Projects/EvaluatingTheEffectivenessOfAClusteringModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and read datasets from local disk\n",
    "\n",
    "https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  AnnualIncome  SpendingScore\n",
       "2    Male   21            15             81\n",
       "3  Female   20            16              6\n",
       "4  Female   23            16             77\n",
       "5  Female   31            17             40\n",
       "6  Female   22            17             76"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_data = pd.read_csv('datasets/Mall_Customers.csv', \n",
    "                        names = ['Gender', 'Age', 'AnnualIncome', 'SpendingScore'],\n",
    "                        index_col=0, header=1)\n",
    "\n",
    "mall_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mall_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AnnualIncome  SpendingScore\n",
       "2            15             81\n",
       "3            16              6\n",
       "4            16             77\n",
       "5            17             40\n",
       "6            17             76"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = mall_data[['AnnualIncome', 'SpendingScore']]\n",
    "\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 81],\n",
       "       [16,  6],\n",
       "       [16, 77],\n",
       "       [17, 40],\n",
       "       [17, 76],\n",
       "       [18,  6],\n",
       "       [18, 94],\n",
       "       [19,  3],\n",
       "       [19, 72],\n",
       "       [19, 14]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array(features)\n",
    "\n",
    "X[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check, how many centroid are good for this cluster\n",
    "\n",
    "https://medium.com/@ODSC/unsupervised-learning-evaluating-clusters-bd47eed175ce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "possible_k_values = range(2, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[181023.87669294522,\n",
       " 105373.18846928165,\n",
       " 72749.98793276899,\n",
       " 43972.8507048507,\n",
       " 36758.209767627006,\n",
       " 29817.147793410295,\n",
       " 24554.994595259814,\n",
       " 21567.66742728002,\n",
       " 19403.13070882606,\n",
       " 17368.070699267002,\n",
       " 15611.499603695904,\n",
       " 14392.26608485184,\n",
       " 12757.341774891775,\n",
       " 11456.410529163817,\n",
       " 10749.877308027593,\n",
       " 10067.808712601742,\n",
       " 9362.947036921101,\n",
       " 8355.368304159609,\n",
       " 7959.968831168831,\n",
       " 7503.517752737458]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_of_squared_distances = []\n",
    "\n",
    "for k in possible_k_values:\n",
    "    k_means = KMeans(n_clusters=k)\n",
    "    k_means = k_means.fit(X)\n",
    "\n",
    "    sum_of_squared_distances.append(k_means.inertia_)\n",
    "    \n",
    "sum_of_squared_distances[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw graph between sum of square of distace vs number of cluster\n",
    "\n",
    "https://medium.com/@ODSC/unsupervised-learning-evaluating-clusters-bd47eed175ce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "plt.plot(possible_k_values, sum_of_squared_distances, 'rx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Sum of squared distances')\n",
    "\n",
    "plt.title('Elbow plot for optimal number of clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette score per k, As silhouette score are more as number of cluster are good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>k_value</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>181023.876693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>105373.188469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>72749.987933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>43972.850705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>36758.209768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>29817.147793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>24554.994595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>21567.667427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>19403.130709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>17368.070699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   k_value          score\n",
       "0        2  181023.876693\n",
       "1        3  105373.188469\n",
       "2        4   72749.987933\n",
       "3        5   43972.850705\n",
       "4        6   36758.209768\n",
       "5        7   29817.147793\n",
       "6        8   24554.994595\n",
       "7        9   21567.667427\n",
       "8       10   19403.130709\n",
       "9       11   17368.070699"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_df = pd.DataFrame({'k_value' : possible_k_values,\n",
    "                          'score' : sum_of_squared_distances})\n",
    "\n",
    "scores_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette Score\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html#examples-using-sklearn-metrics-silhouette-score\n",
    "\n",
    "__Compute the mean Silhouette Coefficient of all samples.__\n",
    "\n",
