Step 3 & 4 UCI Madelon Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn import linear_model\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_selection import SelectKBest, SelectFromModel, f_regression\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_data = pd.read_csv('madelon_train.data.csv', delimiter=' ', header=None).drop(500, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_labels = pd.read_csv('madelon_train.labels.csv', delimiter=' ', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_10pct_1 = df_train_data.sample(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_train_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_10pct_1 = y.sample(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Columns: 500 entries, 0 to 499\n",
      "dtypes: int64(500)\n",
      "memory usage: 7.6 MB\n"
     ]
    }
   ],
   "source": [
    "df_train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_df = df_train_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrs = list(corr_df[corr_df[corr_df.abs() >.5].count() > 1].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating DataFrame with 20 important/redundant features and eliminating noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_feat_df = df_train_data.iloc[:, corrs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>28</th>\n",
       "      <th>48</th>\n",
       "      <th>64</th>\n",
       "      <th>105</th>\n",
       "      <th>128</th>\n",
       "      <th>153</th>\n",
       "      <th>241</th>\n",
       "      <th>281</th>\n",
       "      <th>318</th>\n",
       "      <th>336</th>\n",
       "      <th>338</th>\n",
       "      <th>378</th>\n",
       "      <th>433</th>\n",
       "      <th>442</th>\n",
       "      <th>451</th>\n",
       "      <th>453</th>\n",
       "      <th>455</th>\n",
       "      <th>472</th>\n",
       "      <th>475</th>\n",
       "      <th>493</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>459</td>\n",
       "      <td>440</td>\n",
       "      <td>648</td>\n",
       "      <td>181</td>\n",
       "      <td>452</td>\n",
       "      <td>575</td>\n",
       "      <td>434</td>\n",
       "      <td>517</td>\n",
       "      <td>414</td>\n",
       "      <td>658</td>\n",
       "      <td>628</td>\n",
       "      <td>419</td>\n",
       "      <td>533</td>\n",
       "      <td>568</td>\n",
       "      <td>463</td>\n",
       "      <td>471</td>\n",
       "      <td>630</td>\n",
       "      <td>515</td>\n",
       "      <td>401</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>475</td>\n",
       "      <td>499</td>\n",
       "      <td>488</td>\n",
       "      <td>431</td>\n",
       "      <td>473</td>\n",
       "      <td>404</td>\n",
       "      <td>551</td>\n",
       "      <td>435</td>\n",
       "      <td>469</td>\n",
       "      <td>469</td>\n",
       "      <td>528</td>\n",
       "      <td>526</td>\n",
       "      <td>442</td>\n",
       "      <td>463</td>\n",
       "      <td>474</td>\n",
       "      <td>311</td>\n",
       "      <td>582</td>\n",
       "      <td>465</td>\n",
       "      <td>549</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>491</td>\n",
       "      <td>460</td>\n",
       "      <td>485</td>\n",
       "      <td>593</td>\n",
       "      <td>487</td>\n",
       "      <td>585</td>\n",
       "      <td>474</td>\n",
       "      <td>535</td>\n",
       "      <td>506</td>\n",
       "      <td>465</td>\n",
       "      <td>431</td>\n",
       "      <td>464</td>\n",
       "      <td>569</td>\n",
       "      <td>503</td>\n",
       "      <td>481</td>\n",
       "      <td>606</td>\n",
       "      <td>424</td>\n",
       "      <td>485</td>\n",
       "      <td>454</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>472</td>\n",
       "      <td>529</td>\n",
       "      <td>415</td>\n",
       "      <td>698</td>\n",
       "      <td>493</td>\n",
       "      <td>591</td>\n",
       "      <td>569</td>\n",
       "      <td>526</td>\n",
       "      <td>458</td>\n",
       "      <td>398</td>\n",
       "      <td>377</td>\n",
       "      <td>553</td>\n",
       "      <td>565</td>\n",
       "      <td>447</td>\n",
       "      <td>472</td>\n",
       "      <td>545</td>\n",
       "      <td>456</td>\n",
       "      <td>457</td>\n",
       "      <td>602</td>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>472</td>\n",
       "      <td>429</td>\n",
       "      <td>387</td>\n",
       "      <td>451</td>\n",
       "      <td>475</td>\n",
       "      <td>448</td>\n",
       "      <td>538</td>\n",
       "      <td>456</td>\n",
       "      <td>462</td>\n",
       "      <td>385</td>\n",
       "      <td>509</td>\n",
       "      <td>424</td>\n",
       "      <td>462</td>\n",
       "      <td>536</td>\n",
       "      <td>472</td>\n",
       "      <td>426</td>\n",
       "      <td>465</td>\n",
       "      <td>500</td>\n",
       "      <td>560</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   28   48   64   105  128  153  241  281  318  336  338  378  433  442  451  \\\n",
       "0  459  440  648  181  452  575  434  517  414  658  628  419  533  568  463   \n",
       "1  475  499  488  431  473  404  551  435  469  469  528  526  442  463  474   \n",
       "2  491  460  485  593  487  585  474  535  506  465  431  464  569  503  481   \n",
       "3  472  529  415  698  493  591  569  526  458  398  377  553  565  447  472   \n",
       "4  472  429  387  451  475  448  538  456  462  385  509  424  462  536  472   \n",
       "\n",
       "   453  455  472  475  493  \n",
       "0  471  630  515  401  485  \n",
       "1  311  582  465  549  338  \n",
       "2  606  424  485  454  650  \n",
       "3  545  456  457  602  572  \n",
       "4  426  465  500  560  435  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_feat_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA To Determine 5 Informative Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=20, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=20)\n",
    "pca.fit(imp_feat_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [  9.45492216e-02,   3.57761347e-02,  -4.08954063e-02,\n",
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       "       [  2.74702281e-03,   8.27428385e-01,  -5.70810854e-02,\n",
       "         -4.13201239e-02,   1.82814373e-02,   2.29983506e-02,\n",
       "         -2.84164369e-02,  -1.83425411e-02,   2.88598376e-02,\n",
       "         -1.92653108e-02,   9.44672983e-03,  -5.45195182e-01,\n",
       "          3.09881180e-02,   2.75249953e-02,   2.84509495e-03,\n",
       "         -1.60927688e-02,  -1.29251892e-02,  -8.07565612e-02,\n",
       "         -3.41937934e-02,   2.84809473e-03],\n",
       "       [  8.22178230e-03,  -9.69664293e-02,  -3.62242298e-02,\n",
       "          1.49686240e-02,  -9.60064319e-03,  -3.31611713e-02,\n",
       "         -1.90164430e-01,  -1.85341971e-01,  -4.22739697e-01,\n",
       "          7.37008100e-03,   3.10005847e-03,  -2.45207113e-02,\n",
       "         -5.89626616e-02,   6.82451404e-02,   7.18003692e-03,\n",
       "          4.21646547e-02,  -3.85090955e-02,  -8.50231327e-01,\n",
       "         -3.35258707e-02,   4.94971883e-02],\n",
       "       [  8.94131978e-01,   1.86118935e-02,  -6.20642769e-03,\n",
       "         -2.86110639e-02,   1.85040552e-01,   1.36189464e-02,\n",
       "          4.01511674e-02,   1.99989559e-02,  -2.10565937e-01,\n",
       "          6.90991895e-04,   1.65457982e-02,   1.59678770e-02,\n",
       "          2.14998594e-02,   3.29761622e-02,   3.24047499e-01,\n",
       "          6.27449112e-04,   2.65792319e-02,   9.59564753e-02,\n",
       "          3.92575594e-02,   2.75724543e-03],\n",
       "       [  2.61880477e-01,   4.20566609e-02,  -1.13057887e-02,\n",
       "          2.01965185e-02,  -9.47061880e-01,  -1.89094830e-03,\n",
       "          3.12403374e-03,  -3.79006568e-04,   1.81215385e-02,\n",
       "         -1.38881577e-02,  -1.84065397e-02,   3.32640814e-02,\n",
       "         -2.74818392e-04,   4.48771065e-03,  -1.70840083e-01,\n",
       "          9.29491588e-03,  -2.82807190e-02,  -7.15955940e-04,\n",
       "          8.55240658e-03,   6.44046160e-03],\n",
       "       [ -2.96090613e-01,   1.52986376e-02,  -3.57309542e-03,\n",
       "          1.10058290e-03,  -2.47236536e-01,   2.98961101e-03,\n",
       "          1.25302324e-02,   6.81633009e-03,  -3.85026623e-02,\n",
       "         -4.47167385e-03,   9.13739522e-06,   1.25795297e-02,\n",
       "          2.39825071e-03,   8.67423165e-03,   9.21088497e-01,\n",
       "          4.38312919e-03,  -1.93341824e-03,   2.10018621e-02,\n",
       "          1.13257179e-02,   2.44965973e-03]])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb360d49b00>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8zm/m4PloSPmc6+f/bOq6by62WisaWa3WLcCQKrb/AvStrecXkUufzWYnPiWL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb360fc8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "\n",
    "X = np.arange(1,21)\n",
    "cumulative_explained_variance_uci = np.cumsum(pca.explained_variance_ratio_)\n",
    "\n",
    "plt.plot(X, cumulative_explained_variance_uci, '-o')\n",
    "plt.bar(X, pca.explained_variance_ratio_, align='center', alpha=0.5)\n",
    "\n",
