heart_disease_prediction.ipynb

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      "source": [
        "# **Heart Disease Prediction**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n"
      ],
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              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0     63    1   3       145   233    1        0      150      0      2.3   \n",
              "1     37    1   2       130   250    0        1      187      0      3.5   \n",
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              "302   57    0   1       130   236    0        0      174      0      0.0   \n",
              "\n",
              "     slope  ca  thal  target  \n",
              "0        0   0     1       1  \n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "sns.set_theme(color_codes=True)\n",
        "df = pd.read_csv('heart.csv')\n",
        "df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exploratory Data Analysis**"
      ],
      "metadata": {
        "id": "sVU7GwTdqZkO"
      }
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    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data=df, x=\"sex\", hue=\"target\")\n",
        "\n"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 302
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        "id": "6oUvpytqoWzW",
        "outputId": "17550111-c504-494f-b3d6-fbffc1b5371e"
      },
      "execution_count": 6,
      "outputs": [
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a526ef10>"
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          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df, x=\"age\", hue=\"target\", multiple=\"stack\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "cf2o0jUDofR7",
        "outputId": "23fa1f5f-2b11-4741-8100-9a7ccf59bf73"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a4bd1610>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df, x=\"trestbps\", hue=\"target\", multiple=\"stack\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "vPuZJ75Rohrj",
        "outputId": "44e01da2-585c-4a68-8810-d28bf5dccbb7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a4af7a60>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df, x=\"thalach\", hue=\"target\", multiple=\"stack\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "B2Lf0cvRojeg",
        "outputId": "9f82842d-af29-4b97-9388-6d84a56c1809"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a50fd040>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Preprocessing**"
      ],
      "metadata": {
        "id": "59UDa1_zqmyV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BnFc-srTokNh",
        "outputId": "4788003b-a5a8-4261-d245-184b16fef380"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['target'])\n",
        "print(df.target.value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "va4Gc5MUonQz",
        "outputId": "b53f1847-ed8c-4190-842c-7a301cf88544"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    165\n",
            "0    138\n",
            "Name: target, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Outlier Detection Using Boxplot**"
      ],
      "metadata": {
        "id": "qtXef9rIqvkC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df[\"age\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "Cnn6WsQsoqoW",
        "outputId": "a55b1c5f-8119-4dbb-c12c-c376fdf49d71"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a4ab9d60>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df[\"trestbps\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "CsGbM0ZQotg9",
        "outputId": "2a28f974-1219-4f46-ba34-2bb116bb6b24"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a4a9e4c0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df[\"chol\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "YLOuZZlaozgQ",
        "outputId": "7c408357-0307-4352-d250-2a805f90725b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a4a6eac0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df[\"thalach\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "tZVftuNDo2Um",
        "outputId": "5acbdbdf-d6af-4897-dfb6-f009425cba13"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a49c5340>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df[\"oldpeak\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "zg5g7UkUo6dB",
        "outputId": "c2759f98-2bad-468c-e27a-619c14a7609b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a49ac220>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Outlier Removal Using Z-Score**"
      ],
      "metadata": {
        "id": "TRfHdl8Uq35F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "z = np.abs(stats.zscore(df))\n",
        "data_clean = df[(z<3).all(axis = 1)]\n",
        "data_clean.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rbjROYFLpBgs",
        "outputId": "8480c054-ebc4-4969-c64b-c960dd78915b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(287, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Correlation using Heatmap**"
      ],
      "metadata": {
        "id": "VAsLAq_vq-cE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data_clean.corr(), fmt='.2g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "MjmsYp7gpGGJ",
        "outputId": "32cf9217-5273-4548-8e6d-8ffee37c779c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f86a48f8e50>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Correlation between Class and other attributes**"
      ],
      "metadata": {
        "id": "Wuc9G6CnrEq7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr = data_clean[data_clean.columns[1:]].corr()['target'][:-1]\n",
        "plt.plot(corr)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "5FsL43_7pKx8",
        "outputId": "69870c31-64c7-417c-bce0-5b630fca251e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Machine Learning Model Building**"
      ],
      "metadata": {
        "id": "0LWVOz2drLc1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data_clean.drop('target', axis=1)\n",
        "y = data_clean['target']"
      ],
      "metadata": {
