Lecture_8_Kmean_SecondEdition.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.notebook import tqdm\n",
        "%matplotlib inline"
      ],
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        "data = pd.read_csv(\"https://raw.githubusercontent.com/ashishpatel26/Ganpat-University-Data-Science/main/data/Mall_Customers.csv\")\n",
        "data.head()"
      ],
      "execution_count": 3,
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              "  <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$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
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              "      <td>Male</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\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"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
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              "    <tr>\n",
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              "      <td>81</td>\n",
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              "      <td>16</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
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              "    <tr>\n",
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              "      <td>137</td>\n",
              "      <td>83</td>\n",
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              "<p>200 rows × 2 columns</p>\n",
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              "     Annual Income (k$)  Spending Score (1-100)\n",
              "0                    15                      39\n",
              "1                    15                      81\n",
              "2                    16                       6\n",
              "3                    16                      77\n",
              "4                    17                      40\n",
              "..                  ...                     ...\n",
              "195                 120                      79\n",
              "196                 126                      28\n",
              "197                 126                      74\n",
              "198                 137                      18\n",
              "199                 137                      83\n",
              "\n",
              "[200 rows x 2 columns]"
            ]
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          "metadata": {
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          "execution_count": 8
        }
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        "id": "mFymwuF23WsX",
        "outputId": "0f1b7b57-a138-4c74-90e1-00681bc52da3"
      },
      "source": [
        "X[\"Annual Income (k$)\"].value_counts().plot(kind=\"bar\", figsize=(14,7), xlabel=\"Amount Spent in thousands\", ylabel=\"Number of time spend\")\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "3_GZrQze3rsu",
        "outputId": "520835f3-bfb8-4227-dafc-e417463bc802"
      },
      "source": [
        "X[\"Spending Score (1-100)\"].value_counts().plot(kind=\"bar\", figsize=(14,7), xlabel=\"Amount Spent in thousands\", ylabel=\"Number of time spend\")\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnpNmpmz4K68"
      },
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679,
          "referenced_widgets": [
            "96137272a48a464fb1d2ec8dc01a02b0",
            "d3e123fce2e743c7959cf2661da23499",
            "fcb7f6df24774b51b917296874844abe",
            "e2a3b93f15454aa18d65bbca36ae4230",
            "6db9aa86faf64428847762d11c741d2c",
            "b1a48689e25347d9a4a63d8eb317f47b",
            "1257f5803ef44c8ab7dd06dc8503f72f",
            "4f75dcb53f6d4d549f5e404e0cad8aef"
          ]
        },
        "id": "wsxT0fIL4oSs",
        "outputId": "b4e6964a-c985-4fbf-df19-abd6505a3483"
      },
      "source": [
        "# X = X.values\n",
        "C_inertia = []\n",
        "Davies = []\n",
        "silhauett = []\n",
        "for i in tqdm(range(2,11)):\n",
        "  kmean = KMeans(n_clusters=i, random_state=42)\n",
        "  kmean.fit(X) # training\n",
        "  C_inertia.append(kmean.inertia_)\n",
        "  Y_Kmeans = kmean.fit_predict(X)\n",
        "  print(f\"------K value is: {i}------\")\n",
        "  print(f\"Inertia : {kmean.inertia_}\")\n",
        "  # print(f\"Davies Score : {davies_bouldin_score(X, Y_Kmeans)}\")\n",
        "  Davies.append(davies_bouldin_score(X, Y_Kmeans))\n",
        "  # print(f\"Silhouette Score : {silhouette_score(X, Y_Kmeans)}\")\n",
        "  silhauett.append(silhouette_score(X, Y_Kmeans))\n",
        "\n",
        "result_analysis = pd.DataFrame()\n",
        "result_analysis[\"K\"] = range(2,11)\n",
        "result_analysis[\"Inertia\"] = C_inertia\n",
        "result_analysis[\"D_Score\"] = Davies\n",
        "result_analysis[\"S_score\"] = silhauett\n",
        "result_analysis"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "96137272a48a464fb1d2ec8dc01a02b0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=9.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "------K value is: 2------\n",
            "Inertia : 181363.59595959596\n",
            "------K value is: 3------\n",
            "Inertia : 106348.37306211118\n",
            "------K value is: 4------\n",
            "Inertia : 73679.78903948834\n",
            "------K value is: 5------\n",
            "Inertia : 44448.45544793371\n",
            "------K value is: 6------\n",
            "Inertia : 37265.86520484347\n",
            "------K value is: 7------\n",
            "Inertia : 30241.34361793659\n",
            "------K value is: 8------\n",
            "Inertia : 25336.946861471864\n",
