HeartDisease.ipynb

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      "name": "HeartDisease.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/luigivendetta/heart_disease/blob/master/HeartDisease.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1oLpL4q0P50v",
        "colab_type": "text"
      },
      "source": [
        "# Heart Disease Project\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3W8TqXoP50y",
        "colab_type": "code",
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      "source": [
        "# data manipulation libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pickle\n",
        "# Model Libraries from sklearn\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "# Evaluation libraries from sklearn\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
        "from sklearn.metrics import plot_roc_curve, roc_auc_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmPQA92KP506",
        "colab_type": "text"
      },
      "source": [
        "# Data Dictionary \n",
        "\n",
        "\n",
        "* age age in years\n",
        "* sex(1 = male; 0 = female)\n",
        "* cp chest pain type\n",
        "* trestbps resting blood pressure (in mm Hg on admission to the hospital)\n",
        "* chol serum cholestoral in mg/dl\n",
        "* fbs (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
        "* restecg resting electrocardiographic results\n",
        "* thalach maximum heart rate achieved\n",
        "* exang exercise induced angina (1 = yes; 0 = no)\n",
        "* oldpeak ST depression induced by exercise relative to rest\n",
        "* slope the slope of the peak exercise ST segment\n",
        "* ca number of major vessels (0-3) colored by flourosopy\n",
        "* thal  3 = normal; 6 = fixed defect; 7 = reversable defect\n",
        "* target  1 or 0 "
      ]
    },
    {
      "cell_type": "code",
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          "height": 34
        },
        "outputId": "9a65cec2-1c86-4ca1-a3d9-086839f62dec"
      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/luigivendetta/tuning_hyperparams_classification_model/master/data/heart_disease.csv\")\n",
        "df.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
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    {
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        "id": "5Jkw8JiAP51D",
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        "outputId": "5ee6957c-0220-4c63-8a42-76d236662cc3"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>37</td>\n",
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              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
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        }
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        "outputId": "08bb0fb8-1f9c-4e1c-a835-721047944ff7"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
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              "  </thead>\n",
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              "      <td>57</td>\n",
              "      <td>0</td>\n",
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              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n",
              "300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n",
              "301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
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          "height": 68
        },
        "outputId": "d17a5503-c641-4bfd-8682-2b908c42e9ca"
      },
      "source": [
        "df[\"target\"].value_counts()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKMZ5dfAP51Y",
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          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "outputId": "347dc6a9-ef97-4d90-c375-423e807a57e1"
      },
      "source": [
        "df[\"target\"].value_counts().plot(kind = \"bar\",color=[\"red\", \"blue\"]);"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cJalBhrskNchwl6QG/R8L4D+0KSMxOgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xc8oUuXBP51d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "166ed71a-bc04-4cc0-8a31-e39efec1fee1"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFd2fonYP51o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "4da2d958-155d-4522-aebb-2d6ca5f8b7a8"
      },
      "source": [
        "print(df.isna().sum())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "age         0\n",
            "sex         0\n",
            "cp          0\n",
            "trestbps    0\n",
            "chol        0\n",
            "fbs         0\n",
            "restecg     0\n",
            "thalach     0\n",
            "exang       0\n",
            "oldpeak     0\n",
            "slope       0\n",
            "ca          0\n",
            "thal        0\n",
            "target      0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Td_OiKZIP51w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "abf0f29f-9194-4465-ca55-728d58b374e0"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gw6g7so7P511",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "eef6a579-1b01-4d6f-e513-b968e48e7de2"
      },
      "source": [
        "pd.crosstab(df[\"target\"],df[\"sex\"])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>sex</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>72</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "sex      0    1\n",
              "target         \n",
              "0       24  114\n",
              "1       72   93"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxWIRjAjP518",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "15d3e369-59ef-4f00-d19a-1d61c49ab815"
      },
      "source": [
        "pd.crosstab(df[\"target\"],df[\"sex\"]).plot(kind = \"bar\", \n",
        "                                         color = [\"red\", \"blue\"], \n",
        "                                         figsize =(10,5), \n",
        "                                         title = \"Sex vs Heart Disease\", )\n",
        "plt.legend([\"Female\", \"Male\"])\n",
        "plt.xlabel(\"0 = No Heart Disease, 1 = Heart Disease\")\n",
