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      "source": [
        "# Iris Data Classification\n",
        "- Link Component Color Annotations\n",
        "  - <font color='yellow'>Yellow</font> : data load / preprocessing\n",
        "  - <font color='green'>Green</font> : EDA\n",
        "  - <font color='violet'>Violet</font> : model train / predict"
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      "source": [
        "### Required Python Packages\n",
        "- `numpy`\n",
        "- `pandas`\n",
        "- `scikit-learn`\n",
        "- `seaborn`\n",
        "- `matplotlib`\n",
        "\n",
        "Run the following cell to install the packages."
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        "#\n",
        "# Required Packages\n",
        "#   Run this cell to install required packages.\n",
        "#\n",
        "%pip install \"matplotlib>=2.0\" \"numpy>=1.19\" \"pandas>=1.1\" \"scikit-learn>=0.22.2\" \"seaborn>=0.11\""
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      "source": [
        "### 0. Global Parameters\n",
        "- global paprameter of link pipeline\n",
        "  - test_size : rate of valid-set when train-valid-split\n",
        "  - random_state : random_state "
      ]
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    {
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      },
      "source": ["### 1. Load package,data"]
    },
    {
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d70ec00b-7f5a-4966-9745-4c701c277bb5",
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          "id": "efb0edc1-0372-4bfe-a92c-e481b84ba186",
          "isComponent": true,
          "name": "Load data",
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      "source": ["iris = load_iris()"]
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          "isComponent": true,
          "name": "Create dataframe",
          "parents": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
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              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                  5.1               3.5                1.4               0.2   \n",
              "1                  4.9               3.0                1.4               0.2   \n",
              "2                  4.7               3.2                1.3               0.2   \n",
              "3                  4.6               3.1                1.5               0.2   \n",
              "4                  5.0               3.6                1.4               0.2   \n",
              "..                 ...               ...                ...               ...   \n",
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              "148                6.2               3.4                5.4               2.3   \n",
              "149                5.9               3.0                5.1               1.8   \n",
              "\n",
              "     target  \n",
              "0         0  \n",
              "1         0  \n",
              "2         0  \n",
              "3         0  \n",
              "4         0  \n",
              "..      ...  \n",
              "145       2  \n",
              "146       2  \n",
              "147       2  \n",
              "148       2  \n",
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        "df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
        "df[\"target\"] = iris.target\n",
        "display(df)"
      ]
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        "The `df` of green components and the `df` of purple components can be used independently of each other from the point of branching\n",
        "\n",
        "* Modeling -> `target` of `df` : **0,1,2**\n",
        "* EDA -> `target` of `df` : **setosa, versicolor, virginica**"
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      "source": ["### 2. EDA"]
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              "      <th>sepal length (cm)</th>\n",
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              "      <td>virginica</td>\n",
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              "      <td>3.0</td>\n",
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              "      <td>virginica</td>\n",
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            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                  5.1               3.5                1.4               0.2   \n",
              "1                  4.9               3.0                1.4               0.2   \n",
              "2                  4.7               3.2                1.3               0.2   \n",
              "3                  4.6               3.1                1.5               0.2   \n",
              "4                  5.0               3.6                1.4               0.2   \n",
              "..                 ...               ...                ...               ...   \n",
              "145                6.7               3.0                5.2               2.3   \n",
              "146                6.3               2.5                5.0               1.9   \n",
              "147                6.5               3.0                5.2               2.0   \n",
