DBSCAN-Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DBSCAN is a powerful clustering technique when your data has noise and you dont know shape/distribution of your cluster.\n",
    "it has two key parameters epsilon/radious, and minPoints.\n",
    "The optimal value of radious can be found using domain knowledge, elbow method or Silhouette Score\n",
    "\n",
    "The options to choose minPoints are:\n",
    "-The larger the data set, the larger the value of MinPts should be\n",
    "-If the data set is noisier, choose a larger value of MinPts\n",
    "-Generally, MinPts should be greater than or equal to the dimensionality of the data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual_Income_(k$)</th>\n",
       "      <th>Spending_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual_Income_(k$)  Spending_Score\n",
       "0           1    Male   19                  15              39\n",
       "1           2    Male   21                  15              81\n",
       "2           3  Female   20                  16               6\n",
       "3           4  Female   23                  16              77\n",
       "4           5  Female   31                  17              40"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.metrics import silhouette_score\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "data=pd.read_csv(\"dataset/Mall_Customers.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual_Income_(k$)</th>\n",
       "      <th>Spending_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID         Age  Annual_Income_(k$)  Spending_Score\n",
       "count  200.000000  200.000000          200.000000      200.000000\n",
       "mean   100.500000   38.850000           60.560000       50.200000\n",
       "std     57.879185   13.969007           26.264721       25.823522\n",
       "min      1.000000   18.000000           15.000000        1.000000\n",
       "25%     50.750000   28.750000           41.500000       34.750000\n",
       "50%    100.500000   36.000000           61.500000       50.000000\n",
       "75%    150.250000   49.000000           78.000000       73.000000\n",
       "max    200.000000   70.000000          137.000000       99.000000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.isnull().sum()\n",
    "data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Annual_Income_(k$)</th>\n",
       "      <th>Spending_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Annual_Income_(k$)  Spending_Score\n",
       "0                  15              39\n",
       "1                  15              81\n",
       "2                  16               6\n",
       "3                  16              77\n",
       "4                  17              40"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=data[[\"Annual_Income_(k$)\",\"Spending_Score\"]]\n",
    "X.head()\n",
    "#scaler = StandardScaler()\n",
    "#scaler.fit(X)\n",
    "#X = scaler.transform(X)\n",
    "\n",
    "#print(X[0:2,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elbow method\n",
    "======\n",
    "-find Average distance between each point and its k nearest neighbors, where k = the MinPts value you selected.\n",
    "\n",
    "-The average k-distances are then plotted in ascending order on a k-distance graph.\n",
    "\n",
    "-find the optimal value for ε at the point of maximum curvature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fea1bccfe10>]"
      ]
     },
     "execution_count": 65,
     "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": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "from matplotlib import pyplot as plt\n",
    "minPoints=2\n",
    "neighbors = NearestNeighbors(n_neighbors=minPoints)\n",
    "neighbors_fit = neighbors.fit(X)\n",
    "distances, indices = neighbors_fit.kneighbors(X)\n",
    "\n",
    "print(len(distances),len(X))\n",
    "\n",
    "distances = np.sort(distances, axis=0) #sort column wise \n",
    "distances = distances[:,1]\n",
    "#print(distances)\n",
    "\n",
    "plt.plot(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "wsse=[]\n",
    "k=[]\n",
    "sil_score=[]\n",
    "for i in range(6,20):\n",
    "    dbscan=DBSCAN(eps=i, min_samples=15)\n",
    "    k.append(i)\n",
    "    dbscan.fit(X)\n",
    "    #wsse.append(dbscan.inertia_)\n",
    "    score = silhouette_score(X, dbscan.labels_, metric='euclidean')\n",
    "    sil_score.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure(figsize=(10,5))\n",
    "plt.plot(k,sil_score)\n",
    "plt.title(\"Silhouette Score\")\n",
    "plt.xlabel(\"K\")\n",
    "plt.ylabel(\"Silhouette Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
      " -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1  0 -1  0  0 -1  0  0  0\n",
      "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  0  0  0  2  0  2  0  2  1  2  1  2  0  2  1  2  1  2  1  2  1  2  0  2\n",
      "  1  2  0  2  1  2  1  2  1  2  1  2  1  2  1  2 -1  2  1  2  1  2  1  2\n",
      " -1 -1  1  2  1  2  1  2  1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
      " -1 -1 -1 -1 -1 -1 -1 -1]\n"
     ]
    }
   ],
   "source": [
    "model=DBSCAN(eps=12,min_samples=15)\n",
    "model.fit(X)\n",
    "print(model.labels_)\n",
    "p_cluster=model.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "for i in np.unique(p_cluster):\n",
    "    plt.scatter(X[p_cluster==i][\"Annual_Income_(k$)\"],X[p_cluster==i][\"Spending_Score\"], label='Cluster'+str(i))\n",
    "\n",
    "plt.title('Clusters of customers')\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=7)\n",
    "pca.fit(df_scale)\n",
    "variance = pca.explained_variance_ratio_ \n",
    "var=np.cumsum(np.round(variance, 3)*100)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.ylabel('% Variance Explained')\n",
    "plt.xlabel('# of Features')\n",
    "plt.title('PCA Analysis')\n",
    "plt.ylim(0,100.5)\n",
    "plt.plot(var)"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}