        "plt.ylabel(\"Number of Persons\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Persons')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdgRQo58P52G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "17745ebc-d447-4616-fe83-47d7e768ac60"
      },
      "source": [
        "df.thalach.value_counts()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "162    11\n",
              "160     9\n",
              "163     9\n",
              "173     8\n",
              "152     8\n",
              "       ..\n",
              "129     1\n",
              "128     1\n",
              "127     1\n",
              "124     1\n",
              "71      1\n",
              "Name: thalach, Length: 91, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWeg9CavP52M",
        "colab_type": "text"
      },
      "source": [
        "# Heart Rate vs Age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8V25snYgP52N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1dbbee9b-03f1-42a3-99ce-9213bb5fa10b"
      },
      "source": [
        "fig , ax = plt.subplots(ncols=1,nrows=3, figsize = (15,18), sharex= True)\n",
        "ax[0].scatter(df.age[df.target == 1],\n",
        "              df.thalach[df.target == 1],\n",
        "             color = \"red\")\n",
        "ax[0].set(title = \"Age vs HeartRate for +ve\")\n",
        "ax[1].scatter(df.age[df.target == 0],\n",
        "              df.thalach[df.target == 0],\n",
        "              color = \"blue\")\n",
        "ax[1].set(title = \"Age vs HeartRate for -ve\")\n",
        "ax[2].scatter(df.age[df.target == 0],\n",
        "              df.thalach[df.target == 0],\n",
        "              color = \"blue\")\n",
        "ax[2].scatter(df.age[df.target == 1],\n",
        "              df.thalach[df.target == 1],\n",
        "             color = \"red\")\n",
        "ax[2].set(title = \"Age vs HeartRate for both +ve and -ve\");"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1296 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMopc70yP52S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "beab9c7f-2f64-4654-8e80-099192be0912"
      },
      "source": [
        "df[\"age\"].hist()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c304b5860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcT_ih4BP52Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5d491083-681a-4103-b221-9a6a079797aa"
      },
      "source": [
        "pd.crosstab(df.cp,df.target)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>target</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>104</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "target    0   1\n",
              "cp             \n",
              "0       104  39\n",
              "1         9  41\n",
              "2        18  69\n",
              "3         7  16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JvBwTVvP52e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "2a53b2c5-d271-41ec-b062-16c1b0af4583"
      },
      "source": [
        "pd.crosstab(df.cp,df.target).plot(kind=\"bar\", color = [\"green\", \"indigo\"])\n",
        "plt.xlabel(\"Chest Pain Types\")\n",
        "plt.legend([\"No Disease\", \"Disease\"]);\n",
        "plt.title(\"Chest Pain\")\n",
        "plt.ylabel(\"Number of Persons\");"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mnQfaKavP52k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "13afab8f-81bd-44da-bf0b-323816ef4879"
      },
      "source": [
        "# creating correlation matrix\n",
        "df.corr() #correlation matrix"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.098447</td>\n",
              "      <td>-0.068653</td>\n",
              "      <td>0.279351</td>\n",
              "      <td>0.213678</td>\n",
              "      <td>0.121308</td>\n",
              "      <td>-0.116211</td>\n",
              "      <td>-0.398522</td>\n",
              "      <td>0.096801</td>\n",
              "      <td>0.210013</td>\n",
              "      <td>-0.168814</td>\n",
              "      <td>0.276326</td>\n",
              "      <td>0.068001</td>\n",
              "      <td>-0.225439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>-0.098447</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.049353</td>\n",
              "      <td>-0.056769</td>\n",
              "      <td>-0.197912</td>\n",
              "      <td>0.045032</td>\n",
              "      <td>-0.058196</td>\n",
              "      <td>-0.044020</td>\n",
              "      <td>0.141664</td>\n",
              "      <td>0.096093</td>\n",
              "      <td>-0.030711</td>\n",
              "      <td>0.118261</td>\n",
              "      <td>0.210041</td>\n",
              "      <td>-0.280937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>-0.068653</td>\n",
              "      <td>-0.049353</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.047608</td>\n",
              "      <td>-0.076904</td>\n",
              "      <td>0.094444</td>\n",
              "      <td>0.044421</td>\n",
              "      <td>0.295762</td>\n",
              "      <td>-0.394280</td>\n",
              "      <td>-0.149230</td>\n",
              "      <td>0.119717</td>\n",
              "      <td>-0.181053</td>\n",
              "      <td>-0.161736</td>\n",
              "      <td>0.433798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0.279351</td>\n",
              "      <td>-0.056769</td>\n",
              "      <td>0.047608</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.123174</td>\n",
              "      <td>0.177531</td>\n",
              "      <td>-0.114103</td>\n",
              "      <td>-0.046698</td>\n",
              "      <td>0.067616</td>\n",
              "      <td>0.193216</td>\n",
              "      <td>-0.121475</td>\n",
              "      <td>0.101389</td>\n",
              "      <td>0.062210</td>\n",
              "      <td>-0.144931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0.213678</td>\n",
              "      <td>-0.197912</td>\n",
              "      <td>-0.076904</td>\n",
              "      <td>0.123174</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013294</td>\n",
              "      <td>-0.151040</td>\n",
              "      <td>-0.009940</td>\n",
              "      <td>0.067023</td>\n",
