HeartDisease_DecsionTree_RandomForest.ipynb

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      "name": "HeartDisease_DecsionTree_RandomForest.ipynb",
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        "<body>\n",
        "<div align='center'>\n",
        "<h1>Decision Tree and Random Forests</h1>\n",
        "<h4>using the dataset on Heart Disease in Cleveland, donated to UCI Repository.</h4></div>\n",
        "<p> The column 'target' means N for no presence and Y fore presence in levels 1,2,3,4.\n",
        "<p>The information on the 14 attribute:\n",
        "<li>age--in years\n",
        "<li>sex--(1 = male; 0 = female)\n",
        "<li>cp--chest pain type (1: typical angina, 2: atypical angina, 3: non-anginal, pain 4: asymptomatic)\n",
        "<li>trestbps -- resting blood pressure\n",
        "<li>chol--serum cholesterol in mg/dl\n",
        "<li>fbs--fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
        "<li>restecg--resting ecg (electrocardiographic) results\n",
        "<li>thalach--maximum heart rate achieved\n",
        "<li>exang--exercise induced angina (1 = yes; 0 = no)\n",
        "<li>oldpeak--ST depression induced by exercise relative to rest\n",
        "<li>slope--the slope of the peak exercise ST segment (1: upsloping, 2: flat, 3: downsloping)\n",
        "<li>ca-- number of major vessels (0-3) colored by flourosopy\n",
        "<li>thal-- 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
        "<li>the predicted attribute (0, 1, 2, 3 4) -- In the processed dataset, this one is added as a new column 'target' with 'N' for 0 and 'Y' for 1,2,3 & 4.\n",
        "</body>\n"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Machine_Learning_Material/S_13_Trees/HD_Cleveland_Data_Clean.csv')"
      ],
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        "id": "3x9WkYNGPfOV"
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        "df.head()"
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            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slop  \\\n",
              "0   63    1   1       145   233    1        2      150      0      2.3     3   \n",
              "1   67    1   4       160   286    0        2      108      1      1.5     2   \n",
              "2   67    1   4       120   229    0        2      129      1      2.6     2   \n",
              "3   37    1   3       130   250    0        0      187      0      3.5     3   \n",
              "4   41    0   2       130   204    0        2      172      0      1.4     1   \n",
              "\n",
              "   ca  thal target  \n",
              "0   0     6      N  \n",
              "1   3     3      Y  \n",
              "2   2     7      Y  \n",
              "3   0     3      N  \n",
              "4   0     3      N  "
            ]
          },
          "metadata": {},
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      "cell_type": "code",
      "source": [
        "print('Null', df.isnull().value_counts())\n",
        "print('\\nNaN', df.isna().value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "HiFhVxX4P8pI",
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      },
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Null age    sex    cp     trestbps  chol   fbs    restecg  thalach  exang  oldpeak  slop   ca     thal   target\n",
            "False  False  False  False     False  False  False    False    False  False    False  False  False  False     297\n",
            "dtype: int64\n",
            "\n",
            "NaN age    sex    cp     trestbps  chol   fbs    restecg  thalach  exang  oldpeak  slop   ca     thal   target\n",
            "False  False  False  False     False  False  False    False    False  False    False  False  False  False     297\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "sns.heatmap(df.corr(), cmap='icefire')"
      ],
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        "id": "JtIf4XhYQNji",
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      "execution_count": 6,
      "outputs": [
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e0d7dfb10>"
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          "execution_count": 6
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\n",
            "text/plain": [
              "<Figure size 1008x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,7))\n",
        "sns.countplot(data=df, x='age', hue='target', palette='seismic')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "PuoNAb3AQfvK",
        "outputId": "64476fbb-f5fd-4e18-bbf1-0f5adf2f1148"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e0a6d9b90>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1=male 0=female\n",
        "sns.jointplot(data=df, x='sex', y='age',hue='target', palette='seismic')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "odQBYFvcR3QW",
        "outputId": "841ecb6b-1433-46d1-c4ec-49c2dbe5bab3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f9e0a0e0390>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<body>\n",
        "Higher frequency in men over 30 years old and womans between 55-65 years old.\n",
        "</body>"
      ],
      "metadata": {
        "id": "M7VLAP_9WXfR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.jointplot(data=df, x='age', y='chol',hue='sex',palette='seismic')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "6anJ6FFfTdLx",
        "outputId": "c97677bd-ad83-480b-f1db-a6c82aedceee"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f9e07f1d9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h4>Decision Tree</h4>"
