unsupervised_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iNhqCyh0W6lZ"
   },
   "source": [
    "#Simple K-means example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VztmK8rLdmYj"
   },
   "outputs": [],
   "source": [
    "# import the Libraries\n",
    "import numpy as np        \n",
    "import pandas as pd        \n",
    "import matplotlib.pyplot as plt    \n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "du8WHZwrExZv"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import (make_blobs,\n",
    "                                                make_circles,\n",
    "                                                make_moons)\n",
    "from sklearn.cluster import KMeans, SpectralClustering\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FhM8j2AEXOE1"
   },
   "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": [
    "#Code for generating some random data in a two-dimensional space:\n",
    "X= -2 * np.random.rand(100,2)\n",
    "X1 = 1 + 2 * np.random.rand(50,2)\n",
    "X[50:100, :] = X1\n",
    "plt.scatter(X[ : , 0], X[ :, 1], s = 50, c = 'b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sd1vGgNGXCss"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kmean = KMeans(n_clusters=2)\n",
    "Kmean.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ewxPjqvcXWuW"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.92800933, -1.01200158],\n",
       "       [ 1.98008674,  1.97121671]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the centroid\n",
    "Kmean.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IvmXKwvjXdSF"
   },
   "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": [
    "#Let’s display the cluster centroids (using green and red color).\n",
    "plt.scatter(X[ : , 0], X[ : , 1], s =50, c='b')\n",
    "plt.scatter(-0.94665068, -0.97138368, s=200, c='g', marker='s')\n",
    "plt.scatter(2.01559419, 2.02597093, s=200, c='r', marker='s')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9tHb5LcBP954"
   },
   "source": [
    "#K-means Clustering: Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0_jBGdk3QpuZ"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy.linalg import norm\n",
    "\n",
    "\n",
    "class Kmeans:\n",
    "    '''Implementing Kmeans algorithm.'''\n",
    "\n",
    "    def __init__(self, n_clusters, max_iter=100, random_state=123):\n",
    "        self.n_clusters = n_clusters\n",
    "        self.max_iter = max_iter\n",
    "        self.random_state = random_state\n",
    "\n",
    "    def initializ_centroids(self, X):\n",
    "        np.random.RandomState(self.random_state)\n",
    "        random_idx = np.random.permutation(X.shape[0])\n",
    "        centroids = X[random_idx[:self.n_clusters]]\n",
    "        return centroids\n",
    "\n",
    "    def compute_centroids(self, X, labels):\n",
    "        centroids = np.zeros((self.n_clusters, X.shape[1]))\n",
    "        for k in range(self.n_clusters):\n",
    "            centroids[k, :] = np.mean(X[labels == k, :], axis=0)\n",
    "        return centroids\n",
    "\n",
    "    def compute_distance(self, X, centroids):\n",
    "        distance = np.zeros((X.shape[0], self.n_clusters))\n",
    "        for k in range(self.n_clusters):\n",
    "            row_norm = norm(X - centroids[k, :], axis=1)\n",
    "            distance[:, k] = np.square(row_norm)\n",
    "        return distance\n",
    "\n",
    "    def find_closest_cluster(self, distance):\n",
    "        return np.argmin(distance, axis=1)\n",
    "\n",
    "    def compute_sse(self, X, labels, centroids):\n",
    "        distance = np.zeros(X.shape[0])\n",
    "        for k in range(self.n_clusters):\n",
    "            distance[labels == k] = norm(X[labels == k] - centroids[k], axis=1)\n",
    "        return np.sum(np.square(distance))\n",
    "    \n",
    "    def fit(self, X):\n",
    "        self.centroids = self.initializ_centroids(X)\n",
    "        for i in range(self.max_iter):\n",
    "            old_centroids = self.centroids\n",
    "            distance = self.compute_distance(X, old_centroids)\n",
    "            self.labels = self.find_closest_cluster(distance)\n",
    "            self.centroids = self.compute_centroids(X, self.labels)\n",
    "            if np.all(old_centroids == self.centroids):\n",
    "                break\n",
    "        self.error = self.compute_sse(X, self.labels, self.centroids)\n",
    "    \n",
    "    def predict(self, X):\n",
    "        distance = self.compute_distance(X, old_centroids)\n",
    "        return self.find_closest_cluster(distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wIbiAdVnWO4U"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "sns.set_context('notebook')\n",
