AI CourseWork Final.ipynb

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    "# 55-700241 Applicable Artificial Intelligence (Binary Classification)\n",
    "1.\tOutline of the Task\n",
    "The objective of the coursework is to design, implement and evaluate a Neural Network for data classification and write a report on it. The neural network is to be developed using Matlab.\n",
    "\n",
    "2.\tThe data\n",
    "A subset of the Heart Disease (Cleveland) data set is provided via Blackboard (file named cleveland_heart_disease_dataset_labelled.mat). The data has the following properties:\n",
    "•\tThis is a cleaned up subset of 14 features from a full set of 75,\n",
    "•\tIt contains multiple classes (0: no heart disease, 1: mild heart disease, 2: severe heart disease),\n",
    "•\tThe majority of experiments in the literature focus on detecting presence (1 or 2) from absence (0),\n",
    "•\tCurrent state of the art is around 90% accuracy.\n",
    "\n",
    "The task is to design a neural network to achieve a cross validated classification rate as close as possible to current state of the art. \n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3a64943",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988997b4",
   "metadata": {},
   "source": [
    "#### Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cdd51d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load data\n",
    "heart_df = pd.read_csv('./heart_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53346673",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>CP</th>\n",
       "      <th>Trestbps</th>\n",
       "      <th>Chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  CP  Trestbps  Chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   1       145   233    1        2      150      0      2.3      3   \n",
       "1   67    1   4       160   286    0        2      108      1      1.5      2   \n",
       "2   37    1   3       130   250    0        0      187      0      3.5      3   \n",
       "3   41    0   2       130   204    0        2      172      0      1.4      1   \n",
       "4   56    1   2       120   236    0        0      178      0      0.8      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     6       0  \n",
       "1   3     3       1  \n",
       "2   0     3       0  \n",
       "3   0     3       0  \n",
       "4   0     3       0  "
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "heart_df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "abeb067f",
   "metadata": {},
   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 297 entries, 0 to 296\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Age       297 non-null    int64  \n",
      " 1   Sex       297 non-null    int64  \n",
      " 2   CP        297 non-null    int64  \n",
      " 3   Trestbps  297 non-null    int64  \n",
      " 4   Chol      297 non-null    int64  \n",
      " 5   fbs       297 non-null    int64  \n",
      " 6   restecg   297 non-null    int64  \n",
      " 7   thalach   297 non-null    int64  \n",
      " 8   exang     297 non-null    int64  \n",
      " 9   oldpeak   297 non-null    float64\n",
      " 10  slope     297 non-null    int64  \n",
      " 11  ca        297 non-null    int64  \n",
      " 12  thal      297 non-null    int64  \n",
      " 13  target    297 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 32.6 KB\n"
     ]
    }
   ],
   "source": [
    "heart_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59a7f59c",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>CP</th>\n",
       "      <th>Trestbps</th>\n",
       "      <th>Chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.542088</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>3.158249</td>\n",
       "      <td>131.693603</td>\n",
       "      <td>247.350168</td>\n",
       "      <td>0.144781</td>\n",
       "      <td>0.996633</td>\n",
       "      <td>149.599327</td>\n",
       "      <td>0.326599</td>\n",
       "      <td>1.055556</td>\n",
       "      <td>1.602694</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>4.730640</td>\n",
       "      <td>0.622896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.049736</td>\n",
       "      <td>0.468500</td>\n",
       "      <td>0.964859</td>\n",
       "      <td>17.762806</td>\n",
       "      <td>51.997583</td>\n",
       "      <td>0.352474</td>\n",
       "      <td>0.994914</td>\n",
       "      <td>22.941562</td>\n",
       "      <td>0.469761</td>\n",
       "      <td>1.166123</td>\n",
       "      <td>0.618187</td>\n",
       "      <td>0.938965</td>\n",