      ],
      "metadata": {
        "id": "KAhjlcvvvplQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('target', axis=1)\n",
        "y = df['target']"
      ],
      "metadata": {
        "id": "rS9ktHZUXlgn"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "LGk3Wvlfv9MK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.33, random_state=42)"
      ],
      "metadata": {
        "id": "EKJ1oJpzwgOx"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "fnNn26DMwof4"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dtree = DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "YZxFv502xdtU"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dtree.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4w9Q26nxgWe",
        "outputId": "70159488-801d-417d-9219-32c8f4c0a589"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "from six import StringIO\n",
        "from sklearn.tree import export_graphviz"
      ],
      "metadata": {
        "id": "XjE-gAJgxjQz"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pydot"
      ],
      "metadata": {
        "id": "B8Z0G9K0yghw"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pydot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65CLrWjFxxbZ",
        "outputId": "7cc57623-fbb3-4d26-a9ba-b94b06bc7d09"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pydot in /usr/local/lib/python3.7/dist-packages (1.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.1.4 in /usr/local/lib/python3.7/dist-packages (from pydot) (3.0.7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features=df.columns[:-1]\n",
        "dot_data = StringIO()\n",
        "export_graphviz(dtree, out_file=dot_data, \n",
        "                feature_names=features,filled=True,rounded=True)\n",
        "\n",
        "graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
        "Image(graph[0].create_png())"
      ],
      "metadata": {
        "id": "5-o5GtusyS0f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = dtree.predict(X_test)"
      ],
      "metadata": {
        "id": "SpaAt-vGycpJ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix"
      ],
      "metadata": {
        "id": "q9wtNKXs3f2n"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, r2_score\n"
      ],
      "metadata": {
        "id": "G8yNCl2p4THw"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, classes=None, title='Confusion matrix'):\n",
        "    \"\"\"Plots a confusion matrix.\"\"\"\n",
        "    if classes is not None:\n",
        "        sns.heatmap(cm, xticklabels=classes, yticklabels=classes, \n",
        "                    vmin=0., vmax=1., annot=True, annot_kws={'size':50})\n",
        "    else:\n",
        "        sns.heatmap(cm, vmin=0., vmax=1.)\n",
        "    plt.title(title)\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "gdP7ZK1R3h8c"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, pred)\n",
        "cm_norm = cm/cm.sum(axis=1)[:, np.newaxis]\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cm_norm, classes=dtree.classes_,\n",
        "                      title='\\nTraining confusion\\n\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "bVznmshl3tUJ",
        "outputId": "7fee2b58-2b67-4840-f95b-1ef683d4cddf"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, column in enumerate(df.drop('target', axis=1)):\n",
        "    print('Importance of feature {}:, {:.3f}'.format(column, dtree.feature_importances_[i]))\n",
        "    \n",
        "    fi = pd.DataFrame({'Variable': [column],\n",
        "                       'Feature Importance Score': [dtree.feature_importances_[i]]})\n",
        "    \n",
        "    try: #the others values\n",
        "        final_fi = pd.concat([final_fi,fi], ignore_index = True)\n",
        "    except: #the first value\n",
        "        final_fi = fi\n",
        "        \n",
        "        \n",
        "# Ordering the data\n",
        "final_fi = final_fi.sort_values('Feature Importance Score', ascending = False).reset_index()            \n",
        "final_fi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "1mQPQMGk3zBz",
        "outputId": "832f9532-8067-435e-9795-daa6ea693051"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Importance of feature age:, 0.156\n",
            "Importance of feature sex:, 0.000\n",
            "Importance of feature cp:, 0.127\n",
            "Importance of feature trestbps:, 0.076\n",
            "Importance of feature chol:, 0.093\n",
            "Importance of feature fbs:, 0.010\n",
            "Importance of feature restecg:, 0.005\n",
            "Importance of feature thalach:, 0.040\n",
            "Importance of feature exang:, 0.000\n",
            "Importance of feature oldpeak:, 0.071\n",
            "Importance of feature slop:, 0.000\n",
            "Importance of feature ca:, 0.322\n",
            "Importance of feature thal:, 0.101\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f59b61c7-ab8d-450a-921a-75ae61ab92b1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>index</th>\n",
              "      <th>Variable</th>\n",
              "      <th>Feature Importance Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
              "      <td>ca</td>\n",
              "      <td>0.322404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>age</td>\n",
              "      <td>0.156383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>cp</td>\n",
              "      <td>0.126688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>thal</td>\n",
              "      <td>0.100949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>chol</td>\n",