    "plt.style.use('fivethirtyeight')\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vFkczFhxYtzB"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the data\n",
    "df = pd.read_csv('faithful.csv')\n",
    "\n",
    "# Plot the data\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(df.iloc[:, 1], df.iloc[:, 2])\n",
    "plt.xlabel('Eruption time in mins')\n",
    "plt.ylabel('Waiting time to next eruption')\n",
    "plt.title('Visualization of raw data');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uic551aFWS0M"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standardize the data\n",
    "X_std = StandardScaler().fit_transform(df)\n",
    "\n",
    "# Run local implementation of kmeans\n",
    "km = Kmeans(n_clusters=2, max_iter=100)\n",
    "km.fit(X_std)\n",
    "centroids = km.centroids\n",
    "\n",
    "# Plot the clustered data\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "plt.scatter(X_std[km.labels == 0, 0], X_std[km.labels == 0, 1],\n",
    "            c='green', label='cluster 1')\n",
    "plt.scatter(X_std[km.labels == 1, 0], X_std[km.labels == 1, 1],\n",
    "            c='blue', label='cluster 2')\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], marker='*', s=300,\n",
    "            c='r', label='centroid')\n",
    "plt.legend()\n",
    "plt.xlim([-2, 2])\n",
    "plt.ylim([-2, 2])\n",
    "plt.xlabel('Eruption time in mins')\n",
    "plt.ylabel('Waiting time to next eruption')\n",
    "plt.title('Visualization of clustered data', fontweight='bold')\n",
    "ax.set_aspect('equal');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "A54Layv5Wd_A"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_iter = 9\n",
    "fig, ax = plt.subplots(3, 3, figsize=(16, 16))\n",
    "ax = np.ravel(ax)\n",
    "centers = []\n",
    "for i in range(n_iter):\n",
    "    # Run local implementation of kmeans\n",
    "    km = Kmeans(n_clusters=2,\n",
    "                max_iter=3,\n",
    "                random_state=np.random.randint(0, 1000, size=1))\n",
    "    km.fit(X_std)\n",
    "    centroids = km.centroids\n",
    "    centers.append(centroids)\n",
    "    ax[i].scatter(X_std[km.labels == 0, 0], X_std[km.labels == 0, 1],\n",
    "                  c='green', label='cluster 1')\n",
    "    ax[i].scatter(X_std[km.labels == 1, 0], X_std[km.labels == 1, 1],\n",
    "                  c='blue', label='cluster 2')\n",
    "    ax[i].scatter(centroids[:, 0], centroids[:, 1],\n",
    "                  c='r', marker='*', s=300, label='centroid')\n",
    "    ax[i].set_xlim([-2, 2])\n",
    "    ax[i].set_ylim([-2, 2])\n",
    "    ax[i].legend(loc='lower right')\n",
    "    ax[i].set_title(f'{km.error:.4f}')\n",
    "    ax[i].set_aspect('equal')\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hrYgV7zMWiXi"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ipsYFyOMYTea"
   },
   "source": [
    "#K-means on Wholesale Retail Customer\n",
    "\n",
    "The aim of this problem is to segment the clients of a wholesale distributor based on their annual spending on diverse product categories, like milk, grocery, region, etc. So, let’s start coding!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wuWNf4DmYbF5"
   },
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the data and looking at the first five rows of the data\n",
    "data=pd.read_csv(\"Wholesale customers data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dp1HjHNbY7l3"
   },
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistics of the data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ej1dgT9wZGAe"
   },
   "source": [
    "Here, we see that there is a lot of variation in the magnitude of the data. Variables like Channel and Region have low magnitude whereas variables like Fresh, Milk, Grocery, etc. have a higher magnitude.\n",
    "\n",
    "Since K-Means is a distance-based algorithm, this difference of magnitude can create a problem. So let’s first bring all the variables to the same magnitude:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jZofnQ_zY-Fz"
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "      <td>4.400000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-2.452584e-16</td>\n",
       "      <td>-5.737834e-16</td>\n",
       "      <td>-2.422305e-17</td>\n",
       "      <td>-1.589638e-17</td>\n",
       "      <td>-6.030530e-17</td>\n",
       "      <td>1.135455e-17</td>\n",
       "      <td>-1.917658e-17</td>\n",
       "      <td>-8.276208e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "      <td>1.001138e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-6.902971e-01</td>\n",
