Lecture10.ipynb

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        "# Clustering\n",
        "\n",
        "In contrast to *supervised* machine learning, *unsupervised* learning is used when there is no \"ground truth\" from which to train and validate label predictions.\n",
        "\n",
        "The most common form of unsupervised learning is *clustering*, which is simllar conceptually to *classification*, except that the the training data does not include known values for the class label to be predicted.\n",
        "\n",
        "Clustering works by separating the training cases based on similarities that can be determined from their feature values.\n",
        "\n",
        "## Seed dataset\n",
        "Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes, and can be downloaded from the UCI dataset repository (Dua, D. and Graff, C. (2019). [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml)."
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        "import pandas as pd\n",
        "\n",
        "# load the training dataset\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/satishgunjal/ml-basics/master/data/seeds.csv')\n",
        "\n",
        "data.head()"
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              "    area  perimeter  compactness  kernel_length  kernel_width  \\\n",
              "0  15.26      14.84       0.8710          5.763         3.312   \n",
              "1  14.88      14.57       0.8811          5.554         3.333   \n",
              "2  14.29      14.09       0.9050          5.291         3.337   \n",
              "3  13.84      13.94       0.8955          5.324         3.379   \n",
              "4  16.14      14.99       0.9034          5.658         3.562   \n",
              "\n",
              "   asymmetry_coefficient  groove_length  species  \n",
              "0                  2.221          5.220        0  \n",
              "1                  1.018          4.956        0  \n",
              "2                  2.699          4.825        0  \n",
              "3                  2.259          4.805        0  \n",
              "4                  1.355          5.175        0  "
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              "      <th></th>\n",
              "      <th>area</th>\n",
              "      <th>perimeter</th>\n",
              "      <th>compactness</th>\n",
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              "      <th>kernel_width</th>\n",
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              "      <td>15.26</td>\n",
              "      <td>14.84</td>\n",
              "      <td>0.8710</td>\n",
              "      <td>5.763</td>\n",
              "      <td>3.312</td>\n",
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              "      <td>14.88</td>\n",
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              "      <td>0.8811</td>\n",
              "      <td>5.554</td>\n",
              "      <td>3.333</td>\n",
              "      <td>1.018</td>\n",
              "      <td>4.956</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14.29</td>\n",
              "      <td>14.09</td>\n",
              "      <td>0.9050</td>\n",
              "      <td>5.291</td>\n",
              "      <td>3.337</td>\n",
              "      <td>2.699</td>\n",
              "      <td>4.825</td>\n",
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              "      <th>3</th>\n",
              "      <td>13.84</td>\n",
              "      <td>13.94</td>\n",
              "      <td>0.8955</td>\n",
              "      <td>5.324</td>\n",
              "      <td>3.379</td>\n",
              "      <td>2.259</td>\n",
              "      <td>4.805</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16.14</td>\n",
              "      <td>14.99</td>\n",
              "      <td>0.9034</td>\n",
              "      <td>5.658</td>\n",
              "      <td>3.562</td>\n",
              "      <td>1.355</td>\n",
              "      <td>5.175</td>\n",
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      "source": [
        "## Visualization - Scatter Plot"
      ]
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      "source": [
        "As you can see, the dataset contains six data points (or *features*) for each instance (*observation*) of a seed. So you could interpret these as coordinates that describe each instance's location in six-dimensional space.\n",
        "\n",
        "Now, of course six-dimensional space is difficult to visualise in a three-dimensional world, or on a two-dimensional plot. We should take advantage of a dimensionality reduction technique, however we did not cover yet.\n",
        "\n",
        "Alternatively, we just chose choose two variable (`area`, `groove_length`) visualize the data."