              "      <td>0.092812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "      <td>trestbps</td>\n",
              "      <td>0.075536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9</td>\n",
              "      <td>oldpeak</td>\n",
              "      <td>0.070810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>thalach</td>\n",
              "      <td>0.039570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5</td>\n",
              "      <td>fbs</td>\n",
              "      <td>0.009522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>6</td>\n",
              "      <td>restecg</td>\n",
              "      <td>0.005326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>sex</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8</td>\n",
              "      <td>exang</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>10</td>\n",
              "      <td>slop</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    index  Variable  Feature Importance Score\n",
              "0      11        ca                  0.322404\n",
              "1       0       age                  0.156383\n",
              "2       2        cp                  0.126688\n",
              "3      12      thal                  0.100949\n",
              "4       4      chol                  0.092812\n",
              "5       3  trestbps                  0.075536\n",
              "6       9   oldpeak                  0.070810\n",
              "7       7   thalach                  0.039570\n",
              "8       5       fbs                  0.009522\n",
              "9       6   restecg                  0.005326\n",
              "10      1       sex                  0.000000\n",
              "11      8     exang                  0.000000\n",
              "12     10      slop                  0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#I prefer this visualization \n",
        "from sklearn import tree\n",
        "import graphviz \n",
        "\n",
        "dot_data = tree.export_graphviz(dtree, out_file=None, \n",
        "    feature_names=df.drop('target', axis=1).columns,    \n",
        "    class_names=df['target'].unique().astype(str),  \n",
        "    filled=True, rounded=True,  \n",
        "    special_characters=True)\n",
        "graph = graphviz.Source(dot_data)\n",
        "graph\n",
        "graph.render('HeartDisease_decisionTree.svg', format='svg', directory='output').replace('\\\\', '/')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "wKV4BF9B48YZ",
        "outputId": "362c6ad4-0d60-4401-f43e-d23dfb3ba974"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'output/HeartDisease_decisionTree.svg.svg'"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYolQLSX5VI8",
        "outputId": "be3ec6b4-bdf0-4c0c-d9a7-3586596be90a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.72      0.79      0.76        53\n",
            "           Y       0.73      0.65      0.69        46\n",
            "\n",
            "    accuracy                           0.73        99\n",
            "   macro avg       0.73      0.72      0.72        99\n",
            "weighted avg       0.73      0.73      0.73        99\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h4>Let's see how much the model improves with Random Forest</h4>"
      ],
      "metadata": {
        "id": "3CPphMN37Myq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "2uP-rtZD6pAI"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rforest = RandomForestClassifier(n_estimators=100)\n",
        "rforest.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PH4esS6B8Sld",
        "outputId": "932acdc0-4c25-4dac-8e74-83cbd1ce11f5"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_pred = rforest.predict(X_test)"
      ],
      "metadata": {
        "id": "yribGaJy8fZ-"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i, column in enumerate(df.drop('target', axis=1)):\n",
        "    print('Importance of feature {}:, {:.3f}'.format(column, rforest.feature_importances_[i]))\n",
        "    \n",
        "    fi = pd.DataFrame({'Variable': [column],\n",
        "                       'Feature Importance Score': [rforest.feature_importances_[i]]})\n",
        "    \n",
        "    try: #the others values\n",
        "        final_fi = pd.concat([final_fi,fi], ignore_index = True)\n",
        "    except: #the first value\n",
        "        final_fi = fi\n",
        "        \n",
        "        \n",
        "# Ordering the data\n",
        "final_fi = final_fi.sort_values('Feature Importance Score', ascending = False).reset_index()            \n",
        "final_fi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nsvdn8Qv8mQU",
        "outputId": "27554c19-c454-42fa-ff77-2eb7ad6ea529"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Importance of feature age:, 0.102\n",
            "Importance of feature sex:, 0.033\n",
            "Importance of feature cp:, 0.121\n",
            "Importance of feature trestbps:, 0.076\n",
            "Importance of feature chol:, 0.096\n",
            "Importance of feature fbs:, 0.011\n",
            "Importance of feature restecg:, 0.017\n",
            "Importance of feature thalach:, 0.113\n",
            "Importance of feature exang:, 0.030\n",
            "Importance of feature oldpeak:, 0.104\n",
            "Importance of feature slop:, 0.020\n",
            "Importance of feature ca:, 0.177\n",
            "Importance of feature thal:, 0.101\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>level_0</th>\n",