              "      <td>0.053952</td>\n",
              "      <td>-0.004038</td>\n",
              "      <td>0.070511</td>\n",
              "      <td>0.098803</td>\n",
              "      <td>-0.085239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>0.121308</td>\n",
              "      <td>0.045032</td>\n",
              "      <td>0.094444</td>\n",
              "      <td>0.177531</td>\n",
              "      <td>0.013294</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.084189</td>\n",
              "      <td>-0.008567</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>-0.059894</td>\n",
              "      <td>0.137979</td>\n",
              "      <td>-0.032019</td>\n",
              "      <td>-0.028046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>-0.116211</td>\n",
              "      <td>-0.058196</td>\n",
              "      <td>0.044421</td>\n",
              "      <td>-0.114103</td>\n",
              "      <td>-0.151040</td>\n",
              "      <td>-0.084189</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.044123</td>\n",
              "      <td>-0.070733</td>\n",
              "      <td>-0.058770</td>\n",
              "      <td>0.093045</td>\n",
              "      <td>-0.072042</td>\n",
              "      <td>-0.011981</td>\n",
              "      <td>0.137230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>-0.398522</td>\n",
              "      <td>-0.044020</td>\n",
              "      <td>0.295762</td>\n",
              "      <td>-0.046698</td>\n",
              "      <td>-0.009940</td>\n",
              "      <td>-0.008567</td>\n",
              "      <td>0.044123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.378812</td>\n",
              "      <td>-0.344187</td>\n",
              "      <td>0.386784</td>\n",
              "      <td>-0.213177</td>\n",
              "      <td>-0.096439</td>\n",
              "      <td>0.421741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0.096801</td>\n",
              "      <td>0.141664</td>\n",
              "      <td>-0.394280</td>\n",
              "      <td>0.067616</td>\n",
              "      <td>0.067023</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>-0.070733</td>\n",
              "      <td>-0.378812</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>-0.257748</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.206754</td>\n",
              "      <td>-0.436757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>0.210013</td>\n",
              "      <td>0.096093</td>\n",
              "      <td>-0.149230</td>\n",
              "      <td>0.193216</td>\n",
              "      <td>0.053952</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>-0.058770</td>\n",
              "      <td>-0.344187</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.577537</td>\n",
              "      <td>0.222682</td>\n",
              "      <td>0.210244</td>\n",
              "      <td>-0.430696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>-0.168814</td>\n",
              "      <td>-0.030711</td>\n",
              "      <td>0.119717</td>\n",
              "      <td>-0.121475</td>\n",
              "      <td>-0.004038</td>\n",
              "      <td>-0.059894</td>\n",
              "      <td>0.093045</td>\n",
              "      <td>0.386784</td>\n",
              "      <td>-0.257748</td>\n",
              "      <td>-0.577537</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.080155</td>\n",
              "      <td>-0.104764</td>\n",
              "      <td>0.345877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>0.276326</td>\n",
              "      <td>0.118261</td>\n",
              "      <td>-0.181053</td>\n",
              "      <td>0.101389</td>\n",
              "      <td>0.070511</td>\n",
              "      <td>0.137979</td>\n",
              "      <td>-0.072042</td>\n",
              "      <td>-0.213177</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.222682</td>\n",
              "      <td>-0.080155</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.151832</td>\n",
              "      <td>-0.391724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>0.068001</td>\n",
              "      <td>0.210041</td>\n",
              "      <td>-0.161736</td>\n",
              "      <td>0.062210</td>\n",
              "      <td>0.098803</td>\n",
              "      <td>-0.032019</td>\n",
              "      <td>-0.011981</td>\n",
              "      <td>-0.096439</td>\n",
              "      <td>0.206754</td>\n",
              "      <td>0.210244</td>\n",
              "      <td>-0.104764</td>\n",
              "      <td>0.151832</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.344029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>-0.225439</td>\n",
              "      <td>-0.280937</td>\n",
              "      <td>0.433798</td>\n",
              "      <td>-0.144931</td>\n",
              "      <td>-0.085239</td>\n",
              "      <td>-0.028046</td>\n",
              "      <td>0.137230</td>\n",
              "      <td>0.421741</td>\n",
              "      <td>-0.436757</td>\n",
              "      <td>-0.430696</td>\n",
              "      <td>0.345877</td>\n",
              "      <td>-0.391724</td>\n",
              "      <td>-0.344029</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age       sex        cp  ...        ca      thal    target\n",
              "age       1.000000 -0.098447 -0.068653  ...  0.276326  0.068001 -0.225439\n",
              "sex      -0.098447  1.000000 -0.049353  ...  0.118261  0.210041 -0.280937\n",
              "cp       -0.068653 -0.049353  1.000000  ... -0.181053 -0.161736  0.433798\n",
              "trestbps  0.279351 -0.056769  0.047608  ...  0.101389  0.062210 -0.144931\n",
              "chol      0.213678 -0.197912 -0.076904  ...  0.070511  0.098803 -0.085239\n",
              "fbs       0.121308  0.045032  0.094444  ...  0.137979 -0.032019 -0.028046\n",
              "restecg  -0.116211 -0.058196  0.044421  ... -0.072042 -0.011981  0.137230\n",
              "thalach  -0.398522 -0.044020  0.295762  ... -0.213177 -0.096439  0.421741\n",
              "exang     0.096801  0.141664 -0.394280  ...  0.115739  0.206754 -0.436757\n",