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        "outputId": "ae516eae-a079-482e-fe86-0be4739bce75"
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_style('whitegrid')\n",
        "sns.scatterplot(x='area', y='groove_length', data=data, hue='species')\n",
        "plt.show()"
      ],
      "execution_count": null,
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgAQqSzK7qKT"
      },
      "source": [
        "## Set up DataFrame\n",
        "Drop `species` values from DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Vtn8EHjH27rL",
        "outputId": "acb9566d-df26-405f-f53d-499dc9b7ce15"
      },
      "source": [
        "X = data.drop(columns='species')\n",
        "X.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    area  perimeter  compactness  kernel_length  kernel_width  \\\n",
              "0  15.26      14.84       0.8710          5.763         3.312   \n",
              "1  14.88      14.57       0.8811          5.554         3.333   \n",
              "2  14.29      14.09       0.9050          5.291         3.337   \n",
              "3  13.84      13.94       0.8955          5.324         3.379   \n",
              "4  16.14      14.99       0.9034          5.658         3.562   \n",
              "\n",
              "   asymmetry_coefficient  groove_length  \n",
              "0                  2.221          5.220  \n",
              "1                  1.018          4.956  \n",
              "2                  2.699          4.825  \n",
              "3                  2.259          4.805  \n",
              "4                  1.355          5.175  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "      <th>perimeter</th>\n",
              "      <th>compactness</th>\n",
              "      <th>kernel_length</th>\n",
              "      <th>kernel_width</th>\n",
              "      <th>asymmetry_coefficient</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15.26</td>\n",
              "      <td>14.84</td>\n",
              "      <td>0.8710</td>\n",
              "      <td>5.763</td>\n",
              "      <td>3.312</td>\n",
              "      <td>2.221</td>\n",
              "      <td>5.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14.88</td>\n",
              "      <td>14.57</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>5.554</td>\n",
              "      <td>3.333</td>\n",
              "      <td>1.018</td>\n",
              "      <td>4.956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14.29</td>\n",
              "      <td>14.09</td>\n",
              "      <td>0.9050</td>\n",
              "      <td>5.291</td>\n",
              "      <td>3.337</td>\n",
              "      <td>2.699</td>\n",
              "      <td>4.825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13.84</td>\n",
              "      <td>13.94</td>\n",
              "      <td>0.8955</td>\n",
              "      <td>5.324</td>\n",
              "      <td>3.379</td>\n",
              "      <td>2.259</td>\n",
              "      <td>4.805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16.14</td>\n",
              "      <td>14.99</td>\n",
              "      <td>0.9034</td>\n",
              "      <td>5.658</td>\n",
              "      <td>3.562</td>\n",
              "      <td>1.355</td>\n",
              "      <td>5.175</td>\n",
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              "  </tbody>\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e1dd1a0d-051d-427e-9294-7c75dead3e85 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e1dd1a0d-051d-427e-9294-7c75dead3e85');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDDo2hC45ZG2"
      },
      "source": [
        "## How many clusters?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "44_JCzEb1U3n"
      },
      "source": [
        "Hopefully you can see at least two, arguably three, reasonably distinct groups of data points; but here lies one of the fundamental problems with clustering - without known class labels, how do you know how many clusters to separate your data into?\n",
        "\n",
