Task_4_Decision_Tree.ipynb

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  "metadata": {
    "colab": {
      "name": "Task#4 Decision Tree.ipynb",
      "provenance": []
    },
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMF9KN7vBLoz",
        "colab_type": "text"
      },
      "source": [
        "### **Task-4 To explore Decision Tree Algorithm**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0taePaxShXrd",
        "colab_type": "text"
      },
      "source": [
        "###**Problem Statement - Understanding Decision Tree Workflow**\n",
        "*For the given ‘Iris’ dataset, create the Decision Tree classifier and\n",
        "visualize it graphically. The purpose is if we feed any new data to this\n",
        "classifier, it would be able to predict the right class accordingly.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpvZuiH5R7we",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "3e0b4352-2ded-4115-80b0-f7e690efb137"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1nEJFWUXGod",
        "colab_type": "text"
      },
      "source": [
        "###**Loading Iris data-set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JRttm5KXLC8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "51896bc8-774b-4588-f8ae-71b1ef722fd4"
      },
      "source": [
        "from sklearn import datasets\n",
        "iris=datasets.load_iris()\n",
        "data=pd.DataFrame(iris.data,columns=iris.feature_names)\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\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"
            ]
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          "metadata": {
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          "execution_count": 3
        }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWfjs-hoXrx2",
        "colab_type": "text"
      },
      "source": [
        "Checking for Anomalies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnscPaHgXx6s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "11d60b7f-63a1-44ef-f3b0-85889a501bf2"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal length (cm)    0\n",
              "sepal width (cm)     0\n",
              "petal length (cm)    0\n",
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              "dtype: int64"
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          "metadata": {
            "tags": []
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          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nt_-5XIfX4np",
        "colab_type": "text"
      },
      "source": [
        "Fixing Target Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olsINcWOX95t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "db0fbe56-69fa-4960-cc10-3704a6dc7fee"
      },
      "source": [
        "y=iris.target\n",
        "y\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([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,\n",
              "       0, 0, 0, 0, 0, 0, 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,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       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, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
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          "metadata": {
            "tags": []
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          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qOSPLO__YIBi",
        "colab_type": "text"
      },
      "source": [
        "###**Creating Decison Tree Classifier Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Lf38OY2YQFk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import tree\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_valid,y_train,y_valid=train_test_split(data,y,train_size=0.8,test_size=0.2,random_state=0)\n",
        "clf=DecisionTreeClassifier(random_state=0)\n",
        "model=clf.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWkzhdsNZCGj",
        "colab_type": "text"
      },
      "source": [
        "###**Visualizing D-Tree Rules of the Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeiwOnGJZGr9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "cd7f3c77-cb98-416b-90c9-a2fc26ff3cdd"
      },
      "source": [
        "text_representation = tree.export_text(clf)\n",
        "print(text_representation)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "|--- feature_3 <= 0.80\n",
            "|   |--- class: 0\n",
            "|--- feature_3 >  0.80\n",
            "|   |--- feature_3 <= 1.75\n",
            "|   |   |--- feature_2 <= 4.95\n",
            "|   |   |   |--- feature_3 <= 1.65\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |   |--- feature_3 >  1.65\n",
            "|   |   |   |   |--- class: 2\n",
            "|   |   |--- feature_2 >  4.95\n",
            "|   |   |   |--- feature_3 <= 1.55\n",
            "|   |   |   |   |--- class: 2\n",
            "|   |   |   |--- feature_3 >  1.55\n",
            "|   |   |   |   |--- feature_2 <= 5.45\n",
            "|   |   |   |   |   |--- class: 1\n",
            "|   |   |   |   |--- feature_2 >  5.45\n",
            "|   |   |   |   |   |--- class: 2\n",
            "|   |--- feature_3 >  1.75\n",
            "|   |   |--- feature_2 <= 4.85\n",
            "|   |   |   |--- feature_1 <= 3.10\n",
            "|   |   |   |   |--- class: 2\n",
            "|   |   |   |--- feature_1 >  3.10\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |--- feature_2 >  4.85\n",
            "|   |   |   |--- class: 2\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84seF8VPZOok",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "da04dda1-8e49-42fa-b77b-745a9a1a5f93"
      },
      "source": [
