leafTypeTREES.ipynb

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      "name": "leafTypeTREES.ipynb",
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        "<a href=\"https://colab.research.google.com/github/divyansh-tripathi7/ml_ps/blob/main/leafTypeTREES.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "execution_count": 1,
      "metadata": {
        "id": "N56ViD4QRo6x"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris \n",
        "import pandas as pd \n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_iris()\n",
        "print(dataset.data)\n",
        "print(dataset.target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5exZ5hMZrCM",
        "outputId": "2e0799fa-9871-4c53-c13f-c35747a07e42"
      },
      "execution_count": 2,
      "outputs": [
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            " [7.2 3.  5.8 1.6]\n",
            " [7.4 2.8 6.1 1.9]\n",
            " [7.9 3.8 6.4 2. ]\n",
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            " [7.7 3.  6.1 2.3]\n",
            " [6.3 3.4 5.6 2.4]\n",
            " [6.4 3.1 5.5 1.8]\n",
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            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n"
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      "source": [
        "X = pd.DataFrame(dataset.data, columns = dataset.feature_names)"
      ],
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        "id": "60zE8N5gZ6FA"
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      "execution_count": 4,
      "outputs": []
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              "      <th>sepal length (cm)</th>\n",
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              "      <td>1.9</td>\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",
              "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",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = dataset.target"
      ],
      "metadata": {
        "id": "3EXCP8mibBc8"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split \n",
        "X_train, X_test , Y_train, Y_test = train_test_split(X,Y , test_size= 0.25 , random_state =42)"
      ],
      "metadata": {
        "id": "jbOYYPkTaRoT"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6vfAlxga3Na",
        "outputId": "90771e78-4f2a-4c2d-e156-fbb5da6ac8a7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(112, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# find best max_depth value \n",
        "accuracy = []\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "for i in range(1,10):\n",
        "  model = DecisionTreeClassifier(max_depth = i , random_state= 42)\n",
        "  model.fit(X_train , Y_train)\n",
        "  pred = model.predict(X_test)\n",
        "  score = accuracy_score(Y_test, pred)\n",
        "  accuracy.append(score)\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(range(1,10), accuracy , color ='red', marker = 'o', markerfacecolor= 'green', markersize=11)\n",
        "plt.title(' finding best max_depth')\n",
        "plt.xlabel('pred')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "zh7agy4tbKhL",
        "outputId": "45a1e5c4-2104-41ab-9244-0228f0226fad"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'pred')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model = DecisionTreeClassifier(criterion = 'entropy', max_depth =3, random_state=42)\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KbJYnxzVcu2g",
        "outputId": "73b5068b-9045-4a3f-e004-5af36fc26cbf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "# print(np.concatenate(()))"
      ],
      "metadata": {
        "id": "a3IAd_ggdbSF"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(\"accuracy score of model is {0}%\".format(accuracy_score(Y_test, Y_pred)*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OGyrSrZZdq33",
        "outputId": "ed255ca9-d58b-4b9d-8ad6-8a7f630e58a4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score of model is 97.36842105263158%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "DmB9O9-teBYV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}