        "One way we can try to find out is to use a data sample to create a series of clustering models with an incrementing number of clusters, and measure how tightly the data points are grouped within each cluster. A metric often used to measure this tightness is the `within cluster sum of squares` (WCSS), with lower values meaning that the data points are closer. You can then plot the WCSS for each model.\n",
        "\n",
        "$WCSS = \\sum_{j=1}^k\\sum_{i}^n\\mathrm{dist}(x_i, C_j)^2$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "JMQ74SLT8IeU",
        "outputId": "7cb9aafb-2610-4190-9608-bd3928b5599a"
      },
      "source": [
        "#importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "%matplotlib inline\n",
        "\n",
        "# Create 10 models with 1 to 10 clusters\n",
        "wcss = []\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters = i)\n",
        "    # Fit the data points\n",
        "    kmeans.fit(X.values)\n",
        "    # Get the WCSS (inertia) value\n",
        "    wcss.append(kmeans.inertia_)\n",
        "    \n",
        "#Plot the WCSS values onto a line graph\n",
        "plt.plot(range(1, 11), wcss)\n",
        "plt.title('WCSS by Clusters')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F7vYEjyW6ynW"
      },
      "source": [
        "The plot shows a large reduction in WCSS (so greater *tightness*) as the number of clusters increases from one to two, and a further noticable reduction from two to three clusters. After that, the reduction is less pronounced, resulting in an \"elbow\" in the chart at around three clusters. This is a good indication that there are two to three reasonably well separated clusters of data points."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzReHx3D1U3p"
      },
      "source": [
        "## K-Means Clustering\n",
        "\n",
        "The algorithm we used to create our test clusters is *K-Means*. This is a commonly used clustering algorithm that separates a dataset into *K* clusters of equal variance. The number of clusters, *K*, is user defined. The basic algorithm has the following steps:\n",
        "\n",
        "**[TODO]** Let's try using K-Means on our seeds data with a `K` value of 3 with `random_state=0`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbQJmYD01U3q"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Create a model based on 3 centroids\n",
        "model = KMeans(n_clusters=3,random_state=0)\n",
        "\n",
        "# Fit to the data and predict the cluster assignments for each data point\n",
        "km_clusters = model.fit_predict(X)\n",
        "\n",
        "# Assign the cluster\n",
        "X_withCluster = X.copy()\n",
        "X_withCluster['km_clusters'] = km_clusters"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "to1opddr1U3t"
      },
      "source": [
        "## Hierarchical Clustering\n",
        "\n",
        "Hierarchical clustering methods make fewer distributional assumptions when compared to K-means methods. However, K-means methods are generally more scalable, sometimes very much so.\n",
        "\n",
        "### Agglomerative Nesting (AGNES) Clustering \n",
        "**[TODO]** Let's try using AGNES on our seeds data with a `average` linkage.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmBIvBgH1U3t"
      },
      "source": [
        "from sklearn.cluster import AgglomerativeClustering\n",
        "\n",
        "agg_model = AgglomerativeClustering(n_clusters=3, linkage='average')\n",
        "agg_clusters = agg_model.fit_predict(X)\n",
        "\n",
        "X_withCluster['agg_clusters'] = agg_clusters"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHHbGens1U3t"
      },
      "source": [
        "## Visulize the Clustering Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDxFLpYr1U3u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "80af4782-fd5d-4127-bb41-f7f8e3cff970"
      },
      "source": [
        "fig, ax = plt.subplots(1, 3, figsize=(15,5))\n",
        "\n",
        "sns.scatterplot(x='area', y='groove_length', data=data, hue='species',\n",
        "           ax=ax[0])\n",
        "sns.scatterplot(x='area', y='groove_length', data=X_withCluster, hue='km_clusters',\n",