              "148                6.2               3.4                5.4               2.3   \n",
              "149                5.9               3.0                5.1               1.8   \n",
              "\n",
              "        target  \n",
              "0       setosa  \n",
              "1       setosa  \n",
              "2       setosa  \n",
              "3       setosa  \n",
              "4       setosa  \n",
              "..         ...  \n",
              "145  virginica  \n",
              "146  virginica  \n",
              "147  virginica  \n",
              "148  virginica  \n",
              "149  virginica  \n",
              "\n",
              "[150 rows x 5 columns]"
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        "df[\"target\"] = df[\"target\"].map({0: \"setosa\", 1: \"versicolor\", 2: \"virginica\"})\n",
        "display(df)"
      ]
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\n",
            "text/plain": ["<Figure size 444.75x360 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": ["<Figure size 444.75x360 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.pairplot(df, x_vars=[\"sepal length (cm)\"], y_vars=[\"sepal width (cm)\"], hue=\"target\", height=5)\n",
        "sns.pairplot(df, x_vars=[\"petal length (cm)\"], y_vars=[\"petal width (cm)\"], hue=\"target\", height=5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ead15d6-5496-4254-bc97-8755084c6fc9",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "#00DE62",
          "id": "2af12307-930e-4ae5-b5a4-e75b409a9480",
          "isComponent": true,
          "name": "Make heatmap",
          "parents": [
            {
              "id": "8e2fdf33-8083-483d-89a3-0a666cc0a970",
              "name": "Change target type for eda"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": ["<Figure size 504x288 with 2 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(7, 4))\n",
        "sns.heatmap(\n",
        "    df.corr(), annot=True, cmap=\"coolwarm\"\n",
        ")  # draws  heatmap with input as the correlation matrix calculted by(iris.corr())\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f86fe303-cede-4fb2-864b-0d5d9009a983",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": "2378c82b-7e83-40f0-b8a1-92fcfea0e9b7",
          "diskcache": false,
          "headerColor": "inherit",
          "id": "e7b86c18-d9eb-4b36-9383-6c61d634fb65",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["### 3. Modeling"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "71ef7918-9b45-4a25-900b-ceb6745dfe05",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "#6C00FF",
          "id": "b9975c13-f3bb-4bb1-ab84-4705626a60ce",
          "isComponent": true,
          "name": "Train validation split",
          "parents": [
            {
              "id": "d691b23d-85e0-4ff1-ab92-dacf4e4ed3bc",
              "name": "Create dataframe"
            }
          ]
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "df_X = df.drop(\"target\", axis=1)\n",
        "df_y = df[\"target\"]\n",
        "train_X, valid_X, train_y, valid_y = train_test_split(\n",
        "    df_X, df_y, test_size=test_size, random_state=random_state, stratify=df_y\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1e9bbd7e-9263-4c6d-b9a4-93f5cf471558",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "152d97bd-c155-4151-abc5-dbe4056e06b2",
          "isComponent": true,
          "name": "Define_Train model",
          "parents": [
            {
              "id": "b9975c13-f3bb-4bb1-ab84-4705626a60ce",
              "name": "Train validation split"
            }
          ]
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "model = LogisticRegression(random_state=random_state)\n",
        "model.fit(train_X, train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bd571ac7-6ab9-4176-a3b8-52878de06575",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "6c9887d5-7afd-4dd8-bd22-7a54905d7c49",
          "isComponent": true,
          "name": "Check model accuracy",
          "parents": [
            {
              "id": "152d97bd-c155-4151-abc5-dbe4056e06b2",
              "name": "Define_Train model"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": ["Accuracy : 0.9333333333333333\n"]
        }
      ],
      "source": [
        "pred = model.predict(valid_X)\n",
        "print(f\"Accuracy : {accuracy_score(valid_y, pred)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ebd18b9-bf90-492a-99b5-aefa749f739e",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "f6d4ef88-e610-48eb-b7ad-0203d2dfdb9c",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "canvas": {
      "colorPalette": [
        "inherit",
        "inherit",
        "inherit",
        "inherit",
        "inherit",
        "inherit",
        "inherit",
        "inherit",
        "inherit",
        "inherit"
      ],
      "parameters": [
        {
          "name": "test_size",
          "type": "float",
          "value": "0.3"
        },
        {
          "name": "random_state",
          "type": "int",
          "value": "42"
        }
      ],
      "version": "1.0"
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
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