    "__The Silhouette Coefficient is calculated using the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample. The Silhouette Coefficient for a sample is (b - a) / max(a, b). To clarify, b is the distance between a sample and the nearest cluster that the sample is not a part of. Note that Silhouette Coefficient is only defined if number of labels is 2 <= n_labels <= n_samples - 1.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def silhouette_score_kmeans(k, data):\n",
    "    kmean = KMeans(n_clusters = k).fit(data)\n",
    "    \n",
    "    silhou_score = silhouette_score(data, kmean.labels_)\n",
    "\n",
    "    return silhou_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette score per k value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 0.2946643879293517),\n",
       " (3, 0.46824208542590284),\n",
       " (4, 0.4970171432340684),\n",
       " (5, 0.5552295017995841),\n",
       " (6, 0.5409873241007619),\n",
       " (7, 0.5293467529575666),\n",
       " (8, 0.45993203944280786),\n",
       " (9, 0.45660529927120114),\n",
       " (10, 0.4422380543958117),\n",
       " (11, 0.43554006706682713),\n",
       " (12, 0.44823612153002146),\n",
       " (13, 0.42619063949793334),\n",
       " (14, 0.42778109087295885),\n",
       " (15, 0.4241798357914702),\n",
       " (16, 0.4275947929142733),\n",
       " (17, 0.4104608516757805),\n",
       " (18, 0.41535053858998977),\n",
       " (19, 0.4122513323563501),\n",
       " (20, 0.42718235025048884),\n",
       " (21, 0.4230435288135018),\n",
       " (22, 0.4142540479536087),\n",
       " (23, 0.42776537303819734),\n",
       " (24, 0.41569409473775026),\n",
       " (25, 0.4199778846197309),\n",
       " (26, 0.41919156712470834),\n",
       " (27, 0.3986629052276026),\n",
       " (28, 0.40497196108650535),\n",
       " (29, 0.42381359263729906),\n",
       " (30, 0.41951777794956935),\n",
       " (31, 0.40361751707923366),\n",
       " (32, 0.44946474323662367),\n",
       " (33, 0.42386962736692446),\n",
       " (34, 0.43831164842682235),\n",
       " (35, 0.4340230757455018),\n",
       " (36, 0.4334337228155371),\n",
       " (37, 0.4089976290014462),\n",
       " (38, 0.45487565188664086),\n",
       " (39, 0.43763568419669246),\n",
       " (40, 0.44381450306508174),\n",
       " (41, 0.42643669506465604),\n",
       " (42, 0.43478862727742756),\n",
       " (43, 0.4261632659416222),\n",
       " (44, 0.41481630211313614),\n",
       " (45, 0.42749891313812644),\n",
       " (46, 0.4206650926717867),\n",
       " (47, 0.43976230759395607),\n",
       " (48, 0.42482526860297015),\n",
       " (49, 0.4228726161137603)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score_per_k = []\n",
    "\n",
    "for k in possible_k_values:\n",
    "    silhou_score = silhouette_score_kmeans(k, X)\n",
    "    \n",
    "    silhouette_score_per_k.append(silhou_score)\n",
    "    \n",
    "list(zip(possible_k_values,  silhouette_score_per_k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette score per k value in dataframe formate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>k_value</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.294664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.468242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.497017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0.555230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0.540987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>0.529347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.459932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>0.456605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>0.442238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>0.435540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   k_value     score\n",
       "0        2  0.294664\n",
       "1        3  0.468242\n",
       "2        4  0.497017\n",
       "3        5  0.555230\n",
       "4        6  0.540987\n",
       "5        7  0.529347\n",
       "6        8  0.459932\n",
       "7        9  0.456605\n",
       "8       10  0.442238\n",
       "9       11  0.435540"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_df = pd.DataFrame({'k_value' : possible_k_values,\n",
    "                          'score' : silhouette_score_per_k})\n",
    "\n",
    "scores_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>k_value</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>40</td>\n",
       "      <td>0.443815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>41</td>\n",
       "      <td>0.426437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>42</td>\n",
       "      <td>0.434789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>43</td>\n",
       "      <td>0.426163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>44</td>\n",
       "      <td>0.414816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>45</td>\n",
       "      <td>0.427499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>46</td>\n",
       "      <td>0.420665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>47</td>\n",
       "      <td>0.439762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>48</td>\n",
       "      <td>0.424825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>49</td>\n",
       "      <td>0.422873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k_value     score\n",
       "38       40  0.443815\n",
       "39       41  0.426437\n",
       "40       42  0.434789\n",
       "41       43  0.426163\n",
       "42       44  0.414816\n",
       "43       45  0.427499\n",