    "for i, j in zip(X, np.cumsum(pca.explained_variance_ratio_)):\n",
    "    plt.annotate(str(j.round(4)), xy=(i+.2,j-.02))\n",
    "    \n",
    "plt.xlabel('PCA components')\n",
    "plt.ylabel('Explained variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_pca = pca.transform(imp_feat_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2000, 20), (2000,))"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca.shape, y_10pct_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_pca,\n",
    "                                                    y,\n",
    "                                                    test_size = .3,\n",
    "                                                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing Final Model with KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_pipe = Pipeline([\n",
    "    ('knn', KNeighborsClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_params = {\n",
    "    'knn__n_neighbors': range(1,10,2)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_gs = GridSearchCV(knn_pipe, param_grid=knn_params, cv=5, n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Pipeline(steps=[('knn', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'))]),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid={'knn__n_neighbors': range(1, 10, 2)},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_gs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>0</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.00188656</td>\n",
       "      <td>0.00237298</td>\n",
       "      <td>0.00187302</td>\n",
       "      <td>0.00193138</td>\n",
       "      <td>0.00185552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.00386395</td>\n",
       "      <td>0.00431767</td>\n",
       "      <td>0.00477853</td>\n",
       "      <td>0.00320024</td>\n",
       "      <td>0.0049716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.875</td>\n",
       "      <td>0.866429</td>\n",
       "      <td>0.866429</td>\n",
       "      <td>0.860714</td>\n",
       "      <td>0.857857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_train_score</th>\n",
       "      <td>0.929464</td>\n",
       "      <td>0.913035</td>\n",
       "      <td>0.895715</td>\n",
       "      <td>1</td>\n",
       "      <td>0.891784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_knn__n_neighbors</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'knn__n_neighbors': 3}</td>\n",
       "      <td>{'knn__n_neighbors': 5}</td>\n",
       "      <td>{'knn__n_neighbors': 7}</td>\n",
       "      <td>{'knn__n_neighbors': 1}</td>\n",
       "      <td>{'knn__n_neighbors': 9}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>0.88968</td>\n",
       "      <td>0.871886</td>\n",
       "      <td>0.896797</td>\n",
       "      <td>0.857651</td>\n",
       "      <td>0.886121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_train_score</th>\n",
       "      <td>0.931189</td>\n",
       "      <td>0.911528</td>\n",
       "      <td>0.892761</td>\n",
       "      <td>1</td>\n",
       "      <td>0.881144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>0.867857</td>\n",
       "      <td>0.867857</td>\n",
       "      <td>0.846429</td>\n",
       "      <td>0.878571</td>\n",
       "      <td>0.846429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_train_score</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.904464</td>\n",
       "      <td>0.896429</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>0.892857</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.889286</td>\n",
       "      <td>0.882143</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_train_score</th>\n",
       "      <td>0.926786</td>\n",
       "      <td>0.917857</td>\n",
       "      <td>0.895536</td>\n",
       "      <td>1</td>\n",
       "      <td>0.886607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>0.839286</td>\n",
       "      <td>0.832143</td>\n",
       "      <td>0.810714</td>\n",
       "      <td>0.810714</td>\n",
       "      <td>0.792857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_train_score</th>\n",
       "      <td>0.930357</td>\n",
       "      <td>0.916071</td>\n",
       "      <td>0.90625</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>0.885305</td>\n",
       "      <td>0.874552</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.874552</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_train_score</th>\n",
       "      <td>0.930419</td>\n",
       "      <td>0.915254</td>\n",
       "      <td>0.8876</td>\n",
       "      <td>1</td>\n",
       "      <td>0.891169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>3.25444e-05</td>\n",
       "      <td>0.000886916</td>\n",
       "      <td>3.77127e-05</td>\n",
       "      <td>9.51399e-05</td>\n",
       "      <td>1.21744e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>0.000133298</td>\n",
       "      <td>0.000144277</td>\n",
       "      <td>0.000298193</td>\n",
       "      <td>0.000110567</td>\n",
       "      <td>0.000104091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0.0198367</td>\n",
       "      <td>0.0181391</td>\n",
       "      <td>0.0330295</td>\n",
       "      <td>0.0263753</td>\n",
       "      <td>0.035807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_train_score</th>\n",
       "      <td>0.00159086</td>\n",
       "      <td>0.00475659</td>\n",
       "      <td>0.0061008</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00742154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              1                        2  \\\n",
       "mean_fit_time                        0.00188656               0.00237298   \n",
       "mean_score_time                      0.00386395               0.00431767   \n",
       "mean_test_score                           0.875                 0.866429   \n",
       "mean_train_score                       0.929464                 0.913035   \n",
       "param_knn__n_neighbors                        3                        5   \n",
       "params                  {'knn__n_neighbors': 3}  {'knn__n_neighbors': 5}   \n",
       "rank_test_score                               1                        2   \n",
       "split0_test_score                       0.88968                 0.871886   \n",
       "split0_train_score                     0.931189                 0.911528   \n",
       "split1_test_score                      0.867857                 0.867857   \n",
       "split1_train_score                     0.928571                 0.904464   \n",
       "split2_test_score                      0.892857                 0.885714   \n",
       "split2_train_score                     0.926786                 0.917857   \n",
       "split3_test_score                      0.839286                 0.832143   \n",
       "split3_train_score                     0.930357                 0.916071   \n",
       "split4_test_score                      0.885305                 0.874552   \n",
       "split4_train_score                     0.930419                 0.915254   \n",
       "std_fit_time                        3.25444e-05              0.000886916   \n",
       "std_score_time                      0.000133298              0.000144277   \n",
       "std_test_score                        0.0198367                0.0181391   \n",
       "std_train_score                      0.00159086               0.00475659   \n",
       "\n",
       "                                              3                        0  \\\n",
       "mean_fit_time                        0.00187302               0.00193138   \n",
       "mean_score_time                      0.00477853               0.00320024   \n",
       "mean_test_score                        0.866429                 0.860714   \n",
       "mean_train_score                       0.895715                        1   \n",
       "param_knn__n_neighbors                        7                        1   \n",
       "params                  {'knn__n_neighbors': 7}  {'knn__n_neighbors': 1}   \n",
       "rank_test_score                               2                        4   \n",
       "split0_test_score                      0.896797                 0.857651   \n",
       "split0_train_score                     0.892761                        1   \n",
       "split1_test_score                      0.846429                 0.878571   \n",
       "split1_train_score                     0.896429                        1   \n",
       "split2_test_score                      0.889286                 0.882143   \n",
       "split2_train_score                     0.895536                        1   \n",
       "split3_test_score                      0.810714                 0.810714   \n",
       "split3_train_score                      0.90625                        1   \n",
       "split4_test_score                      0.888889                 0.874552   \n",
       "split4_train_score                       0.8876                        1   \n",
       "std_fit_time                        3.77127e-05              9.51399e-05   \n",
       "std_score_time                      0.000298193              0.000110567   \n",
       "std_test_score                        0.0330295                0.0263753   \n",
       "std_train_score                       0.0061008                        0   \n",