       "      <td>1.938629</td>\n",
       "      <td>0.748341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "              Age         Sex          CP    Trestbps        Chol         fbs  \\\n",
       "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
       "mean    54.542088    0.676768    3.158249  131.693603  247.350168    0.144781   \n",
       "std      9.049736    0.468500    0.964859   17.762806   51.997583    0.352474   \n",
       "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.000000    0.000000    3.000000  120.000000  211.000000    0.000000   \n",
       "50%     56.000000    1.000000    3.000000  130.000000  243.000000    0.000000   \n",
       "75%     61.000000    1.000000    4.000000  140.000000  276.000000    0.000000   \n",
       "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
       "mean     0.996633  149.599327    0.326599    1.055556    1.602694    0.676768   \n",
       "std      0.994914   22.941562    0.469761    1.166123    0.618187    0.938965   \n",
       "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "25%      0.000000  133.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000   \n",
       "75%      2.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  297.000000  297.000000  \n",
       "mean     4.730640    0.622896  \n",
       "std      1.938629    0.748341  \n",
       "min      3.000000    0.000000  \n",
       "25%      3.000000    0.000000  \n",
       "50%      3.000000    0.000000  \n",
       "75%      7.000000    1.000000  \n",
       "max      7.000000    2.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "93cec9f8",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1200x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_df.hist(bins = 50, figsize = (12,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b0d3f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  background-color: #7da0f9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row3_col7, #T_095c0_row5_col9, #T_095c0_row5_col12, #T_095c0_row11_col4 {\n",
       "  background-color: #92b4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row3_col8 {\n",
       "  background-color: #a7c5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row3_col9 {\n",
       "  background-color: #c0d4f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row3_col10 {\n",
       "  background-color: #b6cefa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row3_col11, #T_095c0_row4_col9, #T_095c0_row8_col1 {\n",
       "  background-color: #9abbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row3_col12, #T_095c0_row4_col8 {\n",
       "  background-color: #a6c4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row3_col13, #T_095c0_row12_col11 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row4_col2 {\n",
       "  background-color: #a1c0ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row4_col3 {\n",
       "  background-color: #7699f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row4_col5 {\n",
       "  background-color: #4f69d9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row4_col6 {\n",
       "  background-color: #82a6fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row4_col7, #T_095c0_row10_col11 {\n",
       "  background-color: #9ebeff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row5_col0, #T_095c0_row6_col10 {\n",
       "  background-color: #b9d0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row5_col1, #T_095c0_row9_col4, #T_095c0_row11_col5 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row5_col3 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row5_col6 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row5_col7 {\n",
       "  background-color: #9dbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row5_col11, #T_095c0_row8_col11 {\n",
       "  background-color: #a9c6fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row5_col13 {\n",
       "  background-color: #a5c3fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row6_col1, #T_095c0_row10_col1, #T_095c0_row10_col6, #T_095c0_row12_col3 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row6_col3 {\n",
       "  background-color: #7b9ff9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row6_col5 {\n",
       "  background-color: #5f7fe8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row6_col7, #T_095c0_row13_col6 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row6_col9 {\n",
       "  background-color: #adc9fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row6_col12 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row6_col13, #T_095c0_row10_col12, #T_095c0_row13_col0 {\n",