        "id": "MEhnD52opP0B"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2,random_state=0)\n"
      ],
      "metadata": {
        "id": "Sg49MjqopWi3"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Decision Tree**"
      ],
      "metadata": {
        "id": "MtgsnvqErT1A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dtree = DecisionTreeClassifier(random_state = 0)\n",
        "dtree.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJVXmfNjpZ7C",
        "outputId": "7040ef0a-70e9-4bc9-c865-10bd44afaee2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = dtree.predict(X_test)\n",
        "print(\"Accuracy Score :\", round(accuracy_score(y_test, y_pred)*100 ,2), \"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5gTGeImph27",
        "outputId": "f683ddd8-0ed4-4363-d4ab-3cd3d166b937"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score : 75.86 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
        "print('F-1 Score : ',(f1_score(y_test, y_pred)))\n",
        "print('Precision Score : ',(precision_score(y_test, y_pred)))\n",
        "print('Recall Score : ',(recall_score(y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FX7Z8poQpmsX",
        "outputId": "f647679c-5f62-4434-e770-3088764b2f8f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F-1 Score :  0.7741935483870968\n",
            "Precision Score :  0.7272727272727273\n",
            "Recall Score :  0.8275862068965517\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Importance\n",
        "imp_df = pd.DataFrame({\n",
        "\"Feature Name\": X_train.columns,\n",
        "\"Importance\": dtree.feature_importances_\n",
        "})\n",
        "fi = imp_df.sort_values(by=\"Importance\", ascending=False)\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.barplot(data=fi, x='Importance', y='Feature Name')\n",
        "plt.title('Feature Importance Each Attributes', fontsize=18)\n",
        "plt.xlabel ('Importance', fontsize=16)\n",
        "plt.ylabel ('Feature Name', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "YooidEMWpvFY",
        "outputId": "a7e0f760-72df-4091-dc7d-02f297fd220d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Random Forest**"
      ],
      "metadata": {
        "id": "UKAx9X_Nrqq7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc = RandomForestClassifier(random_state=0)\n",
        "rfc.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CE1WcDMepzPI",
        "outputId": "e0cc1482-9bfe-42f7-ec02-81afe9e82a29"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rfc.predict(X_test)\n",
        "print(\"Accuracy Score :\", round(accuracy_score(y_test, y_pred)*100 ,2), \"%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-iSRbYep2Lm",
        "outputId": "5b613317-c6b4-4f24-8939-1ec6c66a6324"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score : 84.48 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
        "print('F-1 Score : ',(f1_score(y_test, y_pred)))\n",
        "print('Precision Score : ',(precision_score(y_test, y_pred)))\n",
        "print('Recall Score : ',(recall_score(y_test, y_pred)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83jbXP-up6XX",
        "outputId": "286a1baf-f712-4bcc-fb9d-a416c10b25fc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F-1 Score :  0.8524590163934426\n",
            "Precision Score :  0.8125\n",
            "Recall Score :  0.896551724137931\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Importance\n",
        "imp_df = pd.DataFrame({\n",
        "\"Feature Name\": X_train.columns,\n",
        "\"Importance\": rfc.feature_importances_\n",
        "})\n",
        "fi = imp_df.sort_values(by=\"Importance\", ascending=False)\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.barplot(data=fi, x='Importance', y='Feature Name')\n",
        "plt.title('Feature Importance Each Attributes', fontsize=18)\n",
        "plt.xlabel ('Importance', fontsize=16)\n",
        "plt.ylabel ('Feature Name', fontsize=16)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "ZLwqH1Trp_4Y",
        "outputId": "e081f288-d36a-4e23-c842-a2f505c3a387"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **AdaBoost**"
      ],
      "metadata": {
        "id": "A6oSqwkmrxel"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "ada = AdaBoostClassifier(random_state=0)\n",
        "ada.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DrDBZ5edqDmz",
        "outputId": "4bb67e6f-8835-4627-fd96-822b1f22c706"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = ada.predict(X_test)\n",
        "print(\"Accuracy Score :\", round(accuracy_score(y_test, y_pred)*100 ,2), \"%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMdKtXPdqGb1",
        "outputId": "f8a774b1-e483-4322-fbfa-09fe1ae7b66f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score : 79.31 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
        "print('F-1 Score : ',(f1_score(y_test, y_pred)))\n",
        "print('Precision Score : ',(precision_score(y_test, y_pred)))\n",
        "print('Recall Score : ',(recall_score(y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCWQsuA0qLXj",
        "outputId": "f9e6a726-2050-41d3-f773-19780a72ca26"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F-1 Score :  0.7931034482758621\n",
            "Precision Score :  0.7931034482758621\n",
            "Recall Score :  0.7931034482758621\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Importance\n",
        "imp_df = pd.DataFrame({\n",
        "\"Feature Name\": X_train.columns,\n",
        "\"Importance\": ada.feature_importances_\n",
        "})\n",
        "fi = imp_df.sort_values(by=\"Importance\", ascending=False)\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.barplot(data=fi, x='Importance', y='Feature Name')\n",
        "plt.title('Feature Importance Each Attributes', fontsize=18)\n",
        "plt.xlabel ('Importance', fontsize=16)\n",
        "plt.ylabel ('Feature Name', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "YgdY_fKcqROx",
        "outputId": "48bce86a-1f67-4ac1-be12-1e69a92f5e92"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mT2J7XOboc7E"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}