              "oldpeak   0.210013  0.096093 -0.149230  ...  0.222682  0.210244 -0.430696\n",
              "slope    -0.168814 -0.030711  0.119717  ... -0.080155 -0.104764  0.345877\n",
              "ca        0.276326  0.118261 -0.181053  ...  1.000000  0.151832 -0.391724\n",
              "thal      0.068001  0.210041 -0.161736  ...  0.151832  1.000000 -0.344029\n",
              "target   -0.225439 -0.280937  0.433798  ... -0.391724 -0.344029  1.000000\n",
              "\n",
              "[14 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vFwnwnFP52p",
        "colab_type": "text"
      },
      "source": [
        "Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, icefire, icefire_r, inferno, inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, vlag, vlag_r, winter, winter_r"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pm30PlisP52q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "680ce3a1-8ce9-4516-fc9a-d025427ad10b"
      },
      "source": [
        "fig, ax = plt.subplots(figsize = (10,8))\n",
        "ax = sns.heatmap(df.corr(),\n",
        "                annot=True,\n",
        "                fmt= \".2f\",\n",
        "                cmap=\"gnuplot2\",\n",
        "                linewidths= 0.5,\n",
        "                linecolor=\"w\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73DPMXalP52u",
        "colab_type": "text"
      },
      "source": [
        "# Modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fiyuMDDWP52v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "692c668d-c2f5-4c0b-90ad-0019dddf8928"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qV5Q3wNHP522",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# split into feature and label\n",
        "X = df.drop(\"target\", axis = 1)\n",
        "Y = df[\"target\"]\n",
        "\n",
        "# split into train and test\n",
        "np.random.seed(61)\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZsW28RwP529",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# model dictionary\n",
        "model = {\"Logistic Regression\": LogisticRegression(),\n",
        "        \"KNN\": KNeighborsClassifier(),\n",
        "        \"Random Forest\": RandomForestClassifier()}\n",
        "# fit and score\n",
        "def fit_score(models, X_train, X_test, Y_train, Y_test):\n",
        "    \"\"\"\n",
        "    Info --> fits and score different models:\n",
        "    model: dict of models to be fitted\n",
        "    X_train: train data(feature)\n",
        "    X_test: test data(feature)\n",
        "    Y_train: train data(label)\n",
        "    Y_test: test data(label)\n",
        "    \"\"\"\n",
        "    np.random.seed(61)\n",
        "    model_scores =  {}\n",
        "    for name, model in models.items():\n",
        "        model.fit(X_train, Y_train) #fitting the models\n",
        "        model_scores[name] = model.score(X_test, Y_test) #appending in dictionary\n",
        "    return model_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gfb2amOP53C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ad7e8e25-9aa5-445d-d815-59baf453e4a2"
      },
      "source": [
        "x = fit_score(model, X_train, X_test, Y_train, Y_test)\n",
        "x"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'KNN': 0.6885245901639344,\n",
              " 'Logistic Regression': 0.8524590163934426,\n",
              " 'Random Forest': 0.8524590163934426}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "fl7g8EBFP53H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1c47a9a5-94f2-4e91-b8af-fa441c1c83dd"
      },
      "source": [
        "pd.DataFrame(x.values(), index=x.keys()).plot(kind=\"bar\")\n",
        "plt.legend([\"scores\"]);"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J6ggDXZI6wkCXpI4w0CWpIwx0SeoIA12SOsJAl6SOMNAlqSMMdEnqCANdkjrCQJekjjDQJakjhgr0JMcnuTPJ6iTnTnD+VUluTrIxyW80X6YkaTKTBnqSGcCFwOuAecCJSeaN63Y38DbgsqYLlCQNZ5gnFi0CVlfVGoAklwMnALdv6lBV3++fe3IKapQkDWGYKZf9gHsGjtf22yRJ25FpvSia5PQkK5KsWLdu3XR+tCR13jCBfi+w/8DxrH7bVquqi6pqYVUtHBsbeyZfQpK0GcME+nJgbpI5SXYBlgBLp7YsSdLWmjTQq2ojcAZwDXAHcGVVrUpyfpLFAEmOTLIW+A/AZ5OsmsqiJUlPN8wqF6pqGbBsXNt5A++X05uKkSS1xDtFJakjDHRJ6ggDXZI6wkCXpI4w0CWpIwx0SeoIA12SOsJAl6SOMNAlqSMMdEnqCANdkjrCQJekjjDQJakjDHRJ6ggDXZI6wkCXpI4YKtCTHJ/kziSrk5w7wflnJbmif/47SWY3XagkacsmDfQkM4ALgdcB84ATk8wb1+004P6q+kXgk8BHmi5UkrRlw4zQFwGrq2pNVT0GXA6cMK7PCcBf9t9/ATgmSZorU5I0mWGeKbofcM/A8VrgqM31qaqNSR4A9gH+ebBTktOB0/uH/5rkzmdS9A5iX8Z9/1Mp/k7UJH92O7au//wO3NyJoR4S3ZSqugi4aDo/sy1JVlTVwrbr0NbzZ7djG+Wf3zBTLvcC+w8cz+q3Tdgnyc7Ac4D1TRQoSRrOMIG+HJibZE6SXYAlwNJxfZYCb+2//w3g76qqmitTkjSZSadc+nPiZwDXADOAi6tqVZLzgRVVtRT4C+DSJKuBf6EX+qNuJKaWOsqf3Y5tZH9+cSAtSd3gnaKS1BEGuiR1hIEuaYeW5Kxh2kaBgS5pR/fWCdreNt1FbA+m9caiLkvy6/T2sHkekP6rqmqvVgvTFiU5bwunq6r+YNqK0VZJciLwFmBOksGl1HvRW203cgz05nwU+LWquqPtQrRVHp6gbXfgt+ltX2Ggb7/+AbiP3q3+Hx9ofwhY2UpFLXPZYkOSfLOqXtF2HXrmkuwJnEVv99ArgY9X1Y/brUqTSbIH8EhVPZnkYOAQ4KtV9XjLpU07A70hSS4Ang/8DfDopvaq+uvWitJQkuwNvAs4id6uoRdU1f3tVqVhJbkJeCXwXOCb9O5uf6yqTmq1sBY45dKcvYCfAq8ZaCvAQN+OJfkY8Ov07i6cX1X/2nJJ2nqpqp8mOQ34dFV9NMmtbRfVBkfoGmlJnqT3G9VGev8BP3UKL2rvEJLcAryD3sN1TutvTXJbVc1vubRp57LFhiSZleSLSX7cf12VZFbbdWnLqmqnqtqtqvasqr0GXnsa5juMs4H3Al/sh/lBwA0t19QKR+gNSXIdcBlwab/pZOCkqjquvaqk0ZFk96r6adt1tMkRenPGqupzVbWx/7oEGGu7KG1ZkoeSPNj/86GB458m2dh2fZpckpcluR34bv/48CSfbrmsVhjozVmf5OQkM/qvk/EhH9u9gamWPatqT+AFwB8CPwIuaLc6DelPgNfS//dWVf8IvKrVilpioDfnVODN9ILgPnoP+jil1Yo0tCS/kOSD9G5I2RM4sqre3W5VGlZV3TOu6YlWCmmZyxYbUlU/ABa3XYe2TpJ9gXcDvwlcDCyoqgfarUpb6Z4kLwcqyUx6N4eN5B3bXhTdRkl+r7/u9VP8/LI3AKrqzBbK0pCSPAysAz5H75bxn1NVn5j2orRV+v8pXwAcS2+56bXAWVU1clOejtC33aaRwIpWq9Az9TF+9h/xnuPOOdrZziWZQe/O3pG7K3QijtCnQJKdgGdX1YNt16ItS7L/BPOvm879u6r6ynTXpK2T5Ebg6Kp6rO1a2uZF0YYkuSzJXv2Ngv4vcHuSc9quS5O6Lsns8Y1JTsFVLjuKNcA3k3wgybs2vdouqg0GenPm9UfkbwS+CswBfqvdkjSEdwHXJpm7qSHJe/vtv9paVdoa/wR8hV6e7TnwGjnOoTdnZv8K+xuBP62qx5M4n7Wdq6plSR4FvprkjfT2QV8EvModF3cMVfXfAJI8u388shusOUJvzmeB7wN7AH+f5EDAOfQdQFVdT++ega8DB9GbjzXMdxBJXtzfoGsVsCrJTUl+qe262uBF0SmUZOeq8vbx7ViSh+itZgnwLOBxejeluNviDiLJPwC/X1U39I9fDXyoql7eamEtcITekCRn9S+KJslfJLkZOLrturRl427936Wq9nC3xR3OHpvCHKCqvk7vN+WRY6A359T+RdHX0Htyym8BH263JGkkrOmvcJndf72f3sqXkWOgNyf9P18PXFpVqwbaJE2dU+ntbPrXwFX0Hhp9aqsVtcQ59IYk+RywH73liocDM4CvV9VLWy1M6qgkv77pmb1JnuuFbAO9Mf27Q48A1lTVT5LsA+xXVStbLk3qpCQ3V9VLxr8fZU65NKeAecCmzbj2AHZtrxyp87KZ9yPLG4ua82ngSXorW86nt3PfVcCRbRYlddhuSRbQG5ju2n//VLBX1c2tVdYSp1wasulXviS3VNWCfts/VtXhbdcmdVGSLT0Iuqpq5JYNO0JvzuP9rTwLIMkYvRG7pClQVf+27Rq2N86hN+d/AF8EnpfkD4EbgQ+1W5KkUeKUSwP6K1x+GfgX4Bh683jXV9VIPgZLUjsM9IYMzp1LUhucQ2/O9UneBPx1+b+kNK2SHAbMZiDTNt10NEocoTekv2vfHsBGYAPu1idNiyQXA4fR2z5300KEqqqRu/3fQJe0Q0tye1XNa7uO7YFTLg1JMtFtxw8AP3BPdGlKfSvJvKq6ve1C2uYIvSFJvg28BLit3zSf3sOinwO8vaqubas2qcuS/CqwFPgR8Cg/m+48rNXCWuAIvTk/BE7rb5tLknn0tgD4PXrbehro0tT4C3rPH7iNEb+Zz0BvzsGbwhygqm5PckhVrUncN0iaQuuqamnbRWwPDPTmrEryGeDy/vFvArcn2fScSklT45YklwFfpjflArhsUdsgyW7AO4Bf6Td9k94OjBuA3avqX9uqTeqy/sNlxnPZorZNP9QPqKo7265F0uhxc66GJFkM3Ap8rX98RBLn9aQplmRWki8m+XH/dVWSWW3X1QYDvTn/FVgE/ASgqm6l93xRSVPrc/SWLb6w//pyv23kGOjNebyqHhjX5nyWNPXGqupzVbWx/7oEGGu7qDYY6M1ZleQtwIwkc5N8CviHtouSRsD6JCcnmdF/nQysb7uoNhjozXkn8Ev0lk39Fb3b/s9qtSJpNJwKvJnenaL3Ab8BnNJqRS1xlcsUSfIi4L9U1e+0XYuk0eCNRduovw/zH9O7GPM3wIXAnwJHAR9vsTSp0/rTmpsdkVbVmdNYznbBKZdt9+fAZcCbgH+mt3Txn4BfrKpPtlmY1HErgJuAXeltjPe9/usIYJcW62qNUy7bKMmtVXXEwPGaqjqozZqkUdLf6fRXNm1TnWQm8I2q+uV2K5t+Trlsu12TLKC3ZSfAo4PHVXVza5VJo+G5wF70HtIO8Ox+28hxhL6NktywhdNVVUdPWzHSCEpyCvBB4AZ6A6lXAR+sqr9ss642GOiSdnhJnk9vIQLAd6rqR23W0xYDXdIOL8l+wIEMTCNX1d+3V1E7nEOXtENL8hF6zx9Yxc+eWFTAyAW6I3RJO7QkdwKHVdWjk3buONehNyTJv0/ynIHjX0jyxjZrkkbEGmBm20VsDxyhN2T8evR+2y1VtaCtmqRRkOQq4HDgen7+EXQjd6eoc+jNmei3Hf9+pam3tP8aeY7QG5LkYnoPt7iw3/S7wN5V9bbWipI0Ugz0hiTZA/gAcGy/6Trgv1fVw+1VJXVfkrnAHwHz6O3rAsAobsFhoEvaoSW5kd4jID8J/Bq9vdB3qqrzWi2sBQb6NkryJ1V1dpIvM8FWnlW1uIWypJGR5KaqemmS26pq/mBb27VNNy/abbtL+3/+catVSKPr0SQ7Ad9LcgZwL70NukaO69C3UVXd1H97RFX9n8EXvX2ZJU2ts4DdgTOBlwK/BfzHVitqiVMuDUlyc1W9ZFyb69ClaZZkBrCkqj7fdi3TzSmXbZTkROAtwJwkg2thB/dnltSwJHvRWx68H7116Nf1j98NrARGLtAdoW+jJAcCc+gtmzp34NRDwMpNT1GR1KwkXwLuB74FHAM8j95+6GdV1a1t1tYWA70h/XXoj1TVk0kOBg4BvlpVj7dcmtRJ41a1zADuAw6oqg3tVtYeL4o25+/pPY5uP+BaehdmLmm1IqnbnhosVdUTwNpRDnNwDr1JqaqfJjkN+HRVfTTJSP7aJ02Tw5M82H8fYLf+ceg9/nGv9kprh4HenCR5GXAScFq/bUaL9UidVlX++xrHKZfmnA28F/hiVa1KchC9h9ZK0rTwoqgkdYRTLtvIvVwkbS8M9G3nXi6StgtOuUhSRzhCb0iS23j6lMsDwAp6D7pYP/1VSRolBnpzvgo8AVzWP15Cbwe4H9G7wejX2ilL0qhwyqUhm9lt8eaqesngLcqSNFVch96cGUkWbTpIciQ/u7HIDbokTTmnXJrz28DFSZ5N79bjB4HT+pt2/VGrlUkaCU65NCzJcwCq6oG2a5E0WpxyaUiS5yT5BHA9cH2Sj28Kd0maDgZ6cy6m91CLN/dfDwKfa7UiSSPFKZeGJLm1qo6YrE2Spooj9OY8kuRXNh0keQXwSIv1SBoxjtAbkuRw4H8Bm+bN7wfeWlUr26tK0igx0BvWfxI5VfVgkrOr6k/arknSaDDQp1CSu6vqgLbrkDQanEOfWmm7AEmjw0CfWv76I2naeOv/NkryEBMHd4DdprkcSSPMOXRJ6ginXCSpIwx0SeoIA12SOsJAl6SOMNAlqSP+P3QPnpkTioeUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKoGaylZP53L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c6c7af82-4011-43b9-dca5-8394a387ab03"
      },
      "source": [
        "pd.DataFrame(x, index=[\"scores\"]).T.plot.bar();"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnsrvXQiP53Q",
        "colab_type": "text"
      },
      "source": [
        "# Improving the Model\n",
        "1. HyperParameter Tuning\n",
        "2. Feature Importance\n",
        "\n",
        "### Classification problem specific evaluation metrics:\n",
        "3. Confusion Matrix\n",
        "4. Cross Validation\n",
        "5. Precision\n",
        "6. Recall\n",
        "7. F1\n",
        "8. Classification Report\n",
        "9. ROC Curve\n",
        "10. Area Under ROC curve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4SMOtGlP53R",
        "colab_type": "text"
      },
      "source": [
        "# HyperParameter Tuning for KNN:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MFLDLZSuP53U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# storing the scores:\n",
        "train_scores = []\n",
        "test_scores = []\n",
        "np.random.seed(61)\n",
        "neighbours = list(range(1,21))\n",
        "knn = KNeighborsClassifier()\n",
        "for i in neighbours:\n",
        "    knn = KNeighborsClassifier(n_neighbors= i)\n",
        "    knn.fit(X_train, Y_train)\n",
        "    train_scores.append(knn.score(X_train, Y_train))\n",
        "    test_scores.append(knn.score(X_test, Y_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KdhcVt-XP53b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6fd60f9d-6021-4670-e890-8ff8eca28878"
      },
      "source": [
        "print(train_scores, test_scores)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.0, 0.7933884297520661, 0.7851239669421488, 0.756198347107438, 0.7644628099173554, 0.7479338842975206, 0.731404958677686, 0.7272727272727273, 0.7024793388429752, 0.6942148760330579, 0.7024793388429752, 0.6859504132231405, 0.6859504132231405, 0.6859504132231405, 0.6983471074380165, 0.6983471074380165, 0.6900826446280992, 0.7066115702479339, 0.7066115702479339, 0.7024793388429752] [0.5737704918032787, 0.6557377049180327, 0.7049180327868853, 0.7213114754098361, 0.6885245901639344, 0.7213114754098361, 0.6557377049180327, 0.7213114754098361, 0.6557377049180327, 0.6721311475409836, 0.6229508196721312, 0.6229508196721312, 0.6557377049180327, 0.6557377049180327, 0.6065573770491803, 0.6229508196721312, 0.6229508196721312, 0.6721311475409836, 0.6229508196721312, 0.639344262295082]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4SrVbWCP53g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "113b6e98-2aa0-415e-a896-2d74e492001d"
      },
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.plot(neighbours, train_scores)\n",
        "plt.plot(neighbours, test_scores)\n",
        "plt.xticks(np.arange(1,21,1))\n",
        "plt.legend([\"Train Score\", \"Test Score\"])\n",
        "plt.axhline(max(test_scores), linestyle= \"dotted\", c = \"black\")\n",
        "plt.axvline(test_scores.index(max(test_scores)) + 1, linestyle = \"dotted\", c = \"black\")\n",
        "print(f\"Maximum Test Score: {max(test_scores)*100:.2f}% for {test_scores.index(max(test_scores)) + 1} Neighbours\")\n",
        "#plt.savefig(\"KNN model HyperParameter Tuning\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum Test Score: 72.13% for 4 Neighbours\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u_cMjo8CP53l",
        "colab_type": "text"
      },
      "source": [
        "# HyperParameter Tuning with RandomizedSearchCV on:\n",
        "\n",
        "1. `Logistic Regression`\n",
        "2. `Random Forest`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgEg3asSP53m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n",