       "  background-color: #cbd8ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row7_col1, #T_095c0_row12_col5 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row7_col3 {\n",
       "  background-color: #3f53c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row7_col4 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row7_col5 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row8_col0 {\n",
       "  background-color: #b1cbfc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row8_col2 {\n",
       "  background-color: #e6d7cf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row8_col3 {\n",
       "  background-color: #6180e9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row8_col5 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row8_col6 {\n",
       "  background-color: #6788ee;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row8_col9, #T_095c0_row8_col12 {\n",
       "  background-color: #d5dbe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row8_col10, #T_095c0_row10_col8 {\n",
       "  background-color: #d2dbe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row9_col0 {\n",
       "  background-color: #c7d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row9_col3, #T_095c0_row11_col1 {\n",
       "  background-color: #89acfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row9_col7 {\n",
       "  background-color: #4b64d5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row9_col8, #T_095c0_row9_col12, #T_095c0_row12_col1 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row9_col10 {\n",
       "  background-color: #f7ad90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row9_col11 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row9_col13 {\n",
       "  background-color: #f5c2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row10_col2 {\n",
       "  background-color: #b5cdfa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row10_col4 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row10_col7 {\n",
       "  background-color: #4257c9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row10_col9 {\n",
       "  background-color: #f7b093;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row10_col13 {\n",
       "  background-color: #ead5c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row11_col0, #T_095c0_row13_col10 {\n",
       "  background-color: #e7d7ce;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row11_col3 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row11_col6 {\n",
       "  background-color: #779af7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row11_col8 {\n",
       "  background-color: #bbd1f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row11_col10 {\n",
       "  background-color: #b2ccfb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row11_col12 {\n",
       "  background-color: #c5d6f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row11_col13 {\n",
       "  background-color: #f6bda2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row12_col0, #T_095c0_row13_col1 {\n",
       "  background-color: #b7cff9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row12_col2 {\n",
       "  background-color: #d1dae9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row12_col6 {\n",
       "  background-color: #5470de;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row12_col7 {\n",
       "  background-color: #5b7ae5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row12_col8, #T_095c0_row12_col9 {\n",
       "  background-color: #e0dbd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row12_col10 {\n",
       "  background-color: #d8dce2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row12_col13 {\n",
       "  background-color: #f7b89c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row13_col2 {\n",
       "  background-color: #ead4c8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row13_col3, #T_095c0_row13_col4 {\n",
       "  background-color: #80a3fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row13_col5 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_095c0_row13_col8 {\n",
       "  background-color: #edd1c2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row13_col9, #T_095c0_row13_col11 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_095c0_row13_col12 {\n",
       "  background-color: #f5c4ac;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_095c0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_095c0_level0_col0\" class=\"col_heading level0 col0\" >Age</th>\n",
       "      <th id=\"T_095c0_level0_col1\" class=\"col_heading level0 col1\" >Sex</th>\n",