       "      <td>-1.995342e+00</td>\n",
       "      <td>-9.496831e-01</td>\n",
       "      <td>-7.787951e-01</td>\n",
       "      <td>-8.373344e-01</td>\n",
       "      <td>-6.283430e-01</td>\n",
       "      <td>-6.044165e-01</td>\n",
       "      <td>-5.402644e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-6.902971e-01</td>\n",
       "      <td>-7.023369e-01</td>\n",
       "      <td>-7.023339e-01</td>\n",
       "      <td>-5.783063e-01</td>\n",
       "      <td>-6.108364e-01</td>\n",
       "      <td>-4.804306e-01</td>\n",
       "      <td>-5.511349e-01</td>\n",
       "      <td>-3.964005e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-6.902971e-01</td>\n",
       "      <td>5.906683e-01</td>\n",
       "      <td>-2.767602e-01</td>\n",
       "      <td>-2.942580e-01</td>\n",
       "      <td>-3.366684e-01</td>\n",
       "      <td>-3.188045e-01</td>\n",
       "      <td>-4.336004e-01</td>\n",
       "      <td>-1.985766e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.448652e+00</td>\n",
       "      <td>5.906683e-01</td>\n",
       "      <td>3.905226e-01</td>\n",
       "      <td>1.890921e-01</td>\n",
       "      <td>2.849105e-01</td>\n",
       "      <td>9.946441e-02</td>\n",
       "      <td>2.184822e-01</td>\n",
       "      <td>1.048598e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.448652e+00</td>\n",
       "      <td>5.906683e-01</td>\n",
       "      <td>7.927738e+00</td>\n",
       "      <td>9.183650e+00</td>\n",
       "      <td>8.936528e+00</td>\n",
       "      <td>1.191900e+01</td>\n",
       "      <td>7.967672e+00</td>\n",
       "      <td>1.647845e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0             1             2             3             4  \\\n",
       "count  4.400000e+02  4.400000e+02  4.400000e+02  4.400000e+02  4.400000e+02   \n",
       "mean  -2.452584e-16 -5.737834e-16 -2.422305e-17 -1.589638e-17 -6.030530e-17   \n",
       "std    1.001138e+00  1.001138e+00  1.001138e+00  1.001138e+00  1.001138e+00   \n",
       "min   -6.902971e-01 -1.995342e+00 -9.496831e-01 -7.787951e-01 -8.373344e-01   \n",
       "25%   -6.902971e-01 -7.023369e-01 -7.023339e-01 -5.783063e-01 -6.108364e-01   \n",
       "50%   -6.902971e-01  5.906683e-01 -2.767602e-01 -2.942580e-01 -3.366684e-01   \n",
       "75%    1.448652e+00  5.906683e-01  3.905226e-01  1.890921e-01  2.849105e-01   \n",
       "max    1.448652e+00  5.906683e-01  7.927738e+00  9.183650e+00  8.936528e+00   \n",
       "\n",
       "                  5             6             7  \n",
       "count  4.400000e+02  4.400000e+02  4.400000e+02  \n",
       "mean   1.135455e-17 -1.917658e-17 -8.276208e-17  \n",
       "std    1.001138e+00  1.001138e+00  1.001138e+00  \n",
       "min   -6.283430e-01 -6.044165e-01 -5.402644e-01  \n",
       "25%   -4.804306e-01 -5.511349e-01 -3.964005e-01  \n",
       "50%   -3.188045e-01 -4.336004e-01 -1.985766e-01  \n",
       "75%    9.946441e-02  2.184822e-01  1.048598e-01  \n",
       "max    1.191900e+01  7.967672e+00  1.647845e+01  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standardizing the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "data_scaled = scaler.fit_transform(data)\n",
    "\n",
    "# statistics of scaled data\n",
    "pd.DataFrame(data_scaled).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5h037np-ZBRT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining the kmeans function with initialization as k-means++\n",
    "kmeans = KMeans(n_clusters=2, init='k-means++')\n",
    "\n",
    "# fitting the k means algorithm on scaled data\n",
    "kmeans.fit(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wl86a6nlZRPI"
   },
   "outputs": [],
   "source": [
    "# k means using 5 clusters and k-means++ initialization\n",
    "kmeans = KMeans(n_jobs = -1, n_clusters = 5, init='k-means++')\n",
    "kmeans.fit(data_scaled)\n",
    "pred = kmeans.predict(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "49Sx8d-cbhUF"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    209\n",
       "3    126\n",
       "2     91\n",
       "0     10\n",
       "4      4\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(data_scaled)\n",
    "frame['cluster'] = pred\n",
    "frame['cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a20bUrAzbp9h"
   },
   "source": [
    "So, there are 234 data points belonging to cluster 4 (index 3), then 125 points in cluster 2 (index 1), and so on. This is how we can implement K-Means Clustering in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q4zkR444cJ3V"
   },
   "source": [