        "                \"solver\": ['newton-cg', 'liblinear', 'sag', \"saga\"]}\n",
        "                #\"multi_class\": ['auto', 'ovr', 'multinomial']}\n",
        "rf_grid = {\"max_depth\": np.arange(10,1000,50),\n",
        "           \"max_features\": [\"auto\", \"sqrt\"],\n",
        "           \"min_samples_split\": np.arange(2,21,2),\n",
        "           \"min_samples_leaf\": np.arange(1,20,2)}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDrUxR8cP53r",
        "colab_type": "text"
      },
      "source": [
        "`Tuning LogisticRegression`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EORacsHfP53s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c4ca133e-16e5-4a3f-b0e9-8d5e293a7f6f"
      },
      "source": [
        "# Tuning Logistic Regression\n",
        "np.random.seed(61)\n",
        "# Setting up RandomizedSearchCV\n",
        "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
        "                               param_distributions= log_reg_grid,\n",
        "                               cv= 5,\n",
        "                               n_iter=20,\n",
        "                               verbose=True)\n",
        "# f0itting the model\n",
        "rs_log_reg.fit(X_train, Y_train)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.3s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score=nan,\n",
              "                   estimator=LogisticRegression(C=1.0, class_weight=None,\n",
              "                                                dual=False, fit_intercept=True,\n",
              "                                                intercept_scaling=1,\n",
              "                                                l1_ratio=None, max_iter=100,\n",
              "                                                multi_class='auto', n_jobs=None,\n",
              "                                                penalty='l2', random_state=None,\n",
              "                                                solver='lbfgs', tol=0.0001,\n",
              "                                                verbose=0, warm_start=False),\n",
              "                   iid='deprecated', n_iter=20, n_jobs=None,\n",
              "                   param_distributions={'C':...\n",
              "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
              "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
              "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
              "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
              "                                        'solver': ['newton-cg', 'liblinear',\n",
              "                                                   'sag', 'saga']},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1C5cvop5P53w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0256d7bd-1534-493f-f599-6b1c1db6277b"
      },
      "source": [
        "rs_log_reg.best_params_"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b38ePo57P533",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dc098fc7-8e37-43a0-8442-57712f4a1d3f"
      },
      "source": [
        "rs_log_reg.score(X_test, Y_test)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8688524590163934"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQ-aoBY-P538",
        "colab_type": "text"
      },
      "source": [
        "Tuning `RandomForest`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZAZHmV4P539",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "38be97a8-a0f8-4b90-b380-c4b39ed54afe"
      },
      "source": [
        "np.random.seed(61)\n",
        "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
        "                          param_distributions= rf_grid,\n",
        "                          cv = 5,\n",
        "                          n_iter=20,\n",
        "                          verbose=True)\n",
        "rs_rf.fit(X_train, Y_train)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:   13.6s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score=nan,\n",
              "                   estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                    ccp_alpha=0.0,\n",
              "                                                    class_weight=None,\n",
              "                                                    criterion='gini',\n",
              "                                                    max_depth=None,\n",
              "                                                    max_features='auto',\n",
              "                                                    max_leaf_nodes=None,\n",
              "                                                    max_samples=None,\n",
              "                                                    min_impurity_decrease=0.0,\n",
              "                                                    min_impurity_split=None,\n",
              "                                                    min_samples_leaf=1,\n",
              "                                                    min_samples_split=2,\n",
              "                                                    min_weight_fraction_leaf=0.0,\n",
              "                                                    n_estimators=100,\n",
              "                                                    n_jobs...\n",
              "                   param_distributions={'max_depth': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
              "       660, 710, 760, 810, 860, 910, 960]),\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
              "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20])},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O68Em-sPP54B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "e975c16c-2ce0-4dcc-eb8f-1af86e86e466"
      },
      "source": [
        "rs_rf.best_params_"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 910,\n",
              " 'max_features': 'auto',\n",
              " 'min_samples_leaf': 9,\n",
              " 'min_samples_split': 20}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3YwAzuUCP54G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "00fc0560-62be-4d45-bc9c-ffb90f3e7ca2"