       "\n",
       "                                              4  \n",
       "mean_fit_time                        0.00185552  \n",
       "mean_score_time                       0.0049716  \n",
       "mean_test_score                        0.857857  \n",
       "mean_train_score                       0.891784  \n",
       "param_knn__n_neighbors                        9  \n",
       "params                  {'knn__n_neighbors': 9}  \n",
       "rank_test_score                               5  \n",
       "split0_test_score                      0.886121  \n",
       "split0_train_score                     0.881144  \n",
       "split1_test_score                      0.846429  \n",
       "split1_train_score                          0.9  \n",
       "split2_test_score                         0.875  \n",
       "split2_train_score                     0.886607  \n",
       "split3_test_score                      0.792857  \n",
       "split3_train_score                          0.9  \n",
       "split4_test_score                      0.888889  \n",
       "split4_train_score                     0.891169  \n",
       "std_fit_time                        1.21744e-05  \n",
       "std_score_time                      0.000104091  \n",
       "std_test_score                         0.035807  \n",
       "std_train_score                      0.00742154  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(knn_gs.cv_results_).sort_values(by='rank_test_score').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89000000000000001"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.84      0.91      0.87       292\n",
      "          1       0.91      0.83      0.87       308\n",
      "\n",
      "avg / total       0.87      0.87      0.87       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing Final Model with DECISION TREE CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc_pipe =  Pipeline([\n",
    "    ('dtc', DecisionTreeClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc_parms = {\n",
    "    'dtc__max_depth': [3,7,11,None],\n",
    "    'dtc__max_features': [3,5,7,None]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc_gs = GridSearchCV(dtc_pipe, dtc_parms, cv = 5, verbose = 1, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Pipeline(steps=[('dtc', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best'))]),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid={'dtc__max_depth': [3, 7, 11, None], 'dtc__max_features': [3, 5, 7, None]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc_gs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>7</th>\n",
       "      <th>11</th>\n",
       "      <th>15</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>6</th>\n",
       "      <th>14</th>\n",
       "      <th>3</th>\n",
       "      <th>13</th>\n",
       "      <th>5</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>0</th>\n",
       "      <th>8</th>\n",
       "      <th>12</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.0157938</td>\n",
       "      <td>0.0194298</td>\n",
       "      <td>0.0203403</td>\n",
       "      <td>0.00656409</td>\n",
       "      <td>0.00837302</td>\n",
       "      <td>0.00658445</td>\n",
       "      <td>0.00914054</td>\n",
       "      <td>0.0085094</td>\n",
       "      <td>0.00704923</td>\n",
       "      <td>0.0057796</td>\n",
       "      <td>0.00387607</td>\n",
       "      <td>0.00375586</td>\n",
       "      <td>0.0028059</td>\n",
       "      <td>0.00479817</td>\n",
       "      <td>0.00560813</td>\n",
       "      <td>0.00317168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.000482512</td>\n",
       "      <td>0.000432253</td>\n",
       "      <td>0.000551081</td>\n",
       "      <td>0.000374031</td>\n",
       "      <td>0.000415754</td>\n",
       "      <td>0.000470018</td>\n",
       "      <td>0.000457048</td>\n",
       "      <td>0.000371265</td>\n",
       "      <td>0.000416183</td>\n",
       "      <td>0.000364208</td>\n",
       "      <td>0.000359583</td>\n",
       "      <td>0.000375462</td>\n",
       "      <td>0.00036397</td>\n",
       "      <td>0.000384951</td>\n",
       "      <td>0.000395584</td>\n",
       "      <td>0.000361347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.727857</td>\n",
       "      <td>0.705714</td>\n",
       "      <td>0.705714</td>\n",
       "      <td>0.677857</td>\n",
       "      <td>0.676429</td>\n",
       "      <td>0.666429</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.630714</td>\n",
       "      <td>0.622143</td>\n",
       "      <td>0.613571</td>\n",
       "      <td>0.586429</td>\n",
       "      <td>0.586429</td>\n",
       "      <td>0.582857</td>\n",
       "      <td>0.556429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_train_score</th>\n",
       "      <td>0.892677</td>\n",
       "      <td>0.974996</td>\n",
       "      <td>1</td>\n",
       "      <td>0.904106</td>\n",
       "      <td>0.941614</td>\n",
       "      <td>0.828759</td>\n",
       "      <td>1</td>\n",
       "      <td>0.690355</td>\n",
       "      <td>1</td>\n",
       "      <td>0.763561</td>\n",
       "      <td>0.655352</td>\n",
       "      <td>0.717346</td>\n",
       "      <td>0.6384</td>\n",
       "      <td>0.8748</td>\n",
       "      <td>1</td>\n",
       "      <td>0.623221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_dtc__max_depth</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>None</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_dtc__max_features</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'dtc__max_depth': 7, 'dtc__max_features': None}</td>\n",
       "      <td>{'dtc__max_depth': 11, 'dtc__max_features': None}</td>\n",
       "      <td>{'dtc__max_depth': None, 'dtc__max_features': ...</td>\n",
       "      <td>{'dtc__max_depth': 11, 'dtc__max_features': 5}</td>\n",
       "      <td>{'dtc__max_depth': 11, 'dtc__max_features': 7}</td>\n",
       "      <td>{'dtc__max_depth': 7, 'dtc__max_features': 7}</td>\n",
       "      <td>{'dtc__max_depth': None, 'dtc__max_features': 7}</td>\n",
       "      <td>{'dtc__max_depth': 3, 'dtc__max_features': None}</td>\n",
       "      <td>{'dtc__max_depth': None, 'dtc__max_features': 5}</td>\n",
       "      <td>{'dtc__max_depth': 7, 'dtc__max_features': 5}</td>\n",
       "      <td>{'dtc__max_depth': 3, 'dtc__max_features': 7}</td>\n",
       "      <td>{'dtc__max_depth': 7, 'dtc__max_features': 3}</td>\n",
       "      <td>{'dtc__max_depth': 3, 'dtc__max_features': 3}</td>\n",
       "      <td>{'dtc__max_depth': 11, 'dtc__max_features': 3}</td>\n",
       "      <td>{'dtc__max_depth': None, 'dtc__max_features': 3}</td>\n",
       "      <td>{'dtc__max_depth': 3, 'dtc__max_features': 5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>0.761566</td>\n",
       "      <td>0.733096</td>\n",
       "      <td>0.761566</td>\n",
       "      <td>0.715302</td>\n",
       "      <td>0.701068</td>\n",
       "      <td>0.736655</td>\n",
       "      <td>0.69395</td>\n",
       "      <td>0.629893</td>\n",
       "      <td>0.594306</td>\n",
       "      <td>0.572954</td>\n",
       "      <td>0.6121</td>\n",
       "      <td>0.619217</td>\n",
       "      <td>0.58363</td>\n",
       "      <td>0.540925</td>\n",
       "      <td>0.637011</td>\n",
       "      <td>0.601423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_train_score</th>\n",
       "      <td>0.889187</td>\n",
       "      <td>0.954424</td>\n",
       "      <td>1</td>\n",
       "      <td>0.893655</td>\n",
       "      <td>0.928508</td>\n",
       "      <td>0.831099</td>\n",
       "      <td>1</td>\n",
       "      <td>0.679178</td>\n",
       "      <td>1</td>\n",
       "      <td>0.722073</td>\n",
       "      <td>0.673816</td>\n",
       "      <td>0.761394</td>\n",
       "      <td>0.658624</td>\n",
       "      <td>0.817694</td>\n",
       "      <td>1</td>\n",
       "      <td>0.672029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>0.703571</td>\n",
       "      <td>0.682143</td>\n",
       "      <td>0.667857</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.664286</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.578571</td>\n",
       "      <td>0.653571</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.564286</td>\n",
       "      <td>0.660714</td>\n",
       "      <td>0.546429</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.521429</td>\n",
       "      <td>0.578571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_train_score</th>\n",
       "      <td>0.89375</td>\n",
       "      <td>0.983036</td>\n",
       "      <td>1</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1</td>\n",
       "      <td>0.696429</td>\n",
       "      <td>1</td>\n",
       "      <td>0.765179</td>\n",
       "      <td>0.635714</td>\n",
       "      <td>0.773214</td>\n",
       "      <td>0.634821</td>\n",
       "      <td>0.864286</td>\n",
       "      <td>1</td>\n",
       "      <td>0.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>0.739286</td>\n",
       "      <td>0.710714</td>\n",
       "      <td>0.696429</td>\n",
       "      <td>0.635714</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>0.596429</td>\n",
       "      <td>0.621429</td>\n",
       "      <td>0.592857</td>\n",
