DBSCAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DBSCAN.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "PKXY9ayWIhLG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "a2c7d09a-56aa-4924-d213-f4b709656221"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import normalize \n",
        "from sklearn.decomposition import PCA \n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "from sklearn.metrics.cluster import adjusted_rand_score\n",
        "\n",
        "\n",
        "\n",
        "# Importing CSV File\n",
        "url = 'https://raw.githubusercontent.com/varuncj02/Coding-Challenge/master/ClusterPlot.csv'\n",
        "\n",
        "df1 = pd.read_csv(url)\n",
        "df1.drop('Unnamed: 0', axis = 1, inplace = True)\n",
        "\n",
        "# To make it more generic if any misplaced or empty values then data processed\n",
        "df1.fillna(method ='ffill', inplace = True) \n",
        "\n",
        "plt.scatter(df1[\"V1\"], df1[\"V2\"])\n",
        "print(df1.head())\n",
        "\n"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     V1    V2\n",
            "0  4.30  0.80\n",
            "1  3.95  0.80\n",
            "2  3.95  0.75\n",
            "3  3.85  0.85\n",
            "4  4.30  0.80\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jx4B1GuLs8J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3938e468-08a3-4861-b0d7-54e5c1cdfe48"
      },
      "source": [
        "#Determining the Ideal Epsilon Value using the Elbow method\n",
        "neigh = NearestNeighbors(n_neighbors = 2)\n",
        "nbrs = neigh.fit(df1)\n",
        "distances, indices = nbrs.kneighbors(df1)\n",
        "\n",
        "print(distances)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.         0.        ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.        ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.15811388]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.25      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.        ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.        ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.25      ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.20615528]\n",
            " [0.         0.05      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.18027756]\n",
            " [0.         0.07071068]\n",
            " [0.         0.14142136]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.05      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.05      ]\n",
            " [0.         0.18027756]\n",
            " [0.         0.1       ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.1       ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.14142136]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.05      ]\n",
            " [0.         0.2       ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.2236068 ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.        ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.        ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.07071068]\n",
            " [0.         0.        ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.18027756]\n",
            " [0.         0.05      ]\n",
            " [0.         0.25      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.42720019]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.25495098]\n",
            " [0.         0.05      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.26925824]\n",
            " [0.         0.26925824]\n",
            " [0.         0.2236068 ]\n",
            " [0.         0.        ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.14142136]\n",
            " [0.         0.        ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.1       ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.26925824]\n",
            " [0.         0.05      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.1118034 ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.        ]\n",
            " [0.         0.07071068]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]\n",
            " [0.         0.05      ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1SYPYy2N6NX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c6a3adc2-0b66-4464-c17f-66567f311bb9"
      },
      "source": [
        "distances = np.sort(distances, axis=0)\n",
        "distances = distances[:,1]\n",
        "plt.plot(distances)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9c86465240>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8s7gTwFsPn4a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b7d0ed9a-fcec-49d2-df08-29db51ec14c9"
      },
      "source": [
        "#Epsilon Value will be the value at the maximum curvature in this case that value being around (0.27-0.32)\n",
        "#For ease of use epsilon value of 0.3 is selected\n",
        "\n",
        "m = DBSCAN(eps= 0.3, min_samples= 5)\n",
        "m.fit(df1, 3)\n",
        "\n",
        "\n"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DBSCAN(algorithm='auto', eps=0.3, leaf_size=30, metric='euclidean',\n",
              "       metric_params=None, min_samples=5, n_jobs=None, p=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVntrh4NXsvx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "f2542e65-4b98-42f9-ea11-f10b121a1e6c"
      },
      "source": [
        "clusters = m.labels_\n",
        "\n",
        "print(clusters)\n",
        "colors = ['royalblue', 'maroon', 'mediumorchid'] \n",
        "vectorizer = np.vectorize(lambda x: colors[x % len(colors)])\n",
        "\n",
        "plt.scatter(df1['V1'], df1['V2'], c=vectorizer(clusters))\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 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  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  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  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  1  1  1  1  1  1  1  1\n",
            "  1  1  1  1  1  1]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f9c7dc26c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}