZWToyqfjznXAAAgrkW1XD/++ONKi4E5s0VFRXrwwQetjoFjaAoEtLa1Vbu6unRfebke37tXnaGQdnV2Wh0NAADgpJxQuZ4/f76uuuqqXooSPe+9956+/vWvR+4bhqGnn376sP2i8fkYhnHYx6OPPnpKr3m6SHM6tWjoUP2gqEgFbrfOSk3Vj4YOVaHHY3U0AACAk9IvVwvJzs6O2msZhqHy8nIVFRUdcZ/HHntMs2bNitxPTU2N2vH7u5Bp6ql9++R1ODQzk4sfAACA+Nbr5frXv/61HnjgAe3evVtDhgzRXXfdpRtuuEGSdN1118k0TS1ZsiSyfyAQUH5+vn72s5/ppptukmma+tnPfqZHH31U1dXVGjFihH70ox/pC1/4whGPWVRUpAULFmjBggWRUjx37lxJUmFhoSoqKqL6OaalpSkvL++UXuPWF19UaWlplBLFj5tuukmFe/dqxYoVcjqdVscBAAA4Jb16QuPSpUt122236Tvf+Y4+/vhjfeMb39BNN92kV155RZI0b948LVu2TG1tbZHnvPjii2pvb9fVV18tSbrrrrv02GOP6de//rU2btyo22+/Xddff71effXV48rw3nsHLqjw2GOPqbq6OnI/mm699VZlZWVpypQpevTRRxUOh4+4r8/nU0tLS+Tj0M/9dLNr1y49/vjjeu2115Senq7k5GQlJydrzZo1VkcDAAA4Kb06cv3AAw9o/vz5uuWWWyRJ3/72t/X222/rgQce0AUXXKBLL71USUlJWrp0aWQ0+89//rPmzJmjlJQUtbe36xe/+IVefvllnXXWWZKk4uJivf766/rNb36j888//5gZPp0i0tPoss1mk812an9f/PSnP9WFF16o733ve3rzzTe1YcMG1dXV6a677upx/3vvvVeLFy8+pWP2F4WFhTJN0+oYAAAAUdOrI9dlZWU655xzum0755xzVFZWJklyOp265ppr9OSTT0qS2tvb9cwzz2jevHmSpE2bNqmrq0uXXHJJZFQzOTlZTzzxhHbs2HHK+T59vUNddtll3Y4lSSUlJYdt+9Rdd92ld955JzLP+rrrrtPPfvazIx7zjjvuUHNzc+Tj3XffPeXPoy8UFRXp7bff7rPjjR49ulfeZQAAAOhNvT7n2jCMbvdN0+y2bd68eTr//PNVW1urlStXKiEhQZdddpkkRaZXPPvssyooKOj2Om63+5SzpaamKhgMdtv2+9//Xp2HLAU3fPhw3X777frqV7/a42usW7dOv/jFL/Tee+8pPz9fI0aMUEtLi/bt26fc3NzD9ne73d2yf1rWH/7c55Rj8SoZvywr0xcGD9agpKTDHmvau1e/v/56vdjDY73hWodDz331q3pO0tLdu5Xldmt6Tk7k8Q1NTXq/vl43DR16Qq/7Sk2N1tTWyn7IOxZ3jhkTrdinZOG6dVZHAAAAp6hXy3Vpaalef/11feUrX4lse/PNN7uduHf22Wdr0KBB+utf/6rnn39e11xzjVwulyRp1KhRcrvdqqysPK4pIEfidDoV6uGqfz/96U8P2/bZEi9JWVlZGjZs2GHbOzo6dN111+nhhx+OTDkpLy9XQkLCEdf79vl88vl8kfun85zr3tLo9+vxHTt0+xFOEJ2YkaE5Awf2cSoAAHA66NVy/b3vfU/XXnutJk2apIsuukjLly/XP//5T/3rX/+K7GMYhr785S/r0Ucf1datWyMnO0qS1+vVd7/7Xd1+++0Kh8M699xz1dLSojfffFPJycm68cYbjytHUVGRVq1apXPOOUdut1vp6emSDkzRaG1t1cMPP3xSn9/nP/95ZWRkaOjQoZFpKk8++aS+/vWvH3FkvT/Mue4IBvVsVZV2trUpwW7XudnZOiMzU01+vx7ZulXfGzVKzk9Ghj+or9fmlhbNGzJEgXBYL1VXq6y5WYZh6MzMTJ17yGj0oT4dRa/3+bShsVGGYei12lpNSE/X5T38AQQAABALenXO9VVXXaWHHnpIP/vZzzR69Gj95je/0WOPPaYZM2Z022/evHnatGmTCgoKDpuj/dOf/lQ//vGPde+996q0tFSXXnqpli9friFDhhx3jp///OdauXKlBg0apIkTJ0a2V1dXa8+ePd32veeeew6bX33nnXd227ZmzRotW7ZM69evV2trq8466yyNGzdOkvSlL31JP//5z4+YJV7nXB/quaoq2QxDt5eW6ouFhVpVU6Nd7e1Kc7mUk5CgrS0tkX03NjdrzCej+C9VV8sXCumbJSX62rBhWtfYqC2H7NuTCRkZGpuerhm5ubpzzJioFOuPm5p038aNenTrVm1qbj7l1wMAAPiUYbJcQzcNDQ1qaGiI3J83b56uvvpqff7zn49sKygo0B133KH/9//+X7fVRkKhkGw2m6ZPn67Vq1cf1/HKyso0atQobdq0yfJ1rouKirRkyRJNmzbtiI9NmTJFHo9HO3bs0KBBgyQd+IOhpaVF//M//6OHHnpIb7zxhv