       "      <td>0.632143</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.582143</td>\n",
       "      <td>0.482143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_train_score</th>\n",
       "      <td>0.883929</td>\n",
       "      <td>0.980357</td>\n",
       "      <td>1</td>\n",
       "      <td>0.908929</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>0.825893</td>\n",
       "      <td>1</td>\n",
       "      <td>0.675893</td>\n",
       "      <td>1</td>\n",
       "      <td>0.783929</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.772321</td>\n",
       "      <td>0.615179</td>\n",
       "      <td>0.823214</td>\n",
       "      <td>1</td>\n",
       "      <td>0.598214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.664286</td>\n",
       "      <td>0.660714</td>\n",
       "      <td>0.632143</td>\n",
       "      <td>0.653571</td>\n",
       "      <td>0.635714</td>\n",
       "      <td>0.596429</td>\n",
       "      <td>0.607143</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.646429</td>\n",
       "      <td>0.610714</td>\n",
       "      <td>0.589286</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.560714</td>\n",
       "      <td>0.510714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_train_score</th>\n",
       "      <td>0.899107</td>\n",
       "      <td>0.98125</td>\n",
       "      <td>1</td>\n",
       "      <td>0.924107</td>\n",
       "      <td>0.972321</td>\n",
       "      <td>0.849107</td>\n",
       "      <td>1</td>\n",
       "      <td>0.711607</td>\n",
       "      <td>1</td>\n",
       "      <td>0.765179</td>\n",
       "      <td>0.641071</td>\n",
       "      <td>0.655357</td>\n",
       "      <td>0.663393</td>\n",
       "      <td>0.93125</td>\n",
       "      <td>1</td>\n",
       "      <td>0.569643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>0.749104</td>\n",
       "      <td>0.738351</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0.72043</td>\n",
       "      <td>0.706093</td>\n",
       "      <td>0.684588</td>\n",
       "      <td>0.681004</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.659498</td>\n",
       "      <td>0.684588</td>\n",
       "      <td>0.731183</td>\n",
       "      <td>0.566308</td>\n",
       "      <td>0.577061</td>\n",
       "      <td>0.630824</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.609319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_train_score</th>\n",
       "      <td>0.897413</td>\n",
       "      <td>0.975914</td>\n",
       "      <td>1</td>\n",
       "      <td>0.900981</td>\n",
       "      <td>0.889384</td>\n",
       "      <td>0.780553</td>\n",
       "      <td>1</td>\n",
       "      <td>0.688671</td>\n",
       "      <td>1</td>\n",
       "      <td>0.781445</td>\n",
       "      <td>0.70116</td>\n",
       "      <td>0.624442</td>\n",
       "      <td>0.619982</td>\n",
       "      <td>0.937556</td>\n",
       "      <td>1</td>\n",
       "      <td>0.633363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>0.000119026</td>\n",
       "      <td>0.00052002</td>\n",
       "      <td>0.000499666</td>\n",
       "      <td>0.00014399</td>\n",
       "      <td>0.000594717</td>\n",
       "      <td>0.000120369</td>\n",
       "      <td>0.00021541</td>\n",
       "      <td>0.000158578</td>\n",
       "      <td>0.000649077</td>\n",
       "      <td>0.00115527</td>\n",
       "      <td>3.51567e-05</td>\n",
       "      <td>0.000113393</td>\n",
       "      <td>0.000671081</td>\n",
       "      <td>0.000401287</td>\n",
       "      <td>0.000346286</td>\n",
       "      <td>2.06834e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>5.32921e-05</td>\n",
       "      <td>4.83206e-05</td>\n",
       "      <td>6.81419e-05</td>\n",
       "      <td>2.11494e-05</td>\n",
       "      <td>2.89128e-05</td>\n",
       "      <td>0.000179041</td>\n",
       "      <td>4.59051e-05</td>\n",
       "      <td>2.50557e-05</td>\n",
       "      <td>5.81612e-05</td>\n",
       "      <td>1.3665e-05</td>\n",
       "      <td>1.03786e-05</td>\n",
       "      <td>1.81037e-05</td>\n",
       "      <td>2.64611e-05</td>\n",
       "      <td>1.368e-05</td>\n",
       "      <td>1.72944e-05</td>\n",
       "      <td>1.42419e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0.0285887</td>\n",
       "      <td>0.0286731</td>\n",
       "      <td>0.0399555</td>\n",
       "      <td>0.0377907</td>\n",
       "      <td>0.0224853</td>\n",
       "      <td>0.0462764</td>\n",
       "      <td>0.0517073</td>\n",
       "      <td>0.024209</td>\n",
       "      <td>0.0481096</td>\n",
       "      <td>0.036271</td>\n",
       "      <td>0.0570687</td>\n",
       "      <td>0.0329088</td>\n",
       "      <td>0.0342556</td>\n",
       "      <td>0.0407082</td>\n",
       "      <td>0.0402664</td>\n",
       "      <td>0.0508284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_train_score</th>\n",
       "      <td>0.00554337</td>\n",
       "      <td>0.0105504</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0115657</td>\n",
       "      <td>0.0314923</td>\n",
       "      <td>0.0266728</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0128488</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0221862</td>\n",
       "      <td>0.0281072</td>\n",
       "      <td>0.064121</td>\n",
       "      <td>0.0196209</td>\n",
       "      <td>0.0512983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0356835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                       7   \\\n",
       "mean_fit_time                                                   0.0157938   \n",
       "mean_score_time                                               0.000482512   \n",
       "mean_test_score                                                  0.727857   \n",
       "mean_train_score                                                 0.892677   \n",
       "param_dtc__max_depth                                                    7   \n",
       "param_dtc__max_features                                              None   \n",
       "params                   {'dtc__max_depth': 7, 'dtc__max_features': None}   \n",
       "rank_test_score                                                         1   \n",
       "split0_test_score                                                0.761566   \n",
       "split0_train_score                                               0.889187   \n",
       "split1_test_score                                                0.703571   \n",
       "split1_train_score                                                0.89375   \n",
       "split2_test_score                                                0.739286   \n",
       "split2_train_score                                               0.883929   \n",
       "split3_test_score                                                0.685714   \n",
       "split3_train_score                                               0.899107   \n",
       "split4_test_score                                                0.749104   \n",
       "split4_train_score                                               0.897413   \n",
       "std_fit_time                                                  0.000119026   \n",
       "std_score_time                                                5.32921e-05   \n",
       "std_test_score                                                  0.0285887   \n",
       "std_train_score                                                0.00554337   \n",
       "\n",
       "                                                                        11  \\\n",
       "mean_fit_time                                                    0.0194298   \n",
       "mean_score_time                                                0.000432253   \n",
       "mean_test_score                                                   0.705714   \n",
       "mean_train_score                                                  0.974996   \n",
       "param_dtc__max_depth                                                    11   \n",
       "param_dtc__max_features                                               None   \n",
       "params                   {'dtc__max_depth': 11, 'dtc__max_features': None}   \n",
       "rank_test_score                                                          2   \n",
       "split0_test_score                                                 0.733096   \n",
       "split0_train_score                                                0.954424   \n",
       "split1_test_score                                                 0.682143   \n",
       "split1_train_score                                                0.983036   \n",
       "split2_test_score                                                 0.710714   \n",
       "split2_train_score                                                0.980357   \n",
       "split3_test_score                                                 0.664286   \n",
       "split3_train_score                                                 0.98125   \n",
       "split4_test_score                                                 0.738351   \n",
       "split4_train_score                                                0.975914   \n",
       "std_fit_time                                                    0.00052002   \n",