            "------K value is: 9------\n",
            "Inertia : 21850.165282585633\n",
            "------K value is: 10------\n",
            "Inertia : 19634.55462934998\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>K</th>\n",
              "      <th>Inertia</th>\n",
              "      <th>D_Score</th>\n",
              "      <th>S_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>181363.595960</td>\n",
              "      <td>1.256774</td>\n",
              "      <td>0.296897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>106348.373062</td>\n",
              "      <td>0.715277</td>\n",
              "      <td>0.467614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>73679.789039</td>\n",
              "      <td>0.710364</td>\n",
              "      <td>0.493196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>44448.455448</td>\n",
              "      <td>0.572563</td>\n",
              "      <td>0.553932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>37265.865205</td>\n",
              "      <td>0.658859</td>\n",
              "      <td>0.537968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7</td>\n",
              "      <td>30241.343618</td>\n",
              "      <td>0.710833</td>\n",
              "      <td>0.528810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>25336.946861</td>\n",
              "      <td>0.732186</td>\n",
              "      <td>0.459250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9</td>\n",
              "      <td>21850.165283</td>\n",
              "      <td>0.756606</td>\n",
              "      <td>0.456508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>10</td>\n",
              "      <td>19634.554629</td>\n",
              "      <td>0.754907</td>\n",
              "      <td>0.450566</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    K        Inertia   D_Score   S_score\n",
              "0   2  181363.595960  1.256774  0.296897\n",
              "1   3  106348.373062  0.715277  0.467614\n",
              "2   4   73679.789039  0.710364  0.493196\n",
              "3   5   44448.455448  0.572563  0.553932\n",
              "4   6   37265.865205  0.658859  0.537968\n",
              "5   7   30241.343618  0.710833  0.528810\n",
              "6   8   25336.946861  0.732186  0.459250\n",
              "7   9   21850.165283  0.756606  0.456508\n",
              "8  10   19634.554629  0.754907  0.450566"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "F2T5NBNz6KW6",
        "outputId": "f5e5b1a2-99e0-4c40-a98e-4069d7bfb944"
      },
      "source": [
        "plt.figure(figsize=(14,6))\n",
        "plt.plot(range(1,11), C_inertia)\n",
        "plt.title(\"The Elbow Method\")\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"Inertia\")\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AlTjEVN6oh2"
      },
      "source": [
        "  kmean = KMeans(n_clusters=5, random_state=42)\n",
        "  Y_Kmeans = kmean.fit_predict(X) # training"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "B1hIFxVA8JHN",
        "outputId": "c916dd9f-f190-4f6d-f0e3-0b8ccd4d655b"
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "\n",
        "plt.scatter(X[Y_Kmeans == 0, 0], X[Y_Kmeans == 0,1],s = 60, c='red', label = 'Cluster 1')\n",
        "\n",
        "plt.scatter(X[Y_Kmeans == 1, 0], X[Y_Kmeans == 1,1],s = 70, c='blue', label = 'Cluster 2')\n",
        "\n",
        "plt.scatter(X[Y_Kmeans == 2, 0], X[Y_Kmeans == 2,1],s = 80, c='green', label = 'Cluster 3')\n",
        "\n",
        "plt.scatter(X[Y_Kmeans == 3, 0], X[Y_Kmeans == 3,1],s = 90, c='cyan', label = 'Cluster 4')\n",
        "\n",
        "plt.scatter(X[Y_Kmeans == 4, 0], X[Y_Kmeans == 4,1],s = 100, c='magenta', label = 'Cluster 5')\n",
        "\n",
        "plt.scatter(kmean.cluster_centers_[:,0], kmean.cluster_centers_[:,1], s = 300, c = 'orange', label = 'Centroids')\n",
        "    \n",
        "plt.title('Clusters of clients')\n",
        "plt.xlabel('Annual Income (k$)')\n",
        "plt.ylabel('Spending score (1-100)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxoSCDjq-jRo"
      },
      "source": [
        "data[\"Cluster\"] = Y_Kmeans"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "ixdmgZ7Q-k_j",
        "outputId": "2334e2e8-c3e3-4fd7-b9ed-c09285b912ea"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>CustomerID</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)  Cluster\n",
              "0           1    Male   19                  15                      39        3\n",
              "1           2    Male   21                  15                      81        0\n",
              "2           3  Female   20                  16                       6        3\n",
              "3           4  Female   23                  16                      77        0\n",
              "4           5  Female   31                  17                      40        3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ovzcpolr-w3N"
      },
      "source": [
        "from sklearn.metrics import davies_bouldin_score, silhouette_score"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIdIDzWI_IwB",
        "outputId": "679eb791-ed19-4702-9866-1ce0f111a061"
      },
      "source": [
        "print(f\"Davies Score : {davies_bouldin_score(X, Y_Kmeans)}\")\n",
        "print(f\"Silhouette Score : {silhouette_score(X, Y_Kmeans)}\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Davies Score : 0.5725628995597082\n",
            "Silhouette Score : 0.553931997444648\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Ub4r6y1_U4q"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}