       "      <th id=\"T_095c0_level0_col2\" class=\"col_heading level0 col2\" >CP</th>\n",
       "      <th id=\"T_095c0_level0_col3\" class=\"col_heading level0 col3\" >Trestbps</th>\n",
       "      <th id=\"T_095c0_level0_col4\" class=\"col_heading level0 col4\" >Chol</th>\n",
       "      <th id=\"T_095c0_level0_col5\" class=\"col_heading level0 col5\" >fbs</th>\n",
       "      <th id=\"T_095c0_level0_col6\" class=\"col_heading level0 col6\" >restecg</th>\n",
       "      <th id=\"T_095c0_level0_col7\" class=\"col_heading level0 col7\" >thalach</th>\n",
       "      <th id=\"T_095c0_level0_col8\" class=\"col_heading level0 col8\" >exang</th>\n",
       "      <th id=\"T_095c0_level0_col9\" class=\"col_heading level0 col9\" >oldpeak</th>\n",
       "      <th id=\"T_095c0_level0_col10\" class=\"col_heading level0 col10\" >slope</th>\n",
       "      <th id=\"T_095c0_level0_col11\" class=\"col_heading level0 col11\" >ca</th>\n",
       "      <th id=\"T_095c0_level0_col12\" class=\"col_heading level0 col12\" >thal</th>\n",
       "      <th id=\"T_095c0_level0_col13\" class=\"col_heading level0 col13\" >target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row0\" class=\"row_heading level0 row0\" >Age</th>\n",
       "      <td id=\"T_095c0_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row0_col1\" class=\"data row0 col1\" >-0.092399</td>\n",
       "      <td id=\"T_095c0_row0_col2\" class=\"data row0 col2\" >0.110471</td>\n",
       "      <td id=\"T_095c0_row0_col3\" class=\"data row0 col3\" >0.290476</td>\n",
       "      <td id=\"T_095c0_row0_col4\" class=\"data row0 col4\" >0.202644</td>\n",
       "      <td id=\"T_095c0_row0_col5\" class=\"data row0 col5\" >0.132062</td>\n",
       "      <td id=\"T_095c0_row0_col6\" class=\"data row0 col6\" >0.149917</td>\n",
       "      <td id=\"T_095c0_row0_col7\" class=\"data row0 col7\" >-0.394563</td>\n",
       "      <td id=\"T_095c0_row0_col8\" class=\"data row0 col8\" >0.096489</td>\n",
       "      <td id=\"T_095c0_row0_col9\" class=\"data row0 col9\" >0.197123</td>\n",
       "      <td id=\"T_095c0_row0_col10\" class=\"data row0 col10\" >0.159405</td>\n",
       "      <td id=\"T_095c0_row0_col11\" class=\"data row0 col11\" >0.362210</td>\n",
       "      <td id=\"T_095c0_row0_col12\" class=\"data row0 col12\" >0.126586</td>\n",
       "      <td id=\"T_095c0_row0_col13\" class=\"data row0 col13\" >0.210373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row1\" class=\"row_heading level0 row1\" >Sex</th>\n",
       "      <td id=\"T_095c0_row1_col0\" class=\"data row1 col0\" >-0.092399</td>\n",
       "      <td id=\"T_095c0_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row1_col2\" class=\"data row1 col2\" >0.008908</td>\n",
       "      <td id=\"T_095c0_row1_col3\" class=\"data row1 col3\" >-0.066340</td>\n",
       "      <td id=\"T_095c0_row1_col4\" class=\"data row1 col4\" >-0.198089</td>\n",
       "      <td id=\"T_095c0_row1_col5\" class=\"data row1 col5\" >0.038850</td>\n",
       "      <td id=\"T_095c0_row1_col6\" class=\"data row1 col6\" >0.033897</td>\n",
       "      <td id=\"T_095c0_row1_col7\" class=\"data row1 col7\" >-0.060496</td>\n",
       "      <td id=\"T_095c0_row1_col8\" class=\"data row1 col8\" >0.143581</td>\n",
       "      <td id=\"T_095c0_row1_col9\" class=\"data row1 col9\" >0.106567</td>\n",
       "      <td id=\"T_095c0_row1_col10\" class=\"data row1 col10\" >0.033345</td>\n",
       "      <td id=\"T_095c0_row1_col11\" class=\"data row1 col11\" >0.091925</td>\n",
       "      <td id=\"T_095c0_row1_col12\" class=\"data row1 col12\" >0.383652</td>\n",
       "      <td id=\"T_095c0_row1_col13\" class=\"data row1 col13\" >0.248591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row2\" class=\"row_heading level0 row2\" >CP</th>\n",
       "      <td id=\"T_095c0_row2_col0\" class=\"data row2 col0\" >0.110471</td>\n",
       "      <td id=\"T_095c0_row2_col1\" class=\"data row2 col1\" >0.008908</td>\n",
       "      <td id=\"T_095c0_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row2_col3\" class=\"data row2 col3\" >-0.036980</td>\n",
       "      <td id=\"T_095c0_row2_col4\" class=\"data row2 col4\" >0.072088</td>\n",
       "      <td id=\"T_095c0_row2_col5\" class=\"data row2 col5\" >-0.057663</td>\n",
       "      <td id=\"T_095c0_row2_col6\" class=\"data row2 col6\" >0.063905</td>\n",
       "      <td id=\"T_095c0_row2_col7\" class=\"data row2 col7\" >-0.339308</td>\n",
       "      <td id=\"T_095c0_row2_col8\" class=\"data row2 col8\" >0.377525</td>\n",
       "      <td id=\"T_095c0_row2_col9\" class=\"data row2 col9\" >0.203244</td>\n",