       "std_score_time                                                 4.83206e-05   \n",
       "std_test_score                                                   0.0286731   \n",
       "std_train_score                                                  0.0105504   \n",
       "\n",
       "                                                                        15  \\\n",
       "mean_fit_time                                                    0.0203403   \n",
       "mean_score_time                                                0.000551081   \n",
       "mean_test_score                                                   0.705714   \n",
       "mean_train_score                                                         1   \n",
       "param_dtc__max_depth                                                  None   \n",
       "param_dtc__max_features                                               None   \n",
       "params                   {'dtc__max_depth': None, 'dtc__max_features': ...   \n",
       "rank_test_score                                                          2   \n",
       "split0_test_score                                                 0.761566   \n",
       "split0_train_score                                                       1   \n",
       "split1_test_score                                                 0.667857   \n",
       "split1_train_score                                                       1   \n",
       "split2_test_score                                                 0.696429   \n",
       "split2_train_score                                                       1   \n",
       "split3_test_score                                                 0.660714   \n",
       "split3_train_score                                                       1   \n",
       "split4_test_score                                                 0.741935   \n",
       "split4_train_score                                                       1   \n",
       "std_fit_time                                                   0.000499666   \n",
       "std_score_time                                                 6.81419e-05   \n",
       "std_test_score                                                   0.0399555   \n",
       "std_train_score                                                          0   \n",
       "\n",
       "                                                                     9   \\\n",
       "mean_fit_time                                                0.00656409   \n",
       "mean_score_time                                             0.000374031   \n",
       "mean_test_score                                                0.677857   \n",
       "mean_train_score                                               0.904106   \n",
       "param_dtc__max_depth                                                 11   \n",
       "param_dtc__max_features                                               5   \n",
       "params                   {'dtc__max_depth': 11, 'dtc__max_features': 5}   \n",
       "rank_test_score                                                       4   \n",
       "split0_test_score                                              0.715302   \n",
       "split0_train_score                                             0.893655   \n",
       "split1_test_score                                              0.685714   \n",
       "split1_train_score                                             0.892857   \n",
       "split2_test_score                                              0.635714   \n",
       "split2_train_score                                             0.908929   \n",
       "split3_test_score                                              0.632143   \n",
       "split3_train_score                                             0.924107   \n",
       "split4_test_score                                               0.72043   \n",
       "split4_train_score                                             0.900981   \n",
       "std_fit_time                                                 0.00014399   \n",
       "std_score_time                                              2.11494e-05   \n",
       "std_test_score                                                0.0377907   \n",
       "std_train_score                                               0.0115657   \n",
       "\n",
       "                                                                     10  \\\n",
       "mean_fit_time                                                0.00837302   \n",
       "mean_score_time                                             0.000415754   \n",
       "mean_test_score                                                0.676429   \n",
       "mean_train_score                                               0.941614   \n",
       "param_dtc__max_depth                                                 11   \n",
       "param_dtc__max_features                                               7   \n",
       "params                   {'dtc__max_depth': 11, 'dtc__max_features': 7}   \n",
       "rank_test_score                                                       5   \n",
       "split0_test_score                                              0.701068   \n",
       "split0_train_score                                             0.928508   \n",
       "split1_test_score                                              0.664286   \n",
       "split1_train_score                                                0.975   \n",
       "split2_test_score                                              0.657143   \n",
       "split2_train_score                                             0.942857   \n",
       "split3_test_score                                              0.653571   \n",
       "split3_train_score                                             0.972321   \n",
       "split4_test_score                                              0.706093   \n",
       "split4_train_score                                             0.889384   \n",
       "std_fit_time                                                0.000594717   \n",
       "std_score_time                                              2.89128e-05   \n",
       "std_test_score                                                0.0224853   \n",
       "std_train_score                                               0.0314923   \n",
       "\n",
       "                                                                    6   \\\n",
       "mean_fit_time                                               0.00658445   \n",
       "mean_score_time                                            0.000470018   \n",
       "mean_test_score                                               0.666429   \n",
       "mean_train_score                                              0.828759   \n",
       "param_dtc__max_depth                                                 7   \n",
       "param_dtc__max_features                                              7   \n",
       "params                   {'dtc__max_depth': 7, 'dtc__max_features': 7}   \n",
       "rank_test_score                                                      6   \n",
       "split0_test_score                                             0.736655   \n",
       "split0_train_score                                            0.831099   \n",
       "split1_test_score                                                0.675   \n",
       "split1_train_score                                            0.857143   \n",
       "split2_test_score                                                  0.6   \n",
       "split2_train_score                                            0.825893   \n",
       "split3_test_score                                             0.635714   \n",
       "split3_train_score                                            0.849107   \n",
       "split4_test_score                                             0.684588   \n",
       "split4_train_score                                            0.780553   \n",
       "std_fit_time                                               0.000120369   \n",
       "std_score_time                                             0.000179041   \n",
       "std_test_score                                               0.0462764   \n",
       "std_train_score                                              0.0266728   \n",
       "\n",
       "                                                                       14  \\\n",
       "mean_fit_time                                                  0.00914054   \n",
       "mean_score_time                                               0.000457048   \n",
       "mean_test_score                                                      0.65   \n",
       "mean_train_score                                                        1   \n",
       "param_dtc__max_depth                                                 None   \n",
       "param_dtc__max_features                                                 7   \n",
       "params                   {'dtc__max_depth': None, 'dtc__max_features': 7}   \n",
       "rank_test_score                                                         7   \n",
       "split0_test_score                                                 0.69395   \n",
       "split0_train_score                                                      1   \n",
       "split1_test_score                                                0.578571   \n",
