parameter_tuning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "parameter-tuning",
      "provenance": [],
      "collapsed_sections": [
        "H6KVYgQFOvwv",
        "RLTpIgKeLucE",
        "8wHtLvv0LwcQ",
        "AgGtK1nBL-lb",
        "y6d04tM8QYOI",
        "cZAWQTFjWVlW",
        "98oYwoBYX6Tv",
        "yYfvyLBNlF5z",
        "G4gNrqbRpmeX",
        "m2kGCgOLr0a9"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H6KVYgQFOvwv"
      },
      "source": [
        "# data and other import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YcuHWgkLHwZ",
        "outputId": "1273b829-0665-4216-fdad-ddb411c41a5f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2umDYJZLPBM"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns \n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "x-2ga0yeLXAY",
        "outputId": "b86f7243-8929-4f40-943c-dfa992756a45"
      },
      "source": [
        "df=pd.read_csv(\"gdrive/MyDrive/dataset/heart.csv\")\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "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": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADZ2vNIzLZSS"
      },
      "source": [
        "chestPain=pd.get_dummies(df['cp'],prefix='cp',drop_first=True)\n",
        "df=pd.concat([df,chestPain],axis=1)\n",
        "df.drop(['cp'],axis=1,inplace=True)\n",
        "sp=pd.get_dummies(df['slope'],prefix='slope')\n",
        "th=pd.get_dummies(df['thal'],prefix='thal')\n",
        "frames=[df,sp,th]\n",
        "df=pd.concat(frames,axis=1)\n",
        "df.drop(['slope','thal'],axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "akqaGRTRLeVU",
        "outputId": "32b71138-d562-4c2c-cd09-93d019a9108f"
      },
      "source": [
        "df.head(5)"
      ],
      "execution_count": null,
      "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>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>ca</th>\n",
              "      <th>target</th>\n",
              "      <th>cp_1</th>\n",
              "      <th>cp_2</th>\n",
              "      <th>cp_3</th>\n",
              "      <th>slope_0</th>\n",
              "      <th>slope_1</th>\n",
              "      <th>slope_2</th>\n",
              "      <th>thal_0</th>\n",
              "      <th>thal_1</th>\n",
              "      <th>thal_2</th>\n",
              "      <th>thal_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  trestbps  chol  fbs  ...  slope_2  thal_0  thal_1  thal_2  thal_3\n",
              "0   63    1       145   233    1  ...        0       0       1       0       0\n",
              "1   37    1       130   250    0  ...        0       0       0       1       0\n",
              "2   41    0       130   204    0  ...        1       0       0       1       0\n",
              "3   56    1       120   236    0  ...        1       0       0       1       0\n",
              "4   57    0       120   354    0  ...        1       0       0       1       0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8wHtLvv0LwcQ"
      },
      "source": [
        "# split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mxeXwbZLge3"
      },
      "source": [
        "X = df.drop(['target'], axis = 1)\n",
        "y = df.target.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wr4ignUGLkeW"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4968QqzzLmtE"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X=StandardScaler()\n",
        "X_train=sc_X.fit_transform(X_train)\n",
        "X_test=sc_X.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VyX6MK2WL4cS"
      },
      "source": [
        "# para meter tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgGtK1nBL-lb"
      },
      "source": [
        "## lr"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHBRBTRCL9C2"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llm5lDctNTlT"
      },
      "source": [
        "gs_logiReg=LogisticRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUvFyQvkNThr"
      },
      "source": [
        "lr_param_grid = [    \n",
        "    {'penalty' : ['l1', 'l2', 'elasticnet', 'none'],\n",
        "    'C' : np.logspace(-4, 4, 20),\n",
        "    'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],\n",
        "    'max_iter' : [100, 1000,2500, 5000]\n",
        "    }\n",
        "]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZjdIkM1NTcy"
      },
      "source": [
        "lr_clf = GridSearchCV(gs_logiReg, param_grid = lr_param_grid, cv = 3, verbose=True, n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9goEY2DJNgBQ",
        "outputId": "3a922c7f-fc1d-4554-a033-cc688cc0cc64"
      },
      "source": [