        "pip install dtreeviz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting dtreeviz\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/86/49/4c5367b859deb0436c5383295c16b802900992b399df66c13a3b2f99e056/dtreeviz-1.0.tar.gz (42kB)\n",
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            "\u001b[?25hRequirement already satisfied: graphviz>=0.9 in /usr/local/lib/python3.6/dist-packages (from dtreeviz) (0.10.1)\n",
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            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from dtreeviz) (3.2.2)\n",
            "Collecting colour\n",
            "  Downloading https://files.pythonhosted.org/packages/74/46/e81907704ab203206769dee1385dc77e1407576ff8f50a0681d0a6b541be/colour-0.1.5-py2.py3-none-any.whl\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.6/dist-packages (from dtreeviz) (0.90)\n",
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            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.6/dist-packages (from pytest->dtreeviz) (1.4.0)\n",
            "Building wheels for collected packages: dtreeviz\n",
            "  Building wheel for dtreeviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dtreeviz: filename=dtreeviz-1.0-cp36-none-any.whl size=47218 sha256=b2638cd8cea5b5c9cda6ef92d58483fa8eee5fd7958a52fc275067fd9580f573\n",
            "  Stored in directory: /root/.cache/pip/wheels/54/cc/42/32f4f8708750bd4d91bc8ff3df35d8e1f4b19180bf5a62b82b\n",
            "Successfully built dtreeviz\n",
            "Installing collected packages: colour, dtreeviz\n",
            "Successfully installed colour-0.1.5 dtreeviz-1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFBzg7SHaE70",
        "colab_type": "text"
      },
      "source": [
        "Visualizing our decision tree using dtreeviz"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QB3tk00MZdm1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "0e6595ee-630c-4d27-af54-80cbba6466f2"
      },
      "source": [
        "from dtreeviz.trees import dtreeviz\n",
        "\n",
        "viz = dtreeviz(clf, X_train, y_train,\n",
        "                target_name=\"target\",\n",
        "                feature_names=iris.feature_names,\n",
        "                class_names=list(iris.target_names))\n",
        "\n",
        "viz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHHs2odPawD3",
        "colab_type": "text"
      },
      "source": [
        "###**Visualizing D-Tree using Graphviz**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0O7Akv4ap8h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "outputId": "2bc1646e-586b-463e-9917-f572dc5f2972"
      },
      "source": [
        "import graphviz\n",
        "# DOT data\n",
        "dot_data = tree.export_graphviz(clf, out_file=None, \n",
        "                                feature_names=iris.feature_names,  \n",
        "                                class_names=iris.target_names,\n",
        "                                filled=True)\n",
        "\n",
        "# Draw graph\n",
        "graph = graphviz.Source(dot_data, format=\"png\") \n",
        "graph"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f5e923824a8>"
            ],
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnXFlDzbbFHS",
        "colab_type": "text"
      },
      "source": [
        "###**Testing our Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiKBAnLTbJK_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f0712865-e10d-4ca4-f68f-c1b49df9aefc"
      },
      "source": [
        "predictions=model.predict(X_valid)\n",
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1,\n",
              "       0, 0, 2, 0, 0, 1, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1mOnbmTbVI2",
        "colab_type": "text"
      },
      "source": [
        "###**Evaluating Our Results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHEe2BPLbbHV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4dd0b019-20a2-43ec-9ad2-4b544125ee2b"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "MAE=mean_absolute_error(predictions,y_valid)\n",
        "print(\"Mean Absolute Error\",MAE)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3Ofns1Wb0gl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "aa25b135-e79a-4132-b0ba-f4b4c8140ea9"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report\n",
        "result=confusion_matrix(predictions,y_valid)\n",
        "print(\"Confusion Matrix is \")\n",
        "result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix is \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11,  0,  0],\n",
              "       [ 0, 13,  0],\n",
              "       [ 0,  0,  6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nE3KNiNZcPQY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "60c39bcb-6bd7-4f66-c02d-6840ae811e14"
      },
      "source": [
        "accuracy=accuracy_score(predictions,y_valid)\n",
        "print(\"Accuracy Score is\",accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy Score is 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPA_9HTMccB5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "997bfdc1-4d40-4b27-df81-93415e3f5f15"
      },
      "source": [
        "print(\"Report:\",classification_report(predictions,y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Report:               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        11\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00         6\n",
            "\n",
            "    accuracy                           1.00        30\n",
            "   macro avg       1.00      1.00      1.00        30\n",
            "weighted avg       1.00      1.00      1.00        30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvNEcKLzhpcw",
        "colab_type": "text"
      },
      "source": [
        "###**We're Done:)**"
      ]
    }
  ]
}