       "split1_train_score                                                      1   \n",
       "split2_test_score                                                     0.7   \n",
       "split2_train_score                                                      1   \n",
       "split3_test_score                                                0.596429   \n",
       "split3_train_score                                                      1   \n",
       "split4_test_score                                                0.681004   \n",
       "split4_train_score                                                      1   \n",
       "std_fit_time                                                   0.00021541   \n",
       "std_score_time                                                4.59051e-05   \n",
       "std_test_score                                                  0.0517073   \n",
       "std_train_score                                                         0   \n",
       "\n",
       "                                                                       3   \\\n",
       "mean_fit_time                                                   0.0085094   \n",
       "mean_score_time                                               0.000371265   \n",
       "mean_test_score                                                     0.645   \n",
       "mean_train_score                                                 0.690355   \n",
       "param_dtc__max_depth                                                    3   \n",
       "param_dtc__max_features                                              None   \n",
       "params                   {'dtc__max_depth': 3, 'dtc__max_features': None}   \n",
       "rank_test_score                                                         8   \n",
       "split0_test_score                                                0.629893   \n",
       "split0_train_score                                               0.679178   \n",
       "split1_test_score                                                0.653571   \n",
       "split1_train_score                                               0.696429   \n",
       "split2_test_score                                                0.657143   \n",
       "split2_train_score                                               0.675893   \n",
       "split3_test_score                                                0.607143   \n",
       "split3_train_score                                               0.711607   \n",
       "split4_test_score                                                0.677419   \n",
       "split4_train_score                                               0.688671   \n",
       "std_fit_time                                                  0.000158578   \n",
       "std_score_time                                                2.50557e-05   \n",
       "std_test_score                                                   0.024209   \n",
       "std_train_score                                                 0.0128488   \n",
       "\n",
       "                                                                       13  \\\n",
       "mean_fit_time                                                  0.00704923   \n",
       "mean_score_time                                               0.000416183   \n",
       "mean_test_score                                                     0.645   \n",
       "mean_train_score                                                        1   \n",
       "param_dtc__max_depth                                                 None   \n",
       "param_dtc__max_features                                                 5   \n",
       "params                   {'dtc__max_depth': None, 'dtc__max_features': 5}   \n",
       "rank_test_score                                                         8   \n",
       "split0_test_score                                                0.594306   \n",
       "split0_train_score                                                      1   \n",
       "split1_test_score                                                   0.725   \n",
       "split1_train_score                                                      1   \n",
       "split2_test_score                                                0.596429   \n",
       "split2_train_score                                                      1   \n",
       "split3_test_score                                                    0.65   \n",
       "split3_train_score                                                      1   \n",
       "split4_test_score                                                0.659498   \n",
       "split4_train_score                                                      1   \n",
       "std_fit_time                                                  0.000649077   \n",
       "std_score_time                                                5.81612e-05   \n",
       "std_test_score                                                  0.0481096   \n",
       "std_train_score                                                         0   \n",
       "\n",
       "                                                                    5   \\\n",
       "mean_fit_time                                                0.0057796   \n",
       "mean_score_time                                            0.000364208   \n",
       "mean_test_score                                               0.630714   \n",
       "mean_train_score                                              0.763561   \n",
       "param_dtc__max_depth                                                 7   \n",
       "param_dtc__max_features                                              5   \n",
       "params                   {'dtc__max_depth': 7, 'dtc__max_features': 5}   \n",
       "rank_test_score                                                     10   \n",
       "split0_test_score                                             0.572954   \n",
       "split0_train_score                                            0.722073   \n",
       "split1_test_score                                             0.628571   \n",
       "split1_train_score                                            0.765179   \n",
       "split2_test_score                                             0.621429   \n",
       "split2_train_score                                            0.783929   \n",
       "split3_test_score                                             0.646429   \n",
       "split3_train_score                                            0.765179   \n",
       "split4_test_score                                             0.684588   \n",
       "split4_train_score                                            0.781445   \n",
       "std_fit_time                                                0.00115527   \n",
       "std_score_time                                              1.3665e-05   \n",
       "std_test_score                                                0.036271   \n",
       "std_train_score                                              0.0221862   \n",
       "\n",
       "                                                                    2   \\\n",
       "mean_fit_time                                               0.00387607   \n",
       "mean_score_time                                            0.000359583   \n",
       "mean_test_score                                               0.622143   \n",
       "mean_train_score                                              0.655352   \n",
       "param_dtc__max_depth                                                 3   \n",
       "param_dtc__max_features                                              7   \n",
       "params                   {'dtc__max_depth': 3, 'dtc__max_features': 7}   \n",
       "rank_test_score                                                     11   \n",
       "split0_test_score                                               0.6121   \n",
       "split0_train_score                                            0.673816   \n",
       "split1_test_score                                             0.564286   \n",
       "split1_train_score                                            0.635714   \n",
       "split2_test_score                                             0.592857   \n",
       "split2_train_score                                               0.625   \n",
       "split3_test_score                                             0.610714   \n",
       "split3_train_score                                            0.641071   \n",
       "split4_test_score                                             0.731183   \n",
       "split4_train_score                                             0.70116   \n",
       "std_fit_time                                               3.51567e-05   \n",
       "std_score_time                                             1.03786e-05   \n",
       "std_test_score                                               0.0570687   \n",
       "std_train_score                                              0.0281072   \n",
       "\n",
       "                                                                    4   \\\n",
       "mean_fit_time                                               0.00375586   \n",
       "mean_score_time                                            0.000375462   \n",