    "#K-means on random pricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6UCTcli6cJoS"
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'xclara.csv' does not exist: b'xclara.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-dfc9db3f3e83>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Importing the dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'xclara.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    683\u001b[0m         )\n\u001b[0;32m    684\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 685\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    687\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 895\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1134\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1135\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1136\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1137\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1915\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1916\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1917\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1918\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1919\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File b'xclara.csv' does not exist: b'xclara.csv'"
     ]
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "data = pd.read_csv('xclara.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xdV6vMOEcf0W"
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'V1'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'V1'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-5f5ddf0e7326>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Getting the values and plotting it\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'V1'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mf2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'V2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'V1'"
     ]
    }
   ],
   "source": [
    "# Getting the values and plotting it\n",
    "f1 = data['V1'].values\n",
    "f2 = data['V2'].values\n",
    "X = np.array(list(zip(f1, f2)))\n",
    "plt.scatter(f1, f2, c='black', s=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mU1NNdotciaB"
   },
   "outputs": [],
   "source": [
    "# Euclidean Distance Caculator\n",
    "def dist(a, b, ax=1):\n",
    "    return np.linalg.norm(a - b, axis=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lKLlin7JclNP"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Range cannot be empty (low >= high) unless no samples are taken",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-028ba95ce228>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# X coordinates of random centroids\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mC_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;31m# Y coordinates of random centroids\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mC_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.randint\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Range cannot be empty (low >= high) unless no samples are taken"
     ]
    }
   ],
   "source": [
    "# Number of clusters\n",
    "k = 3\n",
    "# X coordinates of random centroids\n",
    "C_x = np.random.randint(0, np.max(X)-20, size=k)\n",
    "# Y coordinates of random centroids\n",
    "C_y = np.random.randint(0, np.max(X)-20, size=k)\n",
    "C = np.array(list(zip(C_x, C_y)), dtype=np.float32)\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nQni4_wHcnB2"
   },
   "outputs": [],
   "source": [
    "# Plotting along with the Centroids\n",
    "plt.scatter(f1, f2, c='#050505', s=7)\n",
    "plt.scatter(C_x, C_y, marker='*', s=200, c='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vrpE2FV3criS"
   },
   "outputs": [],
   "source": [
    "# To store the value of centroids when it updates\n",
    "from copy import deepcopy\n",
    "C_old = np.zeros(C.shape)\n",
    "# Cluster Lables(0, 1, 2)\n",
    "clusters = np.zeros(len(X))\n",
    "# Error func. - Distance between new centroids and old centroids\n",
    "error = dist(C, C_old, None)\n",
    "# Loop will run till the error becomes zero\n",
    "while error != 0:\n",
    "    # Assigning each value to its closest cluster\n",
    "    for i in range(len(X)):\n",
    "        distances = dist(X[i], C)\n",
    "        cluster = np.argmin(distances)\n",
    "        clusters[i] = cluster\n",
    "    # Storing the old centroid values\n",
    "    C_old = deepcopy(C)\n",
    "    # Finding the new centroids by taking the average value\n",
    "    for i in range(k):\n",
    "        points = [X[j] for j in range(len(X)) if clusters[j] == i]\n",
    "        C[i] = np.mean(points, axis=0)\n",