        "lr_best_clf = lr_clf.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 1600 candidates, totalling 4800 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 202 tasks      | elapsed:    4.7s\n",
            "[Parallel(n_jobs=-1)]: Done 1402 tasks      | elapsed:   36.6s\n",
            "[Parallel(n_jobs=-1)]: Done 3114 tasks      | elapsed:  1.8min\n",
            "[Parallel(n_jobs=-1)]: Done 4800 out of 4800 | elapsed:  3.2min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlT0bl-3Nf94",
        "outputId": "8f1c8c84-66c9-4799-88d0-02dc28dfb554"
      },
      "source": [
        "lr_best_clf.best_estimator_"
      ],
      "execution_count": null,
      "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": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-KjEmihVMH_W"
      },
      "source": [
        "logiReg=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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7r23fGtvMRY7",
        "outputId": "b0cac236-e937-4311-da97-85b513e22849"
      },
      "source": [
        "logiReg.fit(X_train,y_train)\n",
        "logiRegPred=logiReg.predict(X_test)\n",
        "logiRegAcu=accuracy_score(y_test, logiRegPred)\n",
        "print(logiRegAcu)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8852459016393442\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UykXLxYnMft-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y6d04tM8QYOI"
      },
      "source": [
        "## svm classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_-bLf0_bgvc"
      },
      "source": [
        "gs_svm = SVC()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isA1kPqjQaYn"
      },
      "source": [
        "svm_param_grid = {'C': [0.1, 1, 10, 100, 1000],  \n",
        "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001], \n",
        "              'kernel': ['rbf']}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTUafzJFSM6Z"
      },
      "source": [
        "svm_clf = GridSearchCV(gs_svm, param_grid = svm_param_grid, cv = 3, verbose=True, n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "chCPLIm2UcOV",
        "outputId": "2d85cae3-95d7-4a44-cdec-a83f566ac8ef"
      },
      "source": [
        "svm_best_clf = svm_clf.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 25 candidates, totalling 75 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done  75 out of  75 | elapsed:    0.6s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTHt-CGvUiEJ",
        "outputId": "1508dcc9-0411-4cca-a9b3-2ae690f85409"
      },
      "source": [
        "svm_best_clf.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1000, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKUTdCdAUofI"
      },
      "source": [
        "svc = SVC(C=1000, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
        "    decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
        "    max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
        "    tol=0.001, verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQHaIydWUwNO",
        "outputId": "ce242224-fb22-4690-acd3-0856d6c3a291"
      },
      "source": [
        "svc.fit(X_train,y_train)\n",
        "svcPred=svc.predict(X_test)\n",
        "svcAcu=accuracy_score(y_test, svcPred)\n",
        "print(svcAcu)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8852459016393442\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZAWQTFjWVlW"
      },
      "source": [
        "## bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF2Zs6uoU3Rs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYiDd7fFWdrW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cD6L3UjEWdmu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7whPvNXuWdgy"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljqhrfAIWdP9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9-pf2D6WdIL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98oYwoBYX6Tv"
      },
      "source": [
        "## rf"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDdzauOSdm5Z"
      },
      "source": [
        "gs_random_forest = RandomForestClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oansd8WZX-tu"
      },
      "source": [
        "rf_param_grid = { \n",
        "    'n_estimators': [200, 500],\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'max_depth' : [4,5,6,7,8],\n",
        "    'criterion' :['gini', 'entropy']\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09VF-wV5X-n_"
      },
      "source": [
        "rf_clf = GridSearchCV(gs_random_forest, param_grid = rf_param_grid, cv = 3, verbose=True, n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iti6lRvqX-ju",
        "outputId": "bac447b5-dd05-418b-a70c-fc8264187a44"