       "mean_test_score                                               0.613571   \n",
       "mean_train_score                                              0.717346   \n",
       "param_dtc__max_depth                                                 7   \n",
       "param_dtc__max_features                                              3   \n",
       "params                   {'dtc__max_depth': 7, 'dtc__max_features': 3}   \n",
       "rank_test_score                                                     12   \n",
       "split0_test_score                                             0.619217   \n",
       "split0_train_score                                            0.761394   \n",
       "split1_test_score                                             0.660714   \n",
       "split1_train_score                                            0.773214   \n",
       "split2_test_score                                             0.632143   \n",
       "split2_train_score                                            0.772321   \n",
       "split3_test_score                                             0.589286   \n",
       "split3_train_score                                            0.655357   \n",
       "split4_test_score                                             0.566308   \n",
       "split4_train_score                                            0.624442   \n",
       "std_fit_time                                               0.000113393   \n",
       "std_score_time                                             1.81037e-05   \n",
       "std_test_score                                               0.0329088   \n",
       "std_train_score                                               0.064121   \n",
       "\n",
       "                                                                    0   \\\n",
       "mean_fit_time                                                0.0028059   \n",
       "mean_score_time                                             0.00036397   \n",
       "mean_test_score                                               0.586429   \n",
       "mean_train_score                                                0.6384   \n",
       "param_dtc__max_depth                                                 3   \n",
       "param_dtc__max_features                                              3   \n",
       "params                   {'dtc__max_depth': 3, 'dtc__max_features': 3}   \n",
       "rank_test_score                                                     13   \n",
       "split0_test_score                                              0.58363   \n",
       "split0_train_score                                            0.658624   \n",
       "split1_test_score                                             0.546429   \n",
       "split1_train_score                                            0.634821   \n",
       "split2_test_score                                                 0.65   \n",
       "split2_train_score                                            0.615179   \n",
       "split3_test_score                                                0.575   \n",
       "split3_train_score                                            0.663393   \n",
       "split4_test_score                                             0.577061   \n",
       "split4_train_score                                            0.619982   \n",
       "std_fit_time                                               0.000671081   \n",
       "std_score_time                                             2.64611e-05   \n",
       "std_test_score                                               0.0342556   \n",
       "std_train_score                                              0.0196209   \n",
       "\n",
       "                                                                     8   \\\n",
       "mean_fit_time                                                0.00479817   \n",
       "mean_score_time                                             0.000384951   \n",
       "mean_test_score                                                0.586429   \n",
       "mean_train_score                                                 0.8748   \n",
       "param_dtc__max_depth                                                 11   \n",
       "param_dtc__max_features                                               3   \n",
       "params                   {'dtc__max_depth': 11, 'dtc__max_features': 3}   \n",
       "rank_test_score                                                      13   \n",
       "split0_test_score                                              0.540925   \n",
       "split0_train_score                                             0.817694   \n",
       "split1_test_score                                              0.535714   \n",
       "split1_train_score                                             0.864286   \n",
       "split2_test_score                                                   0.6   \n",
       "split2_train_score                                             0.823214   \n",
       "split3_test_score                                                 0.625   \n",
       "split3_train_score                                              0.93125   \n",
       "split4_test_score                                              0.630824   \n",
       "split4_train_score                                             0.937556   \n",
       "std_fit_time                                                0.000401287   \n",
       "std_score_time                                                1.368e-05   \n",
       "std_test_score                                                0.0407082   \n",
       "std_train_score                                               0.0512983   \n",
       "\n",
       "                                                                       12  \\\n",
       "mean_fit_time                                                  0.00560813   \n",
       "mean_score_time                                               0.000395584   \n",
       "mean_test_score                                                  0.582857   \n",
       "mean_train_score                                                        1   \n",
       "param_dtc__max_depth                                                 None   \n",
       "param_dtc__max_features                                                 3   \n",
       "params                   {'dtc__max_depth': None, 'dtc__max_features': 3}   \n",
       "rank_test_score                                                        15   \n",
       "split0_test_score                                                0.637011   \n",
       "split0_train_score                                                      1   \n",
       "split1_test_score                                                0.521429   \n",
       "split1_train_score                                                      1   \n",
       "split2_test_score                                                0.582143   \n",
       "split2_train_score                                                      1   \n",
       "split3_test_score                                                0.560714   \n",
       "split3_train_score                                                      1   \n",
       "split4_test_score                                                0.612903   \n",
       "split4_train_score                                                      1   \n",
       "std_fit_time                                                  0.000346286   \n",
       "std_score_time                                                1.72944e-05   \n",
       "std_test_score                                                  0.0402664   \n",
       "std_train_score                                                         0   \n",
       "\n",
       "                                                                    1   \n",
       "mean_fit_time                                               0.00317168  \n",
       "mean_score_time                                            0.000361347  \n",
       "mean_test_score                                               0.556429  \n",
       "mean_train_score                                              0.623221  \n",
       "param_dtc__max_depth                                                 3  \n",
       "param_dtc__max_features                                              5  \n",
       "params                   {'dtc__max_depth': 3, 'dtc__max_features': 5}  \n",
       "rank_test_score                                                     16  \n",
       "split0_test_score                                             0.601423  \n",
       "split0_train_score                                            0.672029  \n",
       "split1_test_score                                             0.578571  \n",
       "split1_train_score                                            0.642857  \n",
       "split2_test_score                                             0.482143  \n",
       "split2_train_score                                            0.598214  \n",
       "split3_test_score                                             0.510714  \n",
       "split3_train_score                                            0.569643  \n",
       "split4_test_score                                             0.609319  \n",
       "split4_train_score                                            0.633363  \n",