72t7+prq5OQ4YMUVVVlbxer5KSklRRUaGcT0arH374Yb333nv64x//eNjx0tPT1dTUpLfeekuPPvqoRo4cqR/+8IeRx5csWaJHH320x69zRUWFZsyY0eN65ps2bVJGRoaysrL08ssv65prrtGqVas0efLkk/yq9Y7bF0+xOgLi1C8XHjwRePziq6wLAuCErVv4dOT2sMvi+13u/sbfVqvdrz9yXH2tX16h8VRkZGQoIyMjct/j8SgnJ+ewOdc//OEPDzvJcezYsfrlL3+pOXPm9ElWK+zfv1+hUEgDD5mzXFhYqBdffFGSdM011+iuu+5SR0eH/vGPf+jiiy9WSkqKamtr1dnZqREjRkg6MF/dbrcf9i6GJFVVVam9vb3b/4djef3113XFFVdIOnAibFtbW7d5701NTZIOzOP/1MyZM3Xddddp2bJlMVeuAQBAfKJcn6S8vLwer8o4ePDgE5qy8qmHr7nG8tVCmqqr9fubbtKLn1lu8NDHCpKSJNPUdyZMUOonJ57+a88e+UIhLZ5yYLQ1U9L8sWP1/v79mpSdrcVTpihsmnIYhv5jyBAlOBxaWl6uIq9XE+vqIs/71N937JDTNOVradHvb7pJ6/bv155//Uu+f/yj234XSN2ee/vw4ZKkRp9Pj2/ZErmvz+x3qA927VKiw6HFzz9/wl+vaFvI0oMAAMS9Xp1zjf7HZhgqTUvTqqoqBcJh7evs1Ed1dRpzyCjz6IwMvVtbq6r2dpV8sv63zTA0PitLL+3Zo85gUKZpqtXv157PrJZS3tKijmAwckKkJCU5nWry+6OSf3NTk7qCQYVNUztbWrShoUHDP8kIAABwqhi5PobjnTst6bS52uDswYP1bGWlfrFundx2uy4oKFCh1xt5fFR6up6vrFRperpcdntk+6UDB2pVVZV+vXGj2oJBpTidGnDIutkh09SLu3drbnGx/rxtW2T7xKws/W3HDt370Ucan5mp2YMHa319vdbX1+v6T6aZHK8N9fV6urxcYdNUmtutOYWFGtTDSD0AAMDJoFwj4vZPVjw51mNJTqeuPcrFW5KdTi3sYQ7z33fuVOUhI9VtwaD+vnOnJOncvDw5bTYN9nqV6/Ho9nHj9Mv16yVJWQkJumX06G6vNS4zU+MyM3s8frrbfcTP5ZoTvOgMAADAiaBco8/MO2QOdGTOdVZWZNtftm/XrtZWbfxktZaOYFB/3rZNMwcN6rYfAABArKJcI2ZcVVSk4CFTa363aZOuKCxU0SFTTgAAAGIZ5Roxw+Po/u1oGIY8Dke3edsAAACxjHINS8w9juUKjzYHHAAAIBaxFB8AAAAQJZRrAAAAIEqYFgIgbvxrWYV2bm1W0B+SN82tcy4qUHFJmtWxAACIoFwDiBuTzsrVjNmD5XDYVFPVrn8+sVX/tmCsEjz8KAMAxAamhQCIGxnZHjkcB35sGZJCwbDaWwPWhgIA4BAM9wCIKy+v2KWNa+sUCpoqLklTZo7H6kgAAERQrgHElQuvKNSM2YO1p7xVdbUdVscBAKAbpoUAiDs2m6HBQ1O0e2erKrY3Wx0HAIAIyjWAuGWappobfFbHAAAggnINIC74fSFtXl8vvy+kcMjUtk2N2l3RqoLCZKujAQAQwZxrWCYYDmvFrl3a0dIifzisPI9HswcPVm5iotXREIMMQ/r4wzq9/GylJCktw63Lri5WVi7fLwCA2EG5hmXCpql0t1tfLS2V1+nU2/v2acmOHbpt7FiroyEGOV12fWF+idUxAAA4KqaFwDIuu13nDxigVJdLNsPQ1JwcNfp86ggGrY4GAABwUijXiBl72tuV7HQq0cEbKgAAID5RrhETuoJBLd+1SxcWFFgdBQAA4KRRrmG5QDisJTt2aERqqiZlZVkdBwAA4KRRrmGpkGnqqZ075XU6NXPgQKvjAAAAnBImt8JSyysqFAyHdW1xsQzDsDoO4kQ4ZOq5p3aoek+72lsD+tp3xivJ67Q6FgAAjFyfit/+9reaMWOGUlJSZBiGmpqarI4UV5p8Pq2tr9eu1lbdt3at7v7wQ9394Yfa1dpqdTTEkBeXlmvjR3WHbS8o9OqKa4dakAgAgCNj5PoYZsyYofnz52v+/PmHPdbR0aFZs2Zp1qxZuuOOO/o+XJxLc7u1aPJkq2MgDtnshiZOy7U6BgAAh6Fcn4IFCxZIklavXm1pDgDoybqFT1sdAQBOO0wLAQAAAKKEkes+5vP55PP5Ivfb2tosTAPEpmee3KaqygP/NoKBsLZtbNSrL+yWJE05N09TpudbGS9uTFl8tdURAJyA9xb+w+oIiALK9Wfcc889uueeeyL3Ozs79fbbb+vWW2+NbHv++ec1ffr0k3r9e++9V4sXLz7lnEB/9rl5wyO3X1xaroFFXo2eyBroAIDYR7n+jJtvvlnXXntt5P68efN09dVX6/Of/3xkW8EpXEXwjjvu0Le//e3I/S1btmjq1Kkn/XrA6SoYDEvmgduhUFjBQFgOJzPdAADWolx/RkZGhjIyMiL3PR6PcnJyNGzYsKi8vtvtltvtjtxPTk6Oyuv2ByHT1FM7dmhPe7taAwF9Z/x4eZ2sXYye/fFXH6u12S9J+sODGyRJCxax+gwAwFqU61NQU1Ojmpoabd++XZK0YcMGeb1eDR48uFtBx+GWlperyOvVxM9c7rzQ69XZeXn6382bLUqGWHPp3CE9bv/328f1cRIAAI6N91BPwaOPPqqJEyfqa1/7miTpvPPO08SJE7Vs2TKLk8Unu2FoWm6uBjGaDwAA4hQj18dwtDWsFy1apEWLFvVZFgAAAMQ2Rq4BAACAKGHkGn3myW3bVPnJut6BcFgbGxv1wu4Daxefm5en6fmsXQwAAOIb5Rp9Zt7wg2sXH+mERgAAgHjGtBDElGA4rEA4LEkKHXIbAAAgHjByjZjyq48/VrP/wNrFD244sHbxosmsXQwAAOID5RqWmDuk57WLbx/H2sUAACB+MS0EAAAAiBLKNQAAABAllGsAAAAgSijXAAAAQJRQrgEAAIAooVwDAAAAUUK5BgAAAKKEda4BAIgDVa9sUuPGKnXVtar46inKGDvI6kjAUYX87ard8LQ668vlSEhV1ujLlZhZbHWsXsfINQAAcSAhI1mDLhunpIJ0q6MAx2X/pmdld3tVdNH3lVlysfat/ZtCgU6rY/U6yjUAAHEgc/xgpQ7NleGwWx0FOKZw0Kf2fVsU7GhUxar/Vv2Wf8nhTlFH7Raro/U6yjUAAACiKtDRIBmSIzE9MnLt76hXV0u11dF6HeUaAAAAURXytUvhkDKGXSCb3aWk3FLZXUkKtNZaHa3XUa4BAAAQVZ/OrXYkeCPbbI4EhUN+qyL1Gco1AAAAosrm9EiSgr7WyLZwsEuG3WVVpD5DuQYAIA6EQ2GFAyHJNGWGTYUDIZlh0+pYQI8c7iTJsKtx22qFQwG1125WyN8uV3KO1dF6HetcAwAQB3Yt+1D16yolSW2V9Spf+r5G3DhdKUOyLU4GHM6ZmCGZpvwdDapYdb8cCSlyJmYqITXf6mi9jnINAEAcGDJ3sobMnWx1DOC42BxuJeWOlN3pUf4ZX1Zn/Q7VbnhaiTklVkfrdZRrAACAGLP9+YVWRzhl+/ffohtvvFGrV/9CAwcO1P89t1wXX3yx1bFOSllZmUaNeuS49qVcx4hb//53lZaWWh0DAAAgKrKzs/Xcc89ZHaPPcUIjAAAAECWMXMeIh7/8ZeUkJlodAxZa+MYbVkcAAACniHINxKBfLnzP6ggAgNNAUVGRlixZomnTpvXJ8UaPHq3HH39cU6ZM6ZPjWYFpIQAAxBkzFNb2v76tdT9/Tu8v+qcCrV1WR0IMKyoq0ttvv211DEnSxo0bI8V6/vz5uu+++7o9vmTJEs2YMeOEX3fRokVyOp1KTk6OfFiFkWsgBn1n8TlWR0Cc+vlCphf1N+VL35e3KFtZEwu7bfcWZinv7BHa/L+rrQmGXjXl6sVRe63q/U266Y7fKznjxRN6rLeVv79W/3p/j/7xni+yrWH3BtWWV/T4+fvaG7VlzeMaN+v2wx6r2rRa6YPGq3DinMi2aH4NO1tqj3tfyjUAAHHGsNuUO22Y1THQzwT9Hdq19lm11u6U3ZmgvBHnKnvIGfJ1NGnjvx7RhMu/J5vdKUnaX/6Bmqo3a/jZ8xQOBbR7w0tq2lsmyVDO0DOVX3Juj8f4aPl9CgW6lD/yPDXs3iAZhqo3v6bMwgkqnHB5H362vYdyDQAAAFWufU6GYdPYWbfL196gra8/oQRvlrxZhfKk5KipZqsyCkZLkhqrNipz8HhJ0u4NLykU8GnMzG+q4v1nVLvjHXlSspWW3/2CMf7OFoWDftmdHqXmDZe/o1kJ3izll0yPSv6GPR+rYc9GuRLTNGDkeUovGBWV1z1RzLkGAAA4zZlmWI1VmzRw9MWyO1xKTM1TVuEkNez5WJKUMXCMGvdslCQFfO1qa9ijtPwSmaap+l0fadDYmbI73LI5nPJmF6mxatNhx9i94UXZnQkybNGvnxkDR2vMJbdqwuXf08AxF6vig2fU3lgV9eMcD0auAQCIMduefFNtlfWSpHAgqMaNVdr9wnpJUt65I5Q/vf9fQhp9K+hrl2macnpSIttcialq2bdDkpReMEpVm15WKOhXU1WZUrKLZXcmKNDVpnAoqI9X/kqSFA4GJMMmb3aRAr52Vbz/tFrrymV3JsrpTpTN4VQ4FDzuXK11u7T9rT9LkkzTVDjo10fL7408PnHOHZIkT0pOZFtq7jBlDBqjpuotSkovOPkvykmiXAMAEGOGzzs7cvtIJzQC0eRwJ8kwDAU6W+RKTJUk+Tua5fR4JUkuT4oS0/LVXLNNDVUblV006ZPnJcqwOTR21u1yOBNU/v5SebOLlFU4UTve+ZucHq/GXfZdbXr5UXW1N8juTDjkqMZhOTIGjVXGoLGR+96swkiBPtoJjYc7/LX7CtNCAACIQ+FgSOFA6