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              "      <th>Variable</th>\n",
              "      <th>Feature Importance Score</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <td>ca</td>\n",
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              "      <td>ca</td>\n",
              "      <td>0.176517</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <th>6</th>\n",
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              "      <td>0.103757</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>age</td>\n",
              "      <td>0.101774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>thal</td>\n",
              "      <td>0.100957</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>12.0</td>\n",
              "      <td>thal</td>\n",
              "      <td>0.100949</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>chol</td>\n",
              "      <td>0.096343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>chol</td>\n",
              "      <td>0.092812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>trestbps</td>\n",
              "      <td>0.075933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>trestbps</td>\n",
              "      <td>0.075536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>6</td>\n",
              "      <td>9.0</td>\n",
              "      <td>oldpeak</td>\n",
              "      <td>0.070810</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>thalach</td>\n",
              "      <td>0.039570</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
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              "      <td>0.032878</td>\n",
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              "      <th>17</th>\n",
              "      <td>21</td>\n",
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              "      <td>23</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.019915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>restecg</td>\n",
              "      <td>0.017039</td>\n",
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              "    [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-cdeff6f7-27d3-4188-ae74-28c9ce2dc86a 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-cdeff6f7-27d3-4188-ae74-28c9ce2dc86a');\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",
              "  "
            ],
            "text/plain": [
              "    level_0  index  Variable  Feature Importance Score\n",
              "0         0   11.0        ca                  0.322404\n",
              "1        24    NaN        ca                  0.176517\n",
              "2         1    0.0       age                  0.156383\n",
              "3         2    2.0        cp                  0.126688\n",
              "4        15    NaN        cp                  0.121253\n",
              "5        20    NaN   thalach                  0.113393\n",
              "6        22    NaN   oldpeak                  0.103757\n",
              "7        13    NaN       age                  0.101774\n",
              "8        25    NaN      thal                  0.100957\n",
              "9         3   12.0      thal                  0.100949\n",
              "10       17    NaN      chol                  0.096343\n",
              "11        4    4.0      chol                  0.092812\n",
              "12       16    NaN  trestbps                  0.075933\n",
              "13        5    3.0  trestbps                  0.075536\n",
              "14        6    9.0   oldpeak                  0.070810\n",
              "15        7    7.0   thalach                  0.039570\n",
              "16       14    NaN       sex                  0.032878\n",
              "17       21    NaN     exang                  0.029655\n",
              "18       23    NaN      slop                  0.019915\n",
              "19       19    NaN   restecg                  0.017039\n",
              "20       18    NaN       fbs                  0.010586\n",
              "21        8    5.0       fbs                  0.009522\n",
              "22        9    6.0   restecg                  0.005326\n",
              "23       11    8.0     exang                  0.000000\n",
              "24       12   10.0      slop                  0.000000\n",
              "25       10    1.0       sex                  0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,rf_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ouGAC5LZ85QP",
        "outputId": "0dffc154-eaf6-4b76-d726-17cc01a6b4bd"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.81      0.89      0.85        53\n",
            "           Y       0.85      0.76      0.80        46\n",
            "\n",
            "    accuracy                           0.83        99\n",
            "   macro avg       0.83      0.82      0.83        99\n",
            "weighted avg       0.83      0.83      0.83        99\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, rf_pred)\n",
        "cm_norm = cm/cm.sum(axis=1)[:, np.newaxis]\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cm_norm, classes=rforest.classes_,\n",
        "                      title='\\nTraining confusion\\n\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "W2cTtzio861l",
        "outputId": "4a143fd0-65a4-461d-9b80-5335b477b73b"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "K-4TRnrt9JCM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "AUrnr9Aq9Drd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}