{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c26e80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies and packages needed from sklearn import datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import datasets\n",
    "# Load iris dataset\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cead1610",
   "metadata": {},
   "source": [
    "<h1>Iris Dataset</h1>\n",
    "<p>The iris dataset was created by R. A. Fisher, UCI.</p>\n",
    "<p>https://archive.ics.uci.edu/ml/datasets/iris</p>\n",
    "<p>The dataset is good for pattern recognition and the datapoints represent three classifications of iris with features giving sepal and petal width and length</p>"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "fb500fcf",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "750e8acd",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86487c42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
     ]
    }
   ],
   "source": [
    "# Let's print the description of the database from sklearn\n",
    "print(iris.DESCR)\n",
    "# and also the keys (labels)\n",
    "print(iris.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9bc249e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
      "0                  5.1               3.5                1.4               0.2   \n",
      "1                  4.9               3.0                1.4               0.2   \n",
      "2                  4.7               3.2                1.3               0.2   \n",
      "3                  4.6               3.1                1.5               0.2   \n",
      "4                  5.0               3.6                1.4               0.2   \n",
      "..                 ...               ...                ...               ...   \n",
      "145                6.7               3.0                5.2               2.3   \n",
      "146                6.3               2.5                5.0               1.9   \n",
      "147                6.5               3.0                5.2               2.0   \n",
      "148                6.2               3.4                5.4               2.3   \n",
      "149                5.9               3.0                5.1               1.8   \n",
      "\n",
      "     target  \n",
      "0         0  \n",
      "1         0  \n",
      "2         0  \n",
      "3         0  \n",
      "4         0  \n",
      "..      ...  \n",
      "145       2  \n",
      "146       2  \n",
      "147       2  \n",
      "148       2  \n",
      "149       2  \n",
      "\n",
      "[150 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Convert keys to Pandas dataframe and create a label target column\n",
    "df = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
    "df['target'] = iris.target\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b8e7224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto',\n",
       " 'copy_x': True,\n",
       " 'init': 'k-means++',\n",
       " 'max_iter': 300,\n",
       " 'n_clusters': 8,\n",
       " 'n_init': 10,\n",
       " 'random_state': None,\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this step is just to demonstrate the adjustable parameters within the algorithm and not a necessary step\n",
    "KMeans().get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d1c460",
   "metadata": {},
   "source": [
    "<h3> Also check the SKLearn website for parameter definitions</h3> \n",
    "<p>https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b559099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store features\n",
    "X = iris.data\n",
    "# Store target (labels)\n",
    "y = iris.target\n",
    "# split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "# view feature data\n",
    "X[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "572da6e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization complete\n",
      "Iteration 0, inertia 77.4\n",
      "Iteration 1, inertia 55.901427353793515\n",
      "Iteration 2, inertia 49.519968280959745\n",
      "Iteration 3, inertia 46.21427132823399\n",
      "Iteration 4, inertia 45.04963695686674\n",
      "Iteration 5, inertia 44.745108543948845\n",
      "Iteration 6, inertia 44.691315814943856\n",
      "Converged at iteration 6: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 67.18999999999998\n",
      "Iteration 1, inertia 44.69947713284963\n",
      "Iteration 2, inertia 44.44227937915742\n",
      "Converged at iteration 2: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 79.07\n",
      "Iteration 1, inertia 47.93996620388689\n",
      "Iteration 2, inertia 45.3710723593627\n",
      "Iteration 3, inertia 44.745108543948845\n",
      "Iteration 4, inertia 44.691315814943856\n",
      "Converged at iteration 4: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 58.68999999999999\n",
      "Iteration 1, inertia 44.06187533875338\n",
      "Converged at iteration 1: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 128.23000000000002\n",
      "Iteration 1, inertia 62.09425055602092\n",
      "Iteration 2, inertia 58.829026187528264\n",
      "Iteration 3, inertia 58.27493482668533\n",
      "Iteration 4, inertia 58.05262304416716\n",
      "Converged at iteration 4: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 69.92999999999999\n",
      "Iteration 1, inertia 46.22236365370462\n",
      "Iteration 2, inertia 44.81999932098189\n",
      "Iteration 3, inertia 44.76695470383275\n",
      "Converged at iteration 3: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 92.55999999999999\n",
      "Iteration 1, inertia 44.45478855589208\n",
      "Iteration 2, inertia 44.194905519453116\n",
      "Iteration 3, inertia 44.11245470383275\n",
      "Converged at iteration 3: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 66.16000000000001\n",
      "Iteration 1, inertia 47.93756565277252\n",
      "Iteration 2, inertia 45.821986846689896\n",
      "Iteration 3, inertia 44.63639931406203\n",
      "Iteration 4, inertia 44.43483211475136\n",
      "Converged at iteration 4: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 71.25000000000001\n",
      "Iteration 1, inertia 45.81415676909893\n",
      "Iteration 2, inertia 45.04963695686674\n",
      "Iteration 3, inertia 44.745108543948845\n",
      "Iteration 4, inertia 44.691315814943856\n",
      "Converged at iteration 4: strict convergence.\n",
      "Initialization complete\n",
      "Iteration 0, inertia 91.86000000000001\n",
      "Iteration 1, inertia 47.607985681448\n",
      "Iteration 2, inertia 44.98920949004662\n",
      "Iteration 3, inertia 44.691315814943856\n",
      "Converged at iteration 3: strict convergence.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 3, 0, 3, 2, 2, 0, 3, 0, 2, 0, 2, 1, 0, 2, 2, 3, 1, 3, 1, 0, 1,\n",
       "       0, 3, 1, 1, 0, 3, 1, 1, 2, 0, 0, 3, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a model\n",
    "KMClust = KMeans(n_clusters = 4, n_init = 10, random_state = 42, verbose = 1)\n",
    "#Fit the training data to the model\n",
    "y_pred = KMClust.fit(X_train)\n",
    "#Predict the targets (labels) of test data\n",
    "KMClust.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38bc8689",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMeans(n_clusters=4, random_state=42, verbose=1)\n"
     ]
    }
   ],
   "source": [
    "# Show model\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ce0e291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.23055556 2.85       5.         1.76388889]\n",
      " [5.0195122  3.42195122 1.45853659 0.24634146]\n",
      " [7.23333333 3.17333333 6.09333333 2.14666667]\n",
      " [5.47       2.555      3.875      1.19      ]]\n"
     ]
    }
   ],
   "source": [
    "# Get the cluster centroids\n",
    "print(y_pred.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "651d70e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 1 3 3 1 2 1 3 0 0 0 0 3 1 1 2 0 2 0 1 2 0 1 1 0 0 0 2 0 0 3 1 1 0\n",
      " 1 0 1 0 0 0 1 1 0 2 1 3 0 0 3 1 0 3 3 1 1 0 3 3 2 1 2 1 3 2 1 2 3 0 0 0 0\n",
      " 0 0 0 0 3 1 3 0 0 1 3 1 1 1 1 3 2 1 1 2 2 1 3 1 1 2 3 1 1 0 2 1 3 0 1 1 1\n",
      " 0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the cluster labels\n",
    "print(y_pred.labels_)\n",
    "type(y_pred.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a4abb25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Three Clusters')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train[:, 0], X_train[:, 2], c = y_pred.labels_)\n",
    "plt.title(\"Three Clusters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c412e290",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}