       "44       46  0.420665\n",
       "45       47  0.439762\n",
       "46       48  0.424825\n",
       "47       49  0.422873"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph between Silhouette score vs k_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "plt.plot(possible_k_values, silhouette_score_per_k, 'gx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Silhouette score')\n",
    "\n",
    "plt.title('Silhouette score per number of clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best k value and corresponding score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "k_value    5.00000\n",
       "score      0.55523\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_score_index = scores_df[scores_df['score'] == max(scores_df['score'])].index[0]\n",
    "\n",
    "scores_df.iloc[max_score_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_k_value = int(scores_df.iloc[max_score_index].k_value)\n",
    "\n",
    "best_k_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View dendrogram\n",
    "\n",
    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.dendrogram.html\n",
    "\n",
    "The dendrogram illustrates how each cluster is composed by drawing a U-shaped link between a non-singleton cluster and its children. Here we can see 5 cluster would be best for clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster import hierarchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "hierarchy.dendrogram(hierarchy.linkage(X, method='single'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "hierarchy.dendrogram(hierarchy.linkage(X, method='average'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "hierarchy.dendrogram(hierarchy.linkage(X, method='complete'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "hierarchy.dendrogram(hierarchy.linkage(X, method='ward'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "possible_k_values = range(2, 15)\n",
    "\n",
    "def silhouette_score_agglomerative(k, data):\n",
    "    agg = AgglomerativeClustering(n_clusters = k, linkage='ward').fit(data)\n",
    "    \n",
    "    silhou_score = silhouette_score(data, agg.labels_)\n",
    "\n",
    "    return silhou_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_score_per_k = []\n",
    "\n",
    "for k in possible_k_values:\n",
    "    silhou_score = silhouette_score_agglomerative(k, X)\n",
    "    \n",
    "    silhouette_score_per_k.append(silhou_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "plt.plot(possible_k_values, silhouette_score_per_k, 'gx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Silhouette score')\n",
    "\n",
    "plt.title('Silhouette score per number of clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MeanShift cluster algorithm\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.cluster.MeanShift.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import MeanShift\n",
    "\n",
    "possible_bw_values = range(1, 30)\n",
    "\n",
    "def silhouette_score_meanshift(bw, data):\n",
    "    meanshift = MeanShift(bandwidth=bw).fit(data)\n",
    "    \n",
    "    silhou_score = silhouette_score(data, meanshift.labels_)\n",
    "\n",
    "    return silhou_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_score_per_bw = []\n",
    "\n",
    "for bw in possible_bw_values:\n",
    "    silhou_score = silhouette_score_meanshift(bw, X)\n",
    "    \n",
    "    silhouette_score_per_bw.append(silhou_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "plt.plot(possible_bw_values, silhouette_score_per_bw, 'gx-')\n",
    "plt.xlabel('bandwidth')\n",
    "plt.ylabel('Silhouette score')\n",
    "\n",
    "plt.title('Silhouette score per bandwidth')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate Bandwidth method for MeanShift clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.322161642835603"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import estimate_bandwidth\n",
    "\n",
    "bandwidth = estimate_bandwidth(X, n_samples=15)\n",
    "\n",
    "bandwidth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import MeanShift\n",
    "\n",
    "meanshift = MeanShift(bandwidth=bandwidth).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54.95402299, 48.73563218],\n",
       "       [78.02325581, 76.58139535],\n",
       "       [80.86842105, 21.21052632]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids = meanshift.cluster_centers_\n",
    "\n",
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanshift_labels = meanshift.labels_\n",
    "\n",
    "meanshift_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4625960894352593"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhou_score = silhouette_score(X, meanshift_labels)\n",
    "\n",
    "silhou_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "colors = ['green', 'blue', 'purple', 'red', 'grey']\n",
    "\n",
    "plt.scatter(features['AnnualIncome'], features['SpendingScore'], \n",
    "            s=200, c=meanshift_labels,\n",
    "            cmap=matplotlib.colors.ListedColormap(colors), alpha=0.5)\n",
    "\n",
    "plt.scatter(centroids[:,0], centroids[:,1], c='k', s=250, marker='s')\n",
    "\n",
    "for i in range(len(centroids)):\n",
    "    plt.annotate( i, (centroids[i][0] + 7, centroids[i][1] + 7), fontsize=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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