        "           palette='coolwarm', ax=ax[1])\n",
        "sns.scatterplot(x='area', y='groove_length', data=X_withCluster, hue='agg_clusters',\n",
        "           palette='deep', ax=ax[2])\n",
        "ax[0].set(title='True Label')\n",
        "ax[1].set(title='k-Means Clustering Results')\n",
        "ax[2].set(title='Hierarchical Clustering Results')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "f1PBBP_LJrNJ",
        "outputId": "1d05f9a5-a92f-4cf1-aeec-9bf3e2f10621"
      },
      "source": [
        "# Import dendrogram\n",
        "from scipy.cluster.hierarchy import dendrogram\n",
        "from scipy.cluster.hierarchy import linkage\n",
        "\n",
        "# create dendrogram using ward linkage\n",
        "dendrogram(linkage(X.values, method='average'), color_threshold=4)\n",
        "\n",
        "# Set title of the plot\n",
        "plt.title('Hierarchical Clustering: Dendrogram')\n",
        "plt.xlabel('Data Items')\n",
        "plt.ylabel('Distance')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nDYFxOBJSY5"
      },
      "source": [
        "# Evaluation\n",
        "\n",
        "**[TODO]** Internal Evaluation with `Silhouette Score`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SvEltGjyEZpp",
        "outputId": "7908c12f-85e7-477c-9ce2-b0665dac0488"
      },
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "print('silhouette of kMeans: %f'%silhouette_score(X,km_clusters))\n",
        "print('silhouette of Hierarchical Clustering: %f'%silhouette_score(X,agg_clusters))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "silhouette of kMeans: 0.471934\n",
            "silhouette of Hierarchical Clustering: 0.458112\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3heVdLSMJZLV"
      },
      "source": [
        "**[TODO]** External Evaluation with `F1 Score`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RsrkScCHGF4t",
        "outputId": "16d5a20f-70b2-4b9b-e34c-f4be25fe0c82"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "target = data.species\n",
        "print('kMeans')\n",
        "print(classification_report(target, km_clusters))\n",
        "\n",
        "_agg_clusters = agg_clusters.copy()\n",
        "_agg_clusters[agg_clusters==0]=2\n",
        "_agg_clusters[agg_clusters==2]=0\n",
        "\n",
        "print('Hierarchical Clustering')\n",
        "print(classification_report(target, _agg_clusters))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kMeans\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.86      0.85        70\n",
            "           1       0.98      0.86      0.92        70\n",
            "           2       0.88      0.97      0.93        70\n",
            "\n",
            "    accuracy                           0.90       210\n",
            "   macro avg       0.90      0.90      0.90       210\n",
            "weighted avg       0.90      0.90      0.90       210\n",
            "\n",
            "Hierarchical Clustering\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.94      0.87        70\n",
            "           1       0.98      0.91      0.95        70\n",
            "           2       0.95      0.87      0.91        70\n",
            "\n",
            "    accuracy                           0.91       210\n",
            "   macro avg       0.92      0.91      0.91       210\n",
            "weighted avg       0.92      0.91      0.91       210\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# [TODO]\n",
        "\n",
        "## 이번 과제는 라이브 셀링에 관한 데이터입니다!\n",
        "\n",
        "라이브 셀러(책, '라이브 커머스, 셀링의 기술' 참조)란?\n",
        "\n",
        "[우리는 알게 모르게 이미 물건을 팔고 있다. 내가 산 전자제품이 마음에 들면 블로그에 자발적 후기를 올리고, 배달 음식을 시켜 먹어도 사진을 찍고 전문가 수준으로 맛을 표현해가며 앱에 평가를 적는다. 책을 읽고 리뷰를 써서 SNS에 공유하고, 캠핑을 다녀와서 그곳이 얼마나 아름다웠는지 사진으로 열변을 토한다. 내 리뷰를 보고 누군가 그 음식을 시켜먹었다면, 내가 찍은 사진을 보고 그 캠핑장을 방문했다면 여러분은 이미 셀러로서의 능력을 충분히 갖춘 셈이다. \n",
        "\n",
        "사람들은 이제 광고에 쉽게 넘어가지 않는다. 지인들의 평가, 직접 찍은 사진과 함께 공들여 쓴 자세한 리뷰, 평소에 팔로우하던 인플루언서의 소개를 보고 물건을 구매한 지 오래다.]\n",
        "\n",
        "이 데이터에 속성들은 share를 얼마나했는지 comment를 얼마나 달았는지, link나 video, photo등에 대해서 어떤 감정표현을 남겼는지 나타내준다."