    "    error = dist(C, C_old, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PreagroHcw9d"
   },
   "outputs": [],
   "source": [
    "colors = ['r', 'g', 'b', 'y', 'c', 'm']\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(k):\n",
    "        points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])\n",
    "        ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])\n",
    "ax.scatter(C[:, 0], C[:, 1], marker='*', s=200, c='#050505')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i54sq_r1c-8r"
   },
   "source": [
    "From this visualization it is clear that there are 3 clusters with black stars as their centroid.\n",
    "\n",
    "If you run K-Means with wrong values of K, you will get completely misleading clusters. For example, if you run K-Means on this with values 2, 4, 5 and 6, you will get the following clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tlEfSRe9dAaw"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aqwL2XCgW8Sz"
   },
   "source": [
    "#K-means on Retail_customer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jvyTqvp9Me0h"
   },
   "outputs": [],
   "source": [
    "# import dataset\n",
    "dataset=pd.read_csv('Retail_Customers.csv')\n",
    "X=dataset.iloc[:, [3,4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xG6eja0rMoAY"
   },
   "outputs": [],
   "source": [
    "# let's plot elbow method graph\n",
    "wcss = []\n",
    "for i in range(1,11):\n",
    "    kmeans=KMeans(n_clusters= i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.title('The Elbow method')\n",
    "plt.xlabel('Number of Cluster')\n",
    "plt.ylabel('wcss')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CvM4Abx0bCpS"
   },
   "outputs": [],
   "source": [
    "# make a dendogram used ward method \n",
    "import scipy.cluster.hierarchy as sch\n",
    "dendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'))\n",
    "plt.title('Dendogram')\n",
    "plt.xlabel('customers')\n",
    "plt.ylabel('Euclidean distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fXN1eE0DMrTN"
   },
   "outputs": [],
   "source": [
    "# so applying take five cluster on the basis of elbow method\n",
    "kmeans = KMeans(n_clusters= 5, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "y_kmeans = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xqy9qxuFMviG"
   },
   "outputs": [],
   "source": [
    "# predict the cluster\n",
    "y_kmeans = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "stNjGnSmMx6y"
   },
   "outputs": [],
   "source": [
    "# Visulising the five Cluster\n",
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s=100, c='red', label = 'Careful')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s=100, c='blue', label = 'Standard')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s=100, c='green', label = 'Target')\n",
    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s=100, c='cyan', label = 'Careless')\n",
    "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s=100, c='magenta', label = 'Sensible')\n",
    "# apply centroid for all cluster\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0] , kmeans.cluster_centers_[:, 1] , s=300, c='yellow', label = 'Centroids')\n",
    "plt.title('Cluster of Client')\n",
    "plt.xlabel('Annual income (k$)')\n",
    "plt.ylabel('Spending Score(1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F6ZfbgDUM14x"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dXjc0ALWeojL"
   },
   "source": [
    "#US Arrests Using Hierarchical Clustering Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "px-BzrgveqzO"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler= StandardScaler()\n",
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fUbmpowSfBYE"
   },
   "outputs": [],
   "source": [
    "crime = pd.read_csv(\"USArrests.csv\")\n",
    "crime.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ePQX_rx3fJIx"
   },
   "outputs": [],
   "source": [
    "# Let's see how many rows and columns we got!\n",
    "\n",
    "crime.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_GmXBGiMfOxb"
   },
   "outputs": [],
   "source": [
    "#Let's see some facts here\n",
    "\n",
    "crime.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2VGFnvy2fQ8D"
   },
   "outputs": [],
   "source": [
    "# Let's get some statistics summary\n",
    "\n",
    "crime.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H9dhEHt_fTOR"
   },
   "outputs": [],
   "source": [
    "#Let's check for missing values.\n",
    "crime.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t4HC6m4CfVno"
   },
   "outputs": [],
   "source": [
    "# Renaming the column as Unnmaed doesn't make sense.\n",