      },
      "source": [
        "rs_rf.score(X_test, Y_test)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8688524590163934"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKOAu75mP54K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a53e1c09-5742-4c50-f1ab-9976fb81c28b"
      },
      "source": [
        "x"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'KNN': 0.6885245901639344,\n",
              " 'Logistic Regression': 0.8524590163934426,\n",
              " 'Random Forest': 0.8524590163934426}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MwdWZusP54Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "log_reg_grid2 = {\"C\": np.logspace(-4, 4, 20),\n",
        "                \"solver\": ['newton-cg', 'lbfgs','liblinear', 'sag', \"saga\"],\n",
        "                \"multi_class\": ['auto', 'ovr', 'multinomial']}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YY8zRgykP54Z",
        "colab_type": "text"
      },
      "source": [
        "# Logistic Regression On Different Grids"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdSoS9uFP54v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "948bf076-1b75-46fb-a65f-772abdc1d141"
      },
      "source": [
        "x"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'KNN': 0.6885245901639344,\n",
              " 'Logistic Regression': 0.8524590163934426,\n",
              " 'Random Forest': 0.8524590163934426}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Hxaqrz4P542",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "ba2ed4a7-0b53-4bda-d94d-4b6682a56ff0"
      },
      "source": [
        "log_reg_grid3 = {\"C\": np.logspace(-4, 4, 20),\n",
        "                \"solver\": ['liblinear']}\n",
        "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
        "                         param_grid=log_reg_grid3,\n",
        "                         cv=5,\n",
        "                         verbose=True)\n",
        "gs_log_reg.fit(X_train, Y_train);"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5L7dOcdP55T",
        "colab_type": "text"
      },
      "source": [
        "# Random Forest on Grid Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wd_wH5L9P55h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "4e20a19b-bf03-40ad-d54f-fcca5b2e8b09"
      },
      "source": [
        "loaded_gs_rf = pickle.load(open(\"GridSearchCV_RandomForest_model_hourlong.pkl\", 'rb'))\n",
        "loaded_gs_rf.score(X_test, Y_test)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 0.22.1 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator RandomForestClassifier from version 0.22.1 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator GridSearchCV from version 0.22.1 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8360655737704918"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FASh9wBsP55n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_preds = gs_log_reg.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elDYApIdP553",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "08c2b6fb-64c9-4a80-9ba8-6e87d4234c06"
      },
      "source": [
        "plt.style.use(\"default\")\n",
        "plot_roc_curve(gs_log_reg, X_test, Y_test)\n",
        "plt.plot([0,1], linestyle = \"dotted\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5c309fd400>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3vmB-jdP55_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "382dfb47-081b-4bd5-bc18-1b4c91f4ef74"
      },
      "source": [
        "roc_auc_score(Y_test, y_preds)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8672043010752688"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnanSM68P56G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "47cee25e-6b56-4de8-931d-6500cea50d8f"
      },
      "source": [
        "print(confusion_matrix(Y_test, y_preds))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[23  7]\n",
            " [ 1 30]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_cB3uD-P56N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "7d1b1c7d-3d1a-4251-870b-6e8cb1bacf09"
      },
      "source": [
        "plt.figure(figsize=(4,4))\n",
        "sns.set(font_scale=1.5)\n",
        "sns.heatmap(confusion_matrix(Y_test,y_preds),\n",
        "           annot= True,\n",
        "           cbar = False)\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.xlabel(\"Predicted Labels\")"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 2.2499999999999947, 'Predicted Labels')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nHTprV2P56W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "7db46d2e-1418-4e30-e511-71381f69bf72"
      },
      "source": [
        "print(classification_report(Y_test,y_preds))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.77      0.85        30\n",
            "           1       0.81      0.97      0.88        31\n",
            "\n",
            "    accuracy                           0.87        61\n",
            "   macro avg       0.88      0.87      0.87        61\n",
            "weighted avg       0.88      0.87      0.87        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLC9H1f1P56e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6a65ca98-7eb0-4f5c-83e4-c89db71de205"
      },
      "source": [
        "gs_log_reg.best_params_"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "si9QwAEfP56k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2bd5ec1b-b10a-4b66-a310-d2e2ee9b4c51"
      },
      "source": [
        "# cross validation accuracy score:\n",
        "lr_cv_acc = cross_val_score(LogisticRegression(C = 0.23357214690901212, solver='liblinear'),\n",
        "                     X, Y, \n",
        "                     cv=5,\n",
        "                     scoring=\"accuracy\")\n",
        "lr_cv_acc"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.81967213, 0.90163934, 0.8852459 , 0.88333333, 0.75      ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1jx7DckP56o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9c99103e-6bf6-44bd-c473-b15c7c173731"
      },
      "source": [
        "lr_cv_acc.mean()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8479781420765027"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLhclHRnP56t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluation_metric(model, X, Y,):\n",