      },
      "source": [
        "rf_best_clf = rf_clf.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 60 candidates, totalling 180 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   20.2s\n",
            "[Parallel(n_jobs=-1)]: Done 180 out of 180 | elapsed:  1.3min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4PMf-OHX-ex",
        "outputId": "c4d178c7-cb5a-4ef3-f5aa-47abc8c68eb3"
      },
      "source": [
        "rf_best_clf.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=6, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "se5iEaqEX-VI"
      },
      "source": [
        "randomForest = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
        "                       criterion='entropy', max_depth=7, max_features='log2',\n",
        "                       max_leaf_nodes=None, max_samples=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=1, min_samples_split=2,\n",
        "                       min_weight_fraction_leaf=0.0, n_estimators=500,\n",
        "                       n_jobs=None, oob_score=False, random_state=None,\n",
        "                       verbose=0, warm_start=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NosSXSHX9Br",
        "outputId": "34097d89-e5dd-41ca-b707-a6236cd44b7b"
      },
      "source": [
        "randomForest.fit(X_train,y_train)\n",
        "randomForestPred=randomForest.predict(X_test)\n",
        "randomForestAcu=accuracy_score(randomForestPred,y_test)\n",
        "print(randomForestAcu)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9016393442622951\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYfvyLBNlF5z"
      },
      "source": [
        "## DT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEbR6Ajujf3i"
      },
      "source": [
        "gs_dt = DecisionTreeClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNzkm9sWnItG"
      },
      "source": [
        "dt_param_grid = {\n",
        "    'criterion': ['entropy', 'gini'],\n",
        "    'max_depth':[4,5,6,7,8,9,10,11,12,15,20,30,40,50,70,90,120,150],\n",
        "    'min_samples_split' : range(10,500,20)\n",
        "    }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drLjr6_LnInL"
      },
      "source": [
        "dt_clf = GridSearchCV(gs_dt, param_grid = dt_param_grid, cv = 3, verbose=True, n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFxpM09znIhd",
        "outputId": "da3d499d-2b54-41b0-a5a7-9635f7f55204"
      },
      "source": [
        "dt_best_clf = dt_clf.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 900 candidates, totalling 2700 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 956 tasks      | elapsed:    2.8s\n",
            "[Parallel(n_jobs=-1)]: Done 2700 out of 2700 | elapsed:    7.5s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbWymkX0nIUL",
        "outputId": "be3eb2af-3f79-4276-c3d2-2f3fc0c1b71a"
      },
      "source": [
        "dt_best_clf.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=4, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=10,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBpN7IlXnHk0"
      },
      "source": [
        "dTree=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
        "                       max_depth=4, max_features=None, max_leaf_nodes=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=1, min_samples_split=30,\n",
        "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
        "                       random_state=None, splitter='best')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OrtUeMOTor_j",
        "outputId": "4194e1e3-7b9a-4bbf-8571-ff0c13cd14fb"
      },
      "source": [
        "dTree.fit(X_train,y_train)\n",
        "dTreePred=dTree.predict(X_test)\n",
        "dTreeAcu=accuracy_score(dTreePred,y_test)\n",
        "print(dTreeAcu)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9016393442622951\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4gNrqbRpmeX"
      },
      "source": [
        "## KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9NUrU0xo32f"
      },
      "source": [
        "gs_knn = KNeighborsClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXcsflp-pyt4"
      },
      "source": [
        "knn_param_grid = {'n_neighbors':np.arange(1,30),\n",
        "              'leaf_size':np.arange(1,30),\n",
        "              'algorithm':['auto', 'kd_tree'],\n",
        "              'n_jobs':[-1]\n",
        "              }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcjriL-dpyoX"
      },
      "source": [
        "knn_clf = GridSearchCV(gs_knn, param_grid = knn_param_grid, cv = 3, verbose=True, n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I47qwPO9pykV",