    "\n",
    "crime = crime.rename(columns={'Unnamed: 0':'State'})\n",
    "crime.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AiR2hq0Yfc-z"
   },
   "outputs": [],
   "source": [
    "#Murder rate\n",
    "plt.figure(figsize=(20,5))\n",
    "crime.groupby('State')['Murder'].max().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4ASJJHq9foWN"
   },
   "source": [
    "Observations:\n",
    "Highest Murder Rate : Georgia and Missisippi\n",
    "Lowest Murder Rate : Idaho , Iowa, Maine, New Hampshire, North Dakota, Vermont and Wisconsin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jCQVIEX_fiII"
   },
   "outputs": [],
   "source": [
    "#Assault Rate\n",
    "plt.figure(figsize=(20,5))\n",
    "crime.groupby('State')['Assault'].max().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "En26ym9Ufu3N"
   },
   "source": [
    "Observations:\n",
    "Highest Assualt Rate : Florida and North California.\n",
    "Lowest Assualt Rate : Hawaii, North Dakota, Vermont , New Hampshire and Wisconsin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y6tQxG2QftcT"
   },
   "outputs": [],
   "source": [
    "#Rape Rate\n",
    "plt.figure(figsize=(20,5))\n",
    "crime.groupby('State')['Rape'].max().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Eh95H_1sf1EQ"
   },
   "outputs": [],
   "source": [
    "data = crime.iloc[:,1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q8pcAGPegFdq"
   },
   "outputs": [],
   "source": [
    "scaled_data = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "44tDxqQbgHML"
   },
   "outputs": [],
   "source": [
    "#1) Single Leakage:\n",
    "#The distance between 2 clusters is defined as the shortest distance between points in the two clusters\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title(\"Crime Rate Dendograms\")\n",
    "dend = sch.dendrogram(sch.linkage(scaled_data, method='single'))\n",
    "plt.xlabel('Crime Rate')\n",
    "plt.ylabel('Euclidean distances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7AH-LJ8pgJQt"
   },
   "outputs": [],
   "source": [
    "#2) Complete Linkage:\n",
    "#The distance between 2 clusters is defined as the maximum distance between any 2 points in the clusters\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title(\"Crime Rate Dendograms\")\n",
    "dend = sch.dendrogram(sch.linkage(scaled_data, method='complete'))\n",
    "plt.xlabel('Crime Rate')\n",
    "plt.ylabel('Euclidean distances')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MnrrHlpAhNY-"
   },
   "source": [
    "How do we determine the optimal number of clusters from this diagram?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vBI7uMg4hJ5Z"
   },
   "outputs": [],
   "source": [
    "# Fit the Agglomerative Clustering\n",
    " \n",
    "AC = AgglomerativeClustering(n_clusters = 3, affinity = 'euclidean', linkage ='ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "af-Se7OVhQsN"
   },
   "outputs": [],
   "source": [
    "# Fit and predict to have the cluster labels.\n",
    "y_pred =AC.fit_predict(data)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Jg73TqARhSr_"
   },
   "outputs": [],
   "source": [
    "# Fetch the cluster labels\n",
    "crime['cluster labels']= y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dpx8isL7hVK7"
   },
   "outputs": [],
   "source": [
    "# Let's see which State falls in which cluster\n",
    "crime[['State','cluster labels']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BOjoLPlQhabT"
   },
   "source": [
    "Observations:\n",
    "\n",
    "The Cities in the Cluster-0 seems to be Safe-Zone where there are relativley less Murders,Assaults and Rapes.\n",
    "\n",
    "The Cities in Cluster-1 seems to have higher crime rates and can be regarded as Danger-Zone.\n",
    "\n",
    "The Cities in Cluster-3 seems to have moderate crime rates when compared to other zones and can be called as Moderate-Zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ANW-ycHAhW6Z"
   },
   "outputs": [],
   "source": [
    "Safe_Zone= crime.groupby('cluster labels')['State'].unique()[0]\n",
    "Safe_Zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2YtsyLvWhk3Y"
   },
   "outputs": [],
   "source": [
    "Danger_Zone= crime.groupby('cluster labels')['State'].unique()[1]\n",
    "Danger_Zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hy9l3v3dhmgY"
   },
   "outputs": [],
   "source": [
    "Moderate_Zone= crime.groupby('cluster labels')['State'].unique()[2]\n",
    "Moderate_Zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b_KkE-JehoXE"
   },
   "outputs": [],
   "source": []
  }
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