4-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import svm, neighbors\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/study-table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>occupation</th>\n",
       "      <th>weight</th>\n",
       "      <th>bp_hosp_min</th>\n",
       "      <th>bp_hosp_max</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>hiv</th>\n",
       "      <th>cancer</th>\n",
       "      <th>dexamethasone</th>\n",
       "      <th>erlotinib</th>\n",
       "      <th>bp_disch_min</th>\n",
       "      <th>bp_disch_max</th>\n",
       "      <th>readmission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>black</td>\n",
       "      <td>42956</td>\n",
       "      <td>builder</td>\n",
       "      <td>115</td>\n",
       "      <td>64</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>white</td>\n",
       "      <td>40369</td>\n",
       "      <td>engineer</td>\n",
       "      <td>112</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>asian</td>\n",
       "      <td>44890</td>\n",
       "      <td>doctor</td>\n",
       "      <td>46</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>native_american</td>\n",
       "      <td>40614</td>\n",
       "      <td>builder</td>\n",
       "      <td>104</td>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>asian</td>\n",
       "      <td>43761</td>\n",
       "      <td>firefighter</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender             race  postal_code   occupation  weight  bp_hosp_min  \\\n",
       "0  female            black        42956      builder     115           64   \n",
       "1    male            white        40369     engineer     112           66   \n",
       "2  female            asian        44890       doctor      46           55   \n",
       "3  female  native_american        40614      builder     104           41   \n",
       "4    male            asian        43761  firefighter      67           49   \n",
       "\n",
       "   bp_hosp_max  diabetes  hiv  cancer  dexamethasone  erlotinib  bp_disch_min  \\\n",
       "0           88         1    0       0              1          0            40   \n",
       "1           75         0    0       1              0          1            40   \n",
       "2           80         0    1       1              0          0            41   \n",
       "3          103         0    1       1              0          0            40   \n",
       "4          103         0    0       0              1          0            40   \n",
       "\n",
       "   bp_disch_max  readmission  \n",
       "0           109            0  \n",
       "1           108            0  \n",
       "2           106            0  \n",
       "3           109            1  \n",
       "4           109            0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q7MuKC4o1o4Yd1ktjnZyMsrqLXqO8aprSjtqQJOMu7wKcANwOHEe0XvaVwAuA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\n/nemHab8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ef04f5a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ix0 = df['readmission'] == 0\n",
    "ix1 = df['readmission'] == 1\n",
    "\n",
    "plt.scatter(df.loc[ix0, 'weight'].values, df.loc[ix0, 'bp_hosp_max'].values, c='b')\n",
    "plt.scatter(df.loc[ix1, 'weight'].values, df.loc[ix1, 'bp_hosp_max'].values, c='r')\n",
    "plt.xlabel('weight')\n",
    "plt.ylabel('bp_hosp_max')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the outcomes from the input features\n",
    "outcomes = df['readmission'].values\n",
    "df = df.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>occupation</th>\n",
       "      <th>weight</th>\n",
       "      <th>bp_hosp_min</th>\n",
       "      <th>bp_hosp_max</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>hiv</th>\n",
       "      <th>cancer</th>\n",
       "      <th>dexamethasone</th>\n",
       "      <th>erlotinib</th>\n",
       "      <th>bp_disch_min</th>\n",
       "      <th>bp_disch_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>black</td>\n",
       "      <td>42956</td>\n",
       "      <td>builder</td>\n",
       "      <td>115</td>\n",
       "      <td>64</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>white</td>\n",
       "      <td>40369</td>\n",
       "      <td>engineer</td>\n",
       "      <td>112</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>asian</td>\n",
       "      <td>44890</td>\n",
       "      <td>doctor</td>\n",
       "      <td>46</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>native_american</td>\n",
       "      <td>40614</td>\n",
       "      <td>builder</td>\n",
       "      <td>104</td>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>asian</td>\n",
       "      <td>43761</td>\n",
       "      <td>firefighter</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender             race  postal_code   occupation  weight  bp_hosp_min  \\\n",
       "0  female            black        42956      builder     115           64   \n",
       "1    male            white        40369     engineer     112           66   \n",
       "2  female            asian        44890       doctor      46           55   \n",
       "3  female  native_american        40614      builder     104           41   \n",
       "4    male            asian        43761  firefighter      67           49   \n",
       "\n",
       "   bp_hosp_max  diabetes  hiv  cancer  dexamethasone  erlotinib  bp_disch_min  \\\n",
       "0           88         1    0       0              1          0            40   \n",
       "1           75         0    0       1              0          1            40   \n",
       "2           80         0    1       1              0          0            41   \n",
       "3          103         0    1       1              0          0            40   \n",
       "4          103         0    0       0              1          0            40   \n",
       "\n",
       "   bp_disch_max  \n",
       "0           109  \n",
       "1           108  \n",
       "2           106  \n",
       "3           109  \n",
       "4           109  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WAIT, STILL NEED PREPROCESSING!\n",
    "\n",
    "# Convert categorical features\n",
    "\n",
    "categoricals = []\n",
    "\n",
    "for feature in ['gender', 'race', 'postal_code', 'occupation']:\n",
    "    categoricals.append(pd.get_dummies(df[feature], prefix=feature))\n",
    "    del(df[feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df] + categoricals, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "      <th>bp_hosp_min</th>\n",
       "      <th>bp_hosp_max</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>hiv</th>\n",
       "      <th>cancer</th>\n",
       "      <th>dexamethasone</th>\n",
       "      <th>erlotinib</th>\n",
       "      <th>bp_disch_min</th>\n",
       "      <th>bp_disch_max</th>\n",
       "      <th>...</th>\n",
       "      <th>postal_code_44909</th>\n",
       "      <th>postal_code_44929</th>\n",