       "std_fit_time                                               2.06834e-05  \n",
       "std_score_time                                             1.42419e-05  \n",
       "std_test_score                                               0.0508284  \n",
       "std_train_score                                              0.0356835  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dtc_gs.cv_results_).sort_values(by='rank_test_score').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc_clf = DecisionTreeClassifier(max_depth=7, max_features=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=7,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76500000000000001"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dtc = dtc_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.77      0.77      0.77       306\n",
      "          1       0.76      0.76      0.76       294\n",
      "\n",
      "avg / total       0.76      0.77      0.76       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_dtc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing Final Model with LOGISTIC REGRESSION\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_pipe_1 = Pipeline([\n",
    "    ('lnr',  LogisticRegression())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_param_1 = {\n",
    "    'lnr__C': [10,25,50,75,100]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple2_gs = GridSearchCV(simple_pipe_1, param_grid=simple_param_1, cv=5, n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Pipeline(steps=[('lnr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False))]),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid={'lnr__C': [10, 25, 50, 75, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple2_gs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.0125447</td>\n",
       "      <td>0.0120023</td>\n",
       "      <td>0.0126133</td>\n",
       "      <td>0.0119197</td>\n",
       "      <td>0.0118708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.000364351</td>\n",
       "      <td>0.00036149</td>\n",
       "      <td>0.00036869</td>\n",
       "      <td>0.000350809</td>\n",
       "      <td>0.000355577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.574286</td>\n",
       "      <td>0.574286</td>\n",
       "      <td>0.574286</td>\n",
       "      <td>0.574286</td>\n",
       "      <td>0.574286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_train_score</th>\n",
       "      <td>0.610545</td>\n",
       "      <td>0.610724</td>\n",
       "      <td>0.610724</td>\n",
       "      <td>0.610545</td>\n",
       "      <td>0.610724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_lnr__C</th>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'lnr__C': 10}</td>\n",
       "      <td>{'lnr__C': 25}</td>\n",
       "      <td>{'lnr__C': 50}</td>\n",
       "      <td>{'lnr__C': 75}</td>\n",
       "      <td>{'lnr__C': 100}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>0.52669</td>\n",
       "      <td>0.52669</td>\n",
       "      <td>0.52669</td>\n",
       "      <td>0.52669</td>\n",
       "      <td>0.52669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_train_score</th>\n",
       "      <td>0.636282</td>\n",
       "      <td>0.636282</td>\n",
       "      <td>0.636282</td>\n",
       "      <td>0.636282</td>\n",
       "      <td>0.636282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.535714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_train_score</th>\n",
       "      <td>0.633929</td>\n",
       "      <td>0.633929</td>\n",
       "      <td>0.633929</td>\n",
       "      <td>0.633036</td>\n",
       "      <td>0.633929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>0.553571</td>\n",
       "      <td>0.553571</td>\n",
       "      <td>0.553571</td>\n",
       "      <td>0.553571</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_train_score</th>\n",
       "      <td>0.589286</td>\n",
       "      <td>0.589286</td>\n",
       "      <td>0.589286</td>\n",
       "      <td>0.589286</td>\n",
       "      <td>0.589286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_train_score</th>\n",
       "      <td>0.608929</td>\n",
       "      <td>0.609821</td>\n",
       "      <td>0.609821</td>\n",
       "      <td>0.609821</td>\n",
       "      <td>0.609821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>0.684588</td>\n",
       "      <td>0.684588</td>\n",
       "      <td>0.684588</td>\n",
       "      <td>0.684588</td>\n",
       "      <td>0.684588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_train_score</th>\n",
       "      <td>0.5843</td>\n",
       "      <td>0.5843</td>\n",
       "      <td>0.5843</td>\n",
       "      <td>0.5843</td>\n",
       "      <td>0.5843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>0.00181535</td>\n",
       "      <td>0.00089493</td>\n",
       "      <td>0.000750708</td>\n",
       "      <td>0.000810766</td>\n",
       "      <td>0.00094915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>1.77172e-05</td>\n",
       "      <td>1.59619e-05</td>\n",
       "      <td>3.08531e-05</td>\n",
       "      <td>7.60492e-06</td>\n",
       "      <td>1.49772e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0.0571341</td>\n",
       "      <td>0.0571341</td>\n",
       "      <td>0.0571341</td>\n",
       "      <td>0.0571341</td>\n",
       "      <td>0.0571341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_train_score</th>\n",
       "      <td>0.0216914</td>\n",
       "      <td>0.0216811</td>\n",
       "      <td>0.0216811</td>\n",
       "      <td>0.021492</td>\n",
       "      <td>0.0216811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0               1               2  \\\n",
       "mean_fit_time            0.0125447       0.0120023       0.0126133   \n",
       "mean_score_time        0.000364351      0.00036149      0.00036869   \n",
       "mean_test_score           0.574286        0.574286        0.574286   \n",
       "mean_train_score          0.610545        0.610724        0.610724   \n",
       "param_lnr__C                    10              25              50   \n",
       "params              {'lnr__C': 10}  {'lnr__C': 25}  {'lnr__C': 50}   \n",
       "rank_test_score                  1               1               1   \n",
       "split0_test_score          0.52669         0.52669         0.52669   \n",
       "split0_train_score        0.636282        0.636282        0.636282   \n",
       "split1_test_score         0.535714        0.535714        0.535714   \n",
       "split1_train_score        0.633929        0.633929        0.633929   \n",
       "split2_test_score         0.553571        0.553571        0.553571   \n",
       "split2_train_score        0.589286        0.589286        0.589286   \n",
       "split3_test_score         0.571429        0.571429        0.571429   \n",
       "split3_train_score        0.608929        0.609821        0.609821   \n",
       "split4_test_score         0.684588        0.684588        0.684588   \n",
       "split4_train_score          0.5843          0.5843          0.5843   \n",
       "std_fit_time            0.00181535      0.00089493     0.000750708   \n",
       "std_score_time         1.77172e-05     1.59619e-05     3.08531e-05   \n",
       "std_test_score           0.0571341       0.0571341       0.0571341   \n",
       "std_train_score          0.0216914       0.0216811       0.0216811   \n",
       "\n",
       "                                 3                4  \n",
       "mean_fit_time            0.0119197        0.0118708  \n",
       "mean_score_time        0.000350809      0.000355577  \n",
       "mean_test_score           0.574286         0.574286  \n",
       "mean_train_score          0.610545         0.610724  \n",
       "param_lnr__C                    75              100  \n",
       "params              {'lnr__C': 75}  {'lnr__C': 100}  \n",
       "rank_test_score                  1                1  \n",
       "split0_test_score          0.52669          0.52669  \n",
       "split0_train_score        0.636282         0.636282  \n",
       "split1_test_score         0.535714         0.535714  \n",
       "split1_train_score        0.633036         0.633929  \n",
       "split2_test_score         0.553571         0.553571  \n",
       "split2_train_score        0.589286         0.589286  \n",
       "split3_test_score         0.571429         0.571429  \n",
       "split3_train_score        0.609821         0.609821  \n",
       "split4_test_score         0.684588         0.684588  \n",
       "split4_train_score          0.5843           0.5843  \n",
       "std_fit_time           0.000810766       0.00094915  \n",
       "std_score_time         7.60492e-06      1.49772e-05  \n",
       "std_test_score           0.0571341        0.0571341  \n",
       "std_train_score           0.021492        0.0216811  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(simple2_gs.cv_results_).sort_values(by='rank_test_score').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57666666666666666"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple2_gs.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The highest average score of 0.89 was acheived using KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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