unsupervised ML k-means on iris dataset LIVE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ef1385b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea88b778",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris_data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5ed238a",
   "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"
     ]
    }
   ],
   "source": [
    "print(iris_data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a810a06f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris_data.data)\n",
    "df.columns = iris_data.feature_names\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7c8ef94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3cc3fdea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  150 non-null    float64\n",
      " 1   sepal width (cm)   150 non-null    float64\n",
      " 2   petal length (cm)  150 non-null    float64\n",
      " 3   petal width (cm)   150 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 4.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "895d808a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)  \n",
       "count        150.000000  \n",
       "mean           1.199333  \n",
       "std            0.762238  \n",
       "min            0.100000  \n",
       "25%            0.300000  \n",
       "50%            1.300000  \n",
       "75%            1.800000  \n",
       "max            2.500000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "745bcf5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, markers='+')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0fd73f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5d2bbb8",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df.plot(kind='scatter', x=\"sepal length (cm)\", y=\"sepal width (cm)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6df88f1d",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "wss = []\n",
    "for i in range(1,10):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
    "    kmeans.fit(df)\n",
    "    wss.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(range(1,10),wss)\n",
    "plt.title('The elbow method')\n",
    "plt.xlabel('Number of clsuters')\n",
    "plt.ylabel('Sum of squared distances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2be00672",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For cluster: 2, the silhouette score is: 0.681046169211746\n",
      "For cluster: 3, the silhouette score is: 0.5528190123564091\n",
      "For cluster: 4, the silhouette score is: 0.49721766487109664\n",
      "For cluster: 5, the silhouette score is: 0.48874888709310454\n",
      "For cluster: 6, the silhouette score is: 0.36483400396700155\n",
      "For cluster: 7, the silhouette score is: 0.35552178566700177\n",
      "For cluster: 8, the silhouette score is: 0.3597454923845753\n",
      "For cluster: 9, the silhouette score is: 0.33626052168459114\n"
     ]
    }
   ],
   "source": [
    "for i in range(2,10):\n",
    "    kmeans = KMeans(n_clusters=i, max_iter=100)\n",
    "    kmeans.fit(df)\n",
    "    score = silhouette_score(df, kmeans.labels_)\n",
    "    print(\"For cluster: {}, the silhouette score is: {}\".format(i,score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7738957e",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "silouette_coeff = []\n",
    "\n",
    "for i in range(2,10):\n",
    "    kmeans = KMeans(n_clusters=i)\n",
    "    kmeans.fit(df)\n",
    "    score = silhouette_score(df, kmeans.labels_)\n",
    "    silouette_coeff.append(score)\n",
    "    \n",
    "plt.plot(range(2,10), silouette_coeff)\n",
    "plt.xticks(range(2,10))\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"silhouette coeff\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c3ae8e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43921737",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba00c01a",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3)\n",
    "y = kmeans.fit_predict(X)\n",
    "unique_labels = np.unique(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6efb10b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "for i in unique_labels:\n",
    "    plt.scatter(X[y==i, 0], X[y==i, 1], label=i)\n",
    "plt.title(\"Iris data clusters\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}