MDtUDhyGzgZhmFT2oBSVW1apXAooM7mfarb9ZEyBo6J7JNRMFq1O99Ve2OVUj+ZT20YNjndiVq34r/14bJ7VF+5Xrs+WqEPn7lbjVWb5HAlqr5yrdLyRigxNU/hUCDyes6EJPk7mqKSv2nvZgUDXTLNsFpqd6ph9wal5g2PymufKEauAQCIQx//aqX8zR2SpA0PviBJmrzo81ZGQpwbPGG2Ktc+q3XP/0J2h1sFpRfIm3XwHZP0glGqXP+80geUyu5wRbaPvuRWVW1cpca9ZTJDQTkTvMobfrb2bl6tgWMu1va3/qLWul0yw6FIud725p+VN+wsNdds00fL71Xm4PEaPH626ivXq373eo045/oTyl6/Z4PKP3haphmWOzFNhZPmKDljUHS+MCeIcg0AQAwbMndyj9vH3T6rj5MgXh1tGsWhjzndSRp65rVH3NeZkKzJcxcett3ucGnw+Ms0ePxlkWkh7qQM2R1uSVLRGVfJDAe1d8vrCgd8atm/U4UTr5A3q0j5I8/r9lqZg8cpc/C4Ho/vTko/4ucydOo1R8zd1yjXAAAAiCq7w6VQ8MDFYRwujyTJME05Ew7M7Xa4PN1Gv/sTyjUAAACiYsjkuZKkUNCnUMCnQFernAkHTorsaK5RVtGk4zwhMX5xQiMAAACiyu5wKy2/RFWbViscCqhp72Z1ttQedmGZ/ohyDQAAgKgbPOFy+TubtXbF/dr98UsaeuY1kSki/RnTQgAAABB1TnfSCa/60R8wcg0AAABECSPXsEwwHNaKHTu0o7lZ/lBIeUlJmj1kiHKTkqyOBgAAcFIYuYZlwqap9IQEfXXsWP1g6lSVZGRoyebNVscCAAA4aZRrWMZlt+v8QYOU6nbLZhiampenRp9PHYHAsZ8MAAAQgyjXiBl7WluV7HQq0em0OgoAAMBJYc41YkJXMKjlO3fqwsGDrY4CAACiyAyHtOPdp9TesEeBrlaNn/2dyIVl+iNGrmG5QDisJZs3a0Ramibl5lodBwAAnKTy95eqbtdHh233ZhVq6LRrLUjU9xi5hqVCpqmntm6V1+XSzKIiq+MAAIAoM2x25Q6bZnWMPsPINSy1fPt2BcNhXTVsmAzDsDoOAADAKaFcn6SGhgZ985vfVElJiRITEzV48GB961vfUnNzs9XR4kZTV5fW7t+vXS0tuu/dd3X322/r7rff1q6WFqujAQAAnBSmhRzDjBkzNH/+fM2fP7/b9r1792rv3r164IEHNGrUKO3atUs333yz9u7dq6eeesqasHEmLSFBi84+2+oYAADgFGx780m11VdKksLBgBqrNmr3+hckSXkjzlV+yXQr4/U5yvVJGjNmjP7xj39E7g8dOlR33323rr/+egWDQTkcfGkBAED/N/zseZHb5e8vlTe7SFmFEy1MZC0aYBQ1NzcrJSXlqMXa5/PJ5/NF7re1tfVFNAAAAMuEQ0FJ5oHb4ZDCoYBs9v55XQvKdZTU19frpz/9qb7xjW8cdb97771Xixcv7qNUAAAA1vt45a/k7zhwXtqGFx6UJE3+/CLL8vQmwzRN0+oQseSee+7RPffcE7nf2dkpp9PZbTT6+eef1/TpB+cPtbS0aObMmUpPT9eyZcvkPMoVBj87cr1lyxZNnTpVt0yYoJzExCh/NognC994I3L7O4vPsTAJ4tnPF75x7J0AxLwpVzMQF0s6W2q18V+PaNOmTSotLT3qvoxcf8bNN9+sa689uMj5vHnzdPXVV+vzn/98ZFtBQUHkdmtrq2bNmqXk5GQtXbr0qMVaktxut9xud+R+cnJyFNMDAADEhoCvXRXvP63WunI5PakqnHC5UnKKrY7V6yjXn5GRkaGMjIzIfY/Ho5ycHA0bNuywfVtaWnTppZfK7XZr2bJlSkhI6Muo/U5dZ6derKjQntZWGZKGpqVpdnGxPJwcCpyUcxZ/0eoIAE7AGwv/anWEqKpc+6ycHq/GX/59tdTu0I53/qaxl94mh8tjdbRexTrXJ6m1tVUzZ85Ue3u7/vd//1ctLS2qqalRTU2NQqGQ1fFi3tJt2/RRbW23bV3BoEZnZuq2SZO04IwzFDJNvVRRYU1AAABw0kJBn5qqt2hA6QWyO1xKH1AqT0qOmqq3WB2t1zEkeJI++OADvfPOO5J02Kh2eXm5iriU9wkb6PVqoNcbuX9Gbq5epFwDAE5D7/1jodURTslHH32kS9/6vdY990Bk2ze/WafExETdf3/8fW5lZWUaNeqR49qXcn0Mq1ev7nH7jBkzxLmgvWt3a6uyPf37rSMAAPqjtrY2paSkdNuWkpKipqYmawL1Ico1YlJ1e7veqa7WTWPGWB0FAIA+d84X43u1kLbGau3aU93t89j54b9kszu1Ng4/t47m2mPv9AnKNfrMk2VlqmxpkSQFwmFtrK/XC+XlkqRzCwo0feBASVJjV5f+UlamK4cOZXlCAADikCc5Q6GAT/7OVrk8B6Z8djTVKKd4ksXJeh/lGn