       "      <th>postal_code_44931</th>\n",
       "      <th>postal_code_44937</th>\n",
       "      <th>postal_code_44976</th>\n",
       "      <th>occupation_builder</th>\n",
       "      <th>occupation_doctor</th>\n",
       "      <th>occupation_engineer</th>\n",
       "      <th>occupation_firefighter</th>\n",
       "      <th>occupation_teacher</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>109</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>109</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 222 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight  bp_hosp_min  bp_hosp_max  diabetes  hiv  cancer  dexamethasone  \\\n",
       "0     115           64           88         1    0       0              1   \n",
       "1     112           66           75         0    0       1              0   \n",
       "2      46           55           80         0    1       1              0   \n",
       "3     104           41          103         0    1       1              0   \n",
       "4      67           49          103         0    0       0              1   \n",
       "\n",
       "   erlotinib  bp_disch_min  bp_disch_max         ...          \\\n",
       "0          0            40           109         ...           \n",
       "1          1            40           108         ...           \n",
       "2          0            41           106         ...           \n",
       "3          0            40           109         ...           \n",
       "4          0            40           109         ...           \n",
       "\n",
       "   postal_code_44909  postal_code_44929  postal_code_44931  postal_code_44937  \\\n",
       "0                  0                  0                  0                  0   \n",
       "1                  0                  0                  0                  0   \n",
       "2                  0                  0                  0                  0   \n",
       "3                  0                  0                  0                  0   \n",
       "4                  0                  0                  0                  0   \n",
       "\n",
       "   postal_code_44976  occupation_builder  occupation_doctor  \\\n",
       "0                  0                   1                  0   \n",
       "1                  0                   0                  0   \n",
       "2                  0                   0                  1   \n",
       "3                  0                   1                  0   \n",
       "4                  0                   0                  0   \n",
       "\n",
       "   occupation_engineer  occupation_firefighter  occupation_teacher  \n",
       "0                    0                       0                   0  \n",
       "1                    1                       0                   0  \n",
       "2                    0                       0                   0  \n",
       "3                    0                       0                   0  \n",
       "4                    0                       1                   0  \n",
       "\n",
       "[5 rows x 222 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training records: 157\n",
      "Number of testing records: 53\n"
     ]
    }
   ],
   "source": [
    "# Split data into training and testing sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(df, outcomes,\n",
    "                                                    train_size=0.75, test_size=0.25,\n",
    "                                                    random_state=0)\n",
    "print('Number of training records: %d' % len(x_train))\n",
    "print('Number of testing records: %d' % len(x_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LR\n",
    "clf_lr = LogisticRegression()\n",
    "clf_lr.fit(x_train, y_train)\n",
    "y_predict_lr = clf_lr.predict(x_test)\n",
    "\n",
    "# KNN\n",
    "clf_knn = neighbors.KNeighborsClassifier()\n",
    "clf_knn.fit(x_train, y_train)\n",
    "y_predict_knn = clf_knn.predict(x_test)\n",
    "\n",
    "# SVM\n",
    "clf_svm = svm.SVC()\n",
    "clf_svm.fit(x_train, y_train)\n",
    "y_predict_svm = clf_svm.predict(x_test)\n",
    "\n",
    "# And GB\n",
    "clf_gb = GradientBoostingClassifier()\n",
    "clf_gb.fit(x_train, y_train)\n",
    "y_predict_gb = clf_gb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_matrix_df(y_true, y_pred):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    cm = pd.DataFrame(cm, columns=['Predict 0', 'Predict 1'], index=['Actual 0', 'Actual 1'])\n",
    "    return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR results:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>Predict 0</th>\n",
       "      <th>Predict 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
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       "      <th>Actual 1</th>\n",
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      "text/plain": [
       "          Predict 0  Predict 1\n",
       "Actual 0         33          6\n",
       "Actual 1         13          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients of first 10 features: \n",
      "['weight', 'bp_hosp_min', 'bp_hosp_max', 'diabetes', 'hiv', 'cancer', 'dexamethasone', 'erlotinib', 'bp_disch_min', 'bp_disch_max']\n",
      "[ 0.01380815 -0.01544398 -0.00195594 -0.33848319 -0.2373213  -0.49754355\n",
      " -0.09407885 -0.76408747  0.14091474 -0.05684233] \n",
      "\n",
      "KNN results:\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>Predict 0</th>\n",
       "      <th>Predict 1</th>\n",
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      "text/plain": [
       "          Predict 0  Predict 1\n",
       "Actual 0         29         10\n",
       "Actual 1         10          4"
      ]
     },
     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM results:\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Predict 0</th>\n",
       "      <th>Predict 1</th>\n",
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       "          Predict 0  Predict 1\n",
       "Actual 0         38          1\n",
       "Actual 1         13          1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB results:\n"
     ]
    },
    {
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       "          Predict 0  Predict 1\n",
       "Actual 0         38          1\n",
       "Actual 1         14          0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "print('LR results:')\n",
    "display(confusion_matrix_df(y_test, y_predict_lr))\n",
    "print('Coefficients of first 10 features: ')\n",
    "print(list(df.columns[:10]))\n",
    "print(clf_lr.coef_[0][:10], '\\n')\n",
    "\n",
    "print('KNN results:')\n",
    "display(confusion_matrix_df(y_test, y_predict_knn))\n",
    "\n",
    "print('SVM results:')\n",
    "display(confusion_matrix_df(y_test, y_predict_svm))\n",
    "\n",
    "print('GB results:')\n",
    "display(confusion_matrix_df(y_test, y_predict_gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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