        "outputId": "f4018792-05e6-4771-827e-8068aacb852c"
      },
      "source": [
        "knn_best_clf = knn_clf.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 1682 candidates, totalling 5046 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done  88 tasks      | elapsed:    5.0s\n",
            "[Parallel(n_jobs=-1)]: Done 388 tasks      | elapsed:   21.6s\n",
            "[Parallel(n_jobs=-1)]: Done 888 tasks      | elapsed:   49.7s\n",
            "[Parallel(n_jobs=-1)]: Done 1588 tasks      | elapsed:  1.5min\n",
            "[Parallel(n_jobs=-1)]: Done 2488 tasks      | elapsed:  2.3min\n",
            "[Parallel(n_jobs=-1)]: Done 3588 tasks      | elapsed:  3.3min\n",
            "[Parallel(n_jobs=-1)]: Done 4888 tasks      | elapsed:  4.5min\n",
            "[Parallel(n_jobs=-1)]: Done 5046 out of 5046 | elapsed:  4.7min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0T7GVBopygQ",
        "outputId": "8e4a670c-d823-44f9-d7d2-81b91b7f470e"
      },
      "source": [
        "knn_best_clf.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=1, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=-1, n_neighbors=13, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n26gTKdZpybM"
      },
      "source": [
        "knn=KNeighborsClassifier(algorithm='auto', leaf_size=1, metric='minkowski',\n",
        "                     metric_params=None, n_jobs=-1, n_neighbors=13, p=2,\n",
        "                     weights='uniform')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icZYhkR2pv-r",
        "outputId": "77d89820-5c79-44a4-fe03-513034556926"
      },
      "source": [
        "knn.fit(X_train,y_train)\n",
        "knnPred=knn.predict(X_test)\n",
        "knnAcu=accuracy_score(knnPred,y_test)\n",
        "print(knnAcu)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9016393442622951\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2kGCgOLr0a9"
      },
      "source": [
        "## XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fs2w4Gmvh-6J",
        "outputId": "b25c120a-6d8c-4d13-fa5b-c27b2d729b6b"
      },
      "source": [
        "gs_xgboost = XGBClassifier()\n",
        "gs_xgboost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofXSYj1Rsff2"
      },
      "source": [
        "xgb_param_grid = {\n",
        "    'max_depth': np.arange(1, 10),\n",
        "    'n_estimators': range(60, 220, 40),\n",
        "    'learning_rate': [0.1, 0.01, 0.05],\n",
        "    'verbosity': [0,1,2,3],\n",
        "    \"subsample\" : [0.5, 0.8]\n",
        "\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tmkCVveetKNQ"
      },
      "source": [
        "xgb_clf = GridSearchCV(gs_xgboost, param_grid = xgb_param_grid, cv = 3, verbose=True, n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89PVSQsVtSy5",
        "outputId": "f69df113-4b46-4c19-e49c-d5e889d2571f"
      },
      "source": [
        "xgb_best_clf = xgb_clf.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 864 candidates, totalling 2592 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 300 tasks      | elapsed:    9.4s\n",
            "[Parallel(n_jobs=-1)]: Done 1500 tasks      | elapsed:   58.1s\n",
            "[Parallel(n_jobs=-1)]: Done 2592 out of 2592 | elapsed:  1.8min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EL7cRIBtWz9",
        "outputId": "7b1452a4-1bb9-442a-8201-e19a3a58c28c"
      },
      "source": [
        "xgb_best_clf.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=1,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=0.5, verbosity=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8j70F4rtcmZ"
      },
      "source": [
        "xgboost = XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
        "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
        "              learning_rate=0.1, max_delta_step=0, max_depth=1,\n",
        "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
        "              nthread=None, objective='binary:logistic', random_state=0,\n",
        "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
        "              silent=None, subsample=0.5, verbosity=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WiWIZS1Ttmaq",
        "outputId": "d24688f5-f6db-4c90-935d-e55c3d3055a2"
      },
      "source": [
        "xgboost.fit(X_train, y_train)\n",
        "xgboost_pred = xgboost.predict(X_test)\n",
        "xgboost_accu = accuracy_score(y_test, xgboost_pred)\n",
        "print(xgboost_accu)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9180327868852459\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZHuGBYatsJM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}