1m3iHrQi7dtk1FqamamJPTbZ9Wv19PbNqk8wYOVGlmZl9HBAAAUWB3upUxoES7N65W0YRZat63Qx0ttcoYUGJ1tF7HaiGIGV3BoP60aZPGZ2drcl6e1XEAAMApKJ50uXwdzXrvmftVse4ljTjrmn6/DJ/EyDViyOaGBu3r6FBDV5feqKqKbL9z2jQLUwEAgJPhTEjSqPOutzpGn6NcwxJzhw8/bNuEnBxN+Mw0EQAAgHjCtBAAAAAgSijXAAAAQJRQrgEAAIAooVwDAAAAUcIJjQDixsplO7Rza4MC/rC8aW5Nv2iwiksyrI4FADiKzpY6Vax7Ua31eyTDUFruUBVPmt1vl+WjXAOIG2ecla8LZg+Rw2FTTVWr/vHEJv37gjOU4OFHGQDEgm3vLlVqdpFyhkyMbAsGupQ5aLSGn3m1DJtN2999WhXrXtKwKZ+zMGnvYVoIgLiRkZ0oh+PTH1uGgsGw2lv9lmYCABydN3OgcoomyOFKkN3hUm7xGWprqDr2E+MUwz0A4sqqFTv08dr9CgXDGlqSocycRKsjAQBOQGv9bnlSsq2O0Wso1wDiykVXDNUFs4u1u7xZdbUdVscBAJyA9sZqVW97R2MuuMnqKL2Gcg0g7thshgqHpunDt6uVme1R0bB0qyMBQFS98deFVkc4bldccYVef/11SVJHR4fa9m3V/q2rJUk//OEP9cMf/lCSVF5ervPOO09//+ufddVVV1mU9uSUlZVp1KhHjmtfyjWAuGWappoauqyOAQCntRUrVkRuz58/XzNmzND8+fO77VNTU6NLLrlEP/rRj+KuWJ8oyjWAuOD3hbRjS4OGlmTI4bBp+5YG7a5o0fRLCq2OBgBRd8GXF1sd4aSUvb1Wb29u0h9f2hXZFvR36aNVjyl7UKn+srpaf1kdf59bW3Ptce9LuQYQFwxD+vjDfXr52Z0yJaVlJGj21cOVnZtkdTQAwFHU7dms9qZ96mxrUGXZG5Ht511zp4Wpeg/lGkBccLrsumb+GKtjAACOonTa3MO25RVPUF7xhL4PYxHWuQYAAACihHINAAAARAnlGgAAAIgS5lzHiFv//GeVlpZaHQMAAACngJFrAAAAIEoYuY4RD3/lK8pJTrY6Biy08JVXrI4QNxrqOvXqixWq3tMqw5AKh6bpwtnFSvDwIw0AYC1GrgHEtBeWbtPGj7ov3u/rCqpkdKb+/bZJ+uqCMxQOmXr1pQprAgIAcAjKNYC4kz/Qq1ETcuROcMjpsmvsGbmqqWqzOhYAAEwLARD/9u5uVVa2x+oYQK+qfOVj1W/co866Fo24epqyxg62OhKAHjByDSCu1Va366N3qnXm+YOsjgL0Kk+GV0Mum6DkggyrowA4CkauAcScpU+WaW9liyQpEAhr68Z6rX6hXJI05dwCTZ0+UJLU3Nilp/9SpplXDlVWTqJleYG+kD2+UJK057Uyi5MAOBrKNYCYM3fewTXfX1i6TYOKUjV6Yk63fdpb/XrqiU2adt5ADSvN7OuIAAD0iGkhAOKOryuof/xpk0aNz9a4yXlWxwEAIIKRawBxZ/vmBtXt61BzQ5fef6Mqsv2bd06zMBUAAJRrADFu1tzhh20bPSFHoyfk9LA3AADWolwDABAHwqGwFDYl01Q4HFY4EJJht8mwGVZHA3AIyjUAAHFgx7L3tX9dhSSppbJO25e+q9E3zlDqEN7FAWIJ5RoAgDgwfO5UDZ871eoYAI6B1UIAAACAKKFcAwAAAFFCuQYAAACihHINAAAARAknNAIAACDq/F3t2vzO02raVy53YqpGTL5c6XnFVsfqdZRrAADiTGddiypeXKfWPfWSYShtaK6KZ0+Sw+OyOhoQse39Z+X2eHXO57+vhpod2vjG33TmnNvkdHmsjtarmBYCAEAM27b0XdV+VN5tW7AroMzRgzTpttk6Y8HlMkNhVby0zqKEwOGCAZ/qqraoaOwFsjtcyh5YqqTUHNVXbbE6Wq+jXAMAEGe8AzOVM6FIjgSX7C6Hcs8oVltVg9WxgIjO1gY5nG65Pd7ItuS0PLU377cwVd9gWggsEwyHtWLrVu1oaJA/FFJecrJmDx+u3ORkq6MBQFxp3V0vT3aK1TGAiFDQL7vT3W2b3elWMNBlUaK+w8g1LBM2TaUnJOirkybpB+eeq5KsLC35+GOrYwFAXGmvblT1O9s06PxRVkcBIuwOl0IBX7dtoYBPdkf/Py+AkWtYxmW36/yiosj9qQUFemnHDnUEAkp0Oq0LBgAWK3tyjVoq6yRJ4UBI9Rt3q/yFtZKkgnNHauD0UklSV2Obyv7yuoZeOUWJOalWxQUO4/FmKBjwydfZGpka0tZUo/yhkyxO1vso14gZe1palOxyUawBnPZK502P3N629F2lFmUrZ+KQbvv4Wzu16YnXNPC8UcosLejriMBROZxuZRWUqGLDag2bNEuNNTvU3lyrzIISq6P1OqaFICZ0BYNavmWLLhwy5Ng7A8BpLtjl16Y/vabs8YXKmzzU6jhAj4ZPvly+jma98c/7tWPtSxp1zjX9fhk+iZFrxIBAKKQlH3+sEZmZmpSfb3UcAIh5DZv3qmNfs7oa2lX1xsGlzabd+XkLUwHduRKSNG7G9VbH6HOUa1gqFA7rqU2b5HW5NHMooy8A8FnD5049bFvOhCLlTCjq+zAAjolpIbDU8q1bFQyHddXIkTIMw+o4AAAAp4RyfQoWLVqkkSNHKikpSenp6br44ov1zjvvWB0rbjR1dWltTY12NTfrvtdf192vvaa7X3tNu5qarI4GAABwUpgWcgpGjBihhx9+WMXFxers7NQvf/lLzZw5U9u3b1d2drbV8WJeWkKCFs2YYXUMAACAqGHk+hR8+ctf1sUXX6zi4mKNHj1av/jFL9TS0qL169dbHQ0AAAAWYOQ6Svx+v377298qNTVV48ePP+J+Pp9PPt/BKxa1tbX1RTwAAIA+VbXtPe0ue0MBX4ccrgQNGDZFhaOnH/uJcY5yfYpWrFihL33pS+ro6FB+fr5WrlyprKysI+5/7733avHixX2YEAAAoO9l5A9TTuEYOV0e+Tpbte6VJ5ScnqfMAcOtjtarmBZynJ588kklJydHPtasWSNJuuCCC7R27Vq9+eabmjVrlq699lrV1tYe8XXuuOMONTc3Rz7efffdvvoUAAAA+ownOb3bRWMMGepqb7QwUd9g5Po4XXnllTrzzDMj9wsKDlxqNikpScOGDdOwYcM0bdo0DR8+XP/7v/+rO+64o8fXcbvdcrvdkfvJycm9GxwAAMAi+yrWa+t7KxQK+uVJzlD2oFFWR+p1lOvj5PV65fV6j7mfaZrd5lTj2N6rqtIbu3erIxBQgsOhKQMGaHphodWxAADAKcotGqfconFqa9qnut1lstmdVkfqdZTrk9Te3q67775bV155pfLz81VfX69HHnlEe/bs0TXXXGN1vLgyLCNDY3Jy5HE61erz6Yl165SXnKzhmZlWRwMAAMdpX8V6bXlvuaQDpbpkypzIY8lpuWqo3q7KjWtUPOFiqyL2Ccr1SbLb7dq8ebP++Mc/qq6uTpmZmZoyZYrWrFmj0aNHWx0vrqR7PN3uG4ahxq4ui9IAAICT8eko9RGZpjrbGvoukEUo1ycpISFB//znP62O0W+s37dPK7ZulT8UUobHo1FchAcAgLhWU75OGflD5XQnqa2pRlXb3tPgUedaHavXUa4RE8bl5mpcbq72tbWprK5OThsL2QAAEM9a66u046OXFAr55UpI1oBhZ2jAsMlWx+p1lGv0ufX79mn5li2SDpTqOSUlkcdyk5O1vaFBayordXFxsVURAQDAKRo+ebaGT55tdYw+R7lGn/t0lPpITEkNnZ19FwgAACBKeO8dlltXU6M2v1+maaq6tVXvVVVpSFqa1bEAAABOGCPXsFxVa6te2rFD/lBIyS6XzhgwQJMHDLA6FgAAwAmjXMNys4cP1+zhw62OAQAAcMqYFgIAAABECeUaAAAAiBKmhQCIGyuX7dDOrQ0K+MPyprk1/aLBKi7JsDoWAAARlGsAceOMs/J1wewhcjhsqqlq1T+e2KR/X3CGEjz8KAMAxAamhQCIGxnZiXI4Pv2xZSgYDKu91W9pJgAADsVwD4C4smrFDn28dr9CwbCGlmQoMyfR6kgAAERQrgHElYuuGKoLZhdrd3mz6mo7rI4D9JnKVz5W/cY96qxr0Yirpylr7GCrIwHoAdNCAMQdm81Q4dA0Ve5sVsX2RqvjAH3Ck+HVkMsmKLmAk3iBWMbINRCDfr7wDasjxIWyd2brorOu0C233GJ1FKDXZY8vlCTtea3M4iQAjoaRawBxoa2tTU8++aTa2toUDAb1j3/8Q6+88oqmT59udTQAACIYuQZi0HcXX2B1hJgT8Ie09M8b9O//fqNMSWkZHs383FD98Z/fkv5pdbrY8cDCV6yOAACnNco1gLjgdNl17fwJVscAAOComBYCAAAARAkj1wAAxIFwKCyFTck0FQ6HFQ6EZNhtMmyG1dEAHIJyDQBAHNix7H3tX1chSWqprNP2pe9q9I0zlDokx9pgALqhXANAP/XGwr9aHQHRtNDqAACOB3OuAQAAgChh5BpA3Fj3XpXee2O3OjsCcic4NH7KAJ05vdDqWDHrgsVftjoCgBPwysI/Wx0BUUC5BhA3ioZlqGRMjhI8TrW1/v/t3V9IVHkfx/HPJI0Vzgy4MS5hpdCDFGaQVkxUYNCAkCXhVRpTCBZoEBIEddGlkUEEpSVB8Ugy0VNWBkVSOPaHmIxcIxahUIwnY6wl/8ymPk5nL5ZtU8t4YHZ+x3q/7s5xLt4XZzhff/zOmVH959+/yPtzijL/9ZPpNAAAJDFcGxeLxSRJv334YLgEpv36698/afwuMmywxN7G/xdTdGhUv/8+rtj4R/331aDcnmTTWbbx+XU0HBkwWALg/zXh+zsQMViCyT4M/Sbp77ltOg7Lsqx/Oghfd+PGDRUWFprOAAAAwDc0Nzdr8+bN036G4dqwsbEx3b59WxkZGUpKSjKdY8Tw8LBWr16tcDislJQU0zmwub+ul8bGRj148EC7du3SvHnzTGcBCREMBnX27Fm9fv1aXq9X27dvV3l5ueks4Iu+p/t7LBZTT0+P/H6/nE7ntJ9luIZxg4OD8ng8GhgYkNvtNp0DG7lw4YJ2794tSSotLdXp06cnXC/19fV69+6dqqurDZcCidHT06NZs2Zp8eLF6urq0rZt21RTU6OCggLTacAUP+r9nT3XAGyrpKREJSUlX/37x48f9fLlywQWAWZlZGRocHDw07HD4VB3d7fBIgCT8Z5rADNGQ0OD+vv7JUmdnZ06deqU8vPzDVcBiXXp0iVJUlZWlkZGRlRcXGy4CMDnWLmGccnJyTp8+LCSk3njA6YXDoe1f/9+zZ49W4FAQOXl5dqzZ4/pLCChSktL9erVK23ZskXNzc08cwDb+lHv7+y5BgDApr703MHnjh07xnMHgM2wcg0AgE3x3AEw87DnGgCAGaKhoUGRSESWZamjo4PnDgAbYrgGAGCGCIfDWr58uVwul4qLi3nuALAh9lwDAAAAccLKNYyorq7WqlWr5HK55PV6VVRUpK6uLtNZsKm6ujrl5OTI7XbL7XbL5/Pp5s2bprOAhGpra1NhYaEWLFggh8Ohq1evmk4CplVbW6vMzEzNmTNHubm5unfvnumkhGC4hhGhUEgVFRV69OiRWlpaND4+Lr/fr2g0ajoNNpSenq4jR46ovb1d7e3t2rhxo7Zu3arnz5+bTgMSJhqNasWKFTp58qTpFOCbLl68qH379unQoUN6+vSp1q9fr4KCAvX29ppO+8exLQS20N/fL6/Xq1AopA0bNpjOwQyQmpqqmpoalZWVmU4BEs7hcKipqUlFRUWmU4AvWrNmjVauXKm6urpP55YuXaqioqLv/tWRrFzDFgYGBiT9OTAB04nFYgoGg4pGo/L5fKZzAACTjI2N6cmTJ/L7/RPO+/1+PXz40FBV4vCeaxhnWZaqqqq0bt06ZWdnm86BTT179kw+n08jIyNKSUlRU1OTli1bZjoLADDJ27dvFYvFlJaWNuF8Wlqa3rx5Y6gqcRiuYVxlZaU6Ozt1//590ymwsaysLHV0dOj9+/e6fPmyAoGAQqEQAzYA2JTD4ZhwbFnWlHPfI4ZrGLV3715dv35dbW1tSk9PN50DG3M6nVqyZIkkKS8vT48fP9aJEyd05swZw2UAgM/Nnz9fSUlJU1apI5HIlNXs7xF7rmGEZVmqrKzUlStXdPfuXWVmZppOwgxjWZZGR0dNZwAAJnE6ncrNzVVLS8uE8y0tLVq7dq2hqsRh5RpGVFRUqLGxUdeuXZPL5fr0363H49HcuXMN18FuDh48qIKCAi1cuFBDQ0MKBoNqbW3VrVu3TKcBCTM8PKwXL158Ou7u7lZHR4dSU1O1aNEig2XAVFVVVdqxY4fy8vLk8/lUX1+v3t7eH+IXRXkVH4z42p6rc+fOaefOnYmNge2VlZXpzp076uvrk8fjUU5Ojg4cOKBNmzaZTgMSprW1Vfn5+VPOBwIBnT9/PvFBwDfU1tbq6NGj6uvrU3Z2to4fP/5DvG6X4RoAAACIE/ZcAwAAAHHCcA0AAADECcM1AAAAECcM1wAAAECcMFwDAAAAccJwDQAAAMQJwzUAAAAQJwzXAAAAQJwwXAMAAABxwnANAAAAxAnDNQAAABAnDNcAAABAnPwB6JzE5KmgENUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of cluster along the x-axis and loving or hating McDonald's along the y-axis\n",
    "fig = plt.figure(figsize = (8, 8))\n",
    "ax = fig.gca()\n",
    "mosaic(data_clust, ['cluster', 'Like'], ax= ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "48f5dc9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender distribution across cluster\n",
    "fig = plt.figure(figsize = (5, 4))\n",
    "ax = fig.gca()\n",
    "mosaic(data_clust, ['cluster', 'Gender'], ax = ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c1e14109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of cluster along the x-axis and age along the y-axis\n",
    "plt.figure(figsize = (5,4))\n",