      ],
      "metadata": {
        "id": "BFyXWFhn3S3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/wonseok-hong/DAP_TA/main/Live.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "HISX8ghhK7cd",
        "outputId": "63a64937-0b29-4ff5-a705-573fa44cd45d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          status_id status_type status_published  \\\n",
              "0  246675545449582_1649696485147474       video   4/22/2018 6:00   \n",
              "1  246675545449582_1649426988507757       photo  4/21/2018 22:45   \n",
              "2  246675545449582_1648730588577397       video   4/21/2018 6:17   \n",
              "3  246675545449582_1648576705259452       photo   4/21/2018 2:29   \n",
              "4  246675545449582_1645700502213739       photo   4/18/2018 3:22   \n",
              "\n",
              "   num_reactions  num_comments  num_shares  num_likes  num_loves  num_wows  \\\n",
              "0            529           512         262        432         92         3   \n",
              "1            150             0           0        150          0         0   \n",
              "2            227           236          57        204         21         1   \n",
              "3            111             0           0        111          0         0   \n",
              "4            213             0           0        204          9         0   \n",
              "\n",
              "   num_hahas  num_sads  num_angrys  Column1  Column2  Column3  Column4  \n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 데이터 전처리\n",
        "- Clustering은 Unsupervise Learning으로 정답값이 없이 결과를 도출해 내야 합니다!\n",
        "- mapping을 통한 라벨링이나 drop등을 이용하여 자유롭게 전처리 해주세요!\n"
      ],
      "metadata": {
        "id": "ctsd2U91M5av"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "scaler = RobustScaler()\n",
        "\n",
        "X = df.drop(['status_id', 'status_published', 'Column1', 'Column2', 'Column3', 'Column4', 'status_type'], axis = 1)\n",
        "\n",
        "#RobustScaling\n",
        "X = pd.DataFrame(scaler.fit_transform(X), columns = X.columns)\n",
        "\n",
        "X"
      ],
      "metadata": {
        "id": "JyR_JVzorpTi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "8a366245-ef21-4020-a1f5-57b5204e19cc"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a0d123c9-8f6a-4ee1-978d-41555d36d76b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a0d123c9-8f6a-4ee1-978d-41555d36d76b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Elbow analysis with K-MEANS"
      ],
      "metadata": {
        "id": "sswqnqser_VV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "%matplotlib inline\n",
        "\n",
        "# Create 10 models with 1 to 10 clusters\n",
        "wcss = []\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters = i)\n",
        "    # Fit the data points\n",
        "    kmeans.fit(X.values)\n",
        "    # Get the WCSS (inertia) value\n",
        "    wcss.append(kmeans.inertia_)\n",
        "    \n",
        "#Plot the WCSS values onto a line graph\n",
        "plt.plot(range(1, 11), wcss)\n",
        "plt.title('WCSS by Clusters')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jKGExDQ8qTUL",
        "outputId": "22b7ae2b-a885-4299-84e5-7e5666483e07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "K-means or Any other Clustering Methods"
      ],
      "metadata": {
        "id": "_5sP5266tmSu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "\n",
        "num_clusters= 3\n",
        "\n",
        "ac = AgglomerativeClustering(n_clusters = num_clusters, linkage = 'average')\n",
        "agg_clusters = ac.fit_predict(X)\n",
        "\n",
        "X_withCluster = X.copy()\n",
        "X_withCluster['agg_clusters'] = agg_clusters"
      ],
      "metadata": {
        "id": "NSVsgDX8Ufyc"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Silhouette Coefficient값"
      ],
      "metadata": {
        "id": "aHFuc7ritpkL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "print(\"Silhouette Coefficient:\", silhouette_score(X,agg_clusters))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INEHr4QYWY_x",
        "outputId": "fa711dbe-d58a-4059-c274-b3071dfc2fdf"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Silhouette Coefficient: 0.959623625415804\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 채점 기준\n",
        "\n",
        "- Silhouette Coefficient 점수가 89 이상: 100점(만점)\n",
        "- Silhouette Coefficient 점수가 85 이상, 89미만: 90점\n",
        "- Silhouette Coefficient 점수가 80 이상, 85미만: 80점\n",
        "- Silhouette Coefficient 점수가 80미만: 70점\n"
      ],
      "metadata": {
        "id": "I_bizrhEwdFP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "AUtYsSFIGi21"
      }
    }
  ]
}