        "    \"\"\"\n",
        "    Calculates score for Classification problems\n",
        "    \"\"\"\n",
        "    clf_rep = [\"accuracy\",\n",
        "          \"precision\",\n",
        "          \"recall\",\n",
        "          \"f1\"]\n",
        "    result = {}\n",
        "    for name in clf_rep:\n",
        "        x = cross_val_score(model,X,Y,cv=5,scoring=name)\n",
        "        result[name] = x.mean()\n",
        "    return result\n",
        "clf = LogisticRegression(C = 0.23357214690901212, solver='liblinear')\n",
        "log_reg_report = evaluation_metric(clf, X, Y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9V6ASrJlP56w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "aca0f4dd-5925-4844-e43c-dc9cb239e5c7"
      },
      "source": [
        "log_reg_report"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.8479781420765027,\n",
              " 'f1': 0.8705403543192143,\n",
              " 'precision': 0.8215873015873015,\n",
              " 'recall': 0.9272727272727274}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6KOpBfWCP560",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "f8bb623e-18f0-4fe9-f99d-33b9ef886d66"
      },
      "source": [
        "log_reg_report_df = pd.DataFrame(log_reg_report, index = [\"Scores\"])\n",
        "log_reg_report_df"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Scores</th>\n",
              "      <td>0.847978</td>\n",
              "      <td>0.821587</td>\n",
              "      <td>0.927273</td>\n",
              "      <td>0.87054</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        accuracy  precision    recall       f1\n",
              "Scores  0.847978   0.821587  0.927273  0.87054"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7AU7um_P564",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "1aae710f-15ac-4b48-d49c-671767473f2a"
      },
      "source": [
        "plt.figure(figsize = (10,4))\n",
        "plt.style.use(\"default\")\n",
        "log_reg_report_df.T.plot(kind=\"bar\");"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CbPmpKmP567",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1a138e26-400b-4bbb-b2be-0ec46a6274b4"
      },
      "source": [
        "gs_log_reg.best_params_"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOyLv-62P57C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "e77c25ed-033c-4419-de2b-8bff83bcdd61"
      },
      "source": [
        "clf = LogisticRegression(C= 0.23357214690901212,\n",
        "                        solver = \"liblinear\")\n",
        "clf.fit(X_train, Y_train)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.23357214690901212, class_weight=None, dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ue_lmrFcP57G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "5e2c9516-5eea-4f59-ed97-943ca78b40f0"
      },
      "source": [
        "clf.coef_"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.00787491, -0.82590556,  0.70727189, -0.0186182 , -0.0023179 ,\n",
              "        -0.04327278,  0.43173841,  0.02925065, -0.53627297, -0.46570704,\n",
              "         0.50250274, -0.55567272, -0.73409538]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HM7vY3L_P57J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "f7464313-8f3e-43ae-a895-53ceb6bc3758"
      },
      "source": [
        "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
        "feature_dict"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'age': 0.007874911797385751,\n",
              " 'ca': -0.5556727205616859,\n",
              " 'chol': -0.002317898993991836,\n",
              " 'cp': 0.7072718897633213,\n",
              " 'exang': -0.5362729738262139,\n",
              " 'fbs': -0.04327277665209399,\n",
              " 'oldpeak': -0.46570703539025277,\n",
              " 'restecg': 0.4317384086408409,\n",
              " 'sex': -0.8259055576362699,\n",
              " 'slope': 0.5025027417128444,\n",
              " 'thal': -0.7340953840469039,\n",
              " 'thalach': 0.029250645713447793,\n",
              " 'trestbps': -0.018618202281033464}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwQ1OQ-0P57M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "c2aa1085-647f-4c72-8389-e53ea7387dec"
      },
      "source": [
        "feature_df = pd.DataFrame(feature_dict, index = [\"Values\"])\n",
        "feature_df"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Values</th>\n",
              "      <td>0.007875</td>\n",
              "      <td>-0.825906</td>\n",
              "      <td>0.707272</td>\n",
              "      <td>-0.018618</td>\n",
              "      <td>-0.002318</td>\n",
              "      <td>-0.043273</td>\n",
              "      <td>0.431738</td>\n",
              "      <td>0.029251</td>\n",
              "      <td>-0.536273</td>\n",
              "      <td>-0.465707</td>\n",
              "      <td>0.502503</td>\n",
              "      <td>-0.555673</td>\n",
              "      <td>-0.734095</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             age       sex        cp  ...     slope        ca      thal\n",
              "Values  0.007875 -0.825906  0.707272  ...  0.502503 -0.555673 -0.734095\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNf4ba0FP57U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "14b2d1c2-4981-41c4-9f93-f22fe164ed6c"
      },
      "source": [
        "feature_df.T.plot.bar(legend=False, title=\"Feature Importance\");"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou_2PE7aP57c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "b9aa9145-dc2d-485f-c170-a03538d06810"
      },
      "source": [
        "pd.crosstab(df.slope, df.target)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>target</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>91</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "target   0    1\n",
              "slope          \n",
              "0       12    9\n",
              "1       91   49\n",
              "2       35  107"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9D6pMtGP57i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}