    "sns.boxplot(data_clust, x= 'cluster', y = 'Age', palette = 'viridis', width = 0.7)\n",
    "plt.xlabel(\"segment number\")\n",
    "plt.ylabel(\"age\")\n",
    "plt.title(\"Parallel box and whisker plot of age by segment\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ba940a",
   "metadata": {},
   "source": [
    "# Step 8 Selecting the Target Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "875146d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_segment = data_clust[data_clust.columns[-5:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bf7f67bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_like = {'I hate it!-5': -5, '-4': -4, '-3': -3, '-2': -2, '-1': -1, '0': 0, '+1': 1, '+2': 2, '+3': 3, '+4': 4, 'I love it!+5': 5}\n",
    "mask_gender = {'Female':0, 'Male':1}\n",
    "mask_VisitFrequency = {'Never': 0, 'Once a year':1, 'Every three months':2, 'Once a month':3, 'Once a week': 4, 'More than once a week': 5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "038cceda",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_segment['Like'] = data_segment['Like'].map(mask_like)\n",
    "data_segment['Gender'] = data_segment['Gender'].map(mask_gender)\n",
    "data_segment['VisitFrequency'] = data_segment['VisitFrequency'].map(mask_VisitFrequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5d73d6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_segment_mean = data_segment.groupby('cluster').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "471c4d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 4))\n",
    "size = data_segment_mean['Gender'] * 500\n",
    "sns.scatterplot(data_segment_mean, x = 'VisitFrequency', y = 'Like', s=size, hue = 'cluster', palette = 'viridis')\n",
    "plt.xlim(1, 4)\n",
    "plt.ylim(-3, 3)\n",
    "plt.title(\"Segment Evaluation Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee0266c",
   "metadata": {},
   "source": [
    "Above figure represents a simplified segment evaluation plot. Market segments 1 and 3 are located in the attractive quadrant of the segment evaluation plot. Members of these two segments like McDonald's and visit it frequently. These segments need to be retained, and their needs must be satisfied in the future. Market segment 0 is located in the least attractive position. Members of this segment hate McDonald's and rarely eat there, making them unattractive as a potential market segment. "
   ]
  }
 ],
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