       "      <td id=\"T_095c0_row2_col10\" class=\"data row2 col10\" >0.151079</td>\n",
       "      <td id=\"T_095c0_row2_col11\" class=\"data row2 col11\" >0.235644</td>\n",
       "      <td id=\"T_095c0_row2_col12\" class=\"data row2 col12\" >0.268500</td>\n",
       "      <td id=\"T_095c0_row2_col13\" class=\"data row2 col13\" >0.405773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row3\" class=\"row_heading level0 row3\" >Trestbps</th>\n",
       "      <td id=\"T_095c0_row3_col0\" class=\"data row3 col0\" >0.290476</td>\n",
       "      <td id=\"T_095c0_row3_col1\" class=\"data row3 col1\" >-0.066340</td>\n",
       "      <td id=\"T_095c0_row3_col2\" class=\"data row3 col2\" >-0.036980</td>\n",
       "      <td id=\"T_095c0_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row3_col4\" class=\"data row3 col4\" >0.131536</td>\n",
       "      <td id=\"T_095c0_row3_col5\" class=\"data row3 col5\" >0.180860</td>\n",
       "      <td id=\"T_095c0_row3_col6\" class=\"data row3 col6\" >0.149242</td>\n",
       "      <td id=\"T_095c0_row3_col7\" class=\"data row3 col7\" >-0.049108</td>\n",
       "      <td id=\"T_095c0_row3_col8\" class=\"data row3 col8\" >0.066691</td>\n",
       "      <td id=\"T_095c0_row3_col9\" class=\"data row3 col9\" >0.191243</td>\n",
       "      <td id=\"T_095c0_row3_col10\" class=\"data row3 col10\" >0.121172</td>\n",
       "      <td id=\"T_095c0_row3_col11\" class=\"data row3 col11\" >0.097954</td>\n",
       "      <td id=\"T_095c0_row3_col12\" class=\"data row3 col12\" >0.138183</td>\n",
       "      <td id=\"T_095c0_row3_col13\" class=\"data row3 col13\" >0.159274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row4\" class=\"row_heading level0 row4\" >Chol</th>\n",
       "      <td id=\"T_095c0_row4_col0\" class=\"data row4 col0\" >0.202644</td>\n",
       "      <td id=\"T_095c0_row4_col1\" class=\"data row4 col1\" >-0.198089</td>\n",
       "      <td id=\"T_095c0_row4_col2\" class=\"data row4 col2\" >0.072088</td>\n",
       "      <td id=\"T_095c0_row4_col3\" class=\"data row4 col3\" >0.131536</td>\n",
       "      <td id=\"T_095c0_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row4_col5\" class=\"data row4 col5\" >0.012708</td>\n",
       "      <td id=\"T_095c0_row4_col6\" class=\"data row4 col6\" >0.165046</td>\n",
       "      <td id=\"T_095c0_row4_col7\" class=\"data row4 col7\" >-0.000075</td>\n",
       "      <td id=\"T_095c0_row4_col8\" class=\"data row4 col8\" >0.059339</td>\n",
       "      <td id=\"T_095c0_row4_col9\" class=\"data row4 col9\" >0.038596</td>\n",
       "      <td id=\"T_095c0_row4_col10\" class=\"data row4 col10\" >-0.009215</td>\n",
       "      <td id=\"T_095c0_row4_col11\" class=\"data row4 col11\" >0.115945</td>\n",
       "      <td id=\"T_095c0_row4_col12\" class=\"data row4 col12\" >0.010859</td>\n",
       "      <td id=\"T_095c0_row4_col13\" class=\"data row4 col13\" >0.057668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row5\" class=\"row_heading level0 row5\" >fbs</th>\n",
       "      <td id=\"T_095c0_row5_col0\" class=\"data row5 col0\" >0.132062</td>\n",
       "      <td id=\"T_095c0_row5_col1\" class=\"data row5 col1\" >0.038850</td>\n",
       "      <td id=\"T_095c0_row5_col2\" class=\"data row5 col2\" >-0.057663</td>\n",
       "      <td id=\"T_095c0_row5_col3\" class=\"data row5 col3\" >0.180860</td>\n",
       "      <td id=\"T_095c0_row5_col4\" class=\"data row5 col4\" >0.012708</td>\n",
       "      <td id=\"T_095c0_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row5_col6\" class=\"data row5 col6\" >0.068831</td>\n",
       "      <td id=\"T_095c0_row5_col7\" class=\"data row5 col7\" >-0.007842</td>\n",
       "      <td id=\"T_095c0_row5_col8\" class=\"data row5 col8\" >-0.000893</td>\n",
       "      <td id=\"T_095c0_row5_col9\" class=\"data row5 col9\" >0.008311</td>\n",
       "      <td id=\"T_095c0_row5_col10\" class=\"data row5 col10\" >0.047819</td>\n",
       "      <td id=\"T_095c0_row5_col11\" class=\"data row5 col11\" >0.152086</td>\n",
       "      <td id=\"T_095c0_row5_col12\" class=\"data row5 col12\" >0.062209</td>\n",
       "      <td id=\"T_095c0_row5_col13\" class=\"data row5 col13\" >0.028376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row6\" class=\"row_heading level0 row6\" >restecg</th>\n",
       "      <td id=\"T_095c0_row6_col0\" class=\"data row6 col0\" >0.149917</td>\n",
       "      <td id=\"T_095c0_row6_col1\" class=\"data row6 col1\" >0.033897</td>\n",
       "      <td id=\"T_095c0_row6_col2\" class=\"data row6 col2\" >0.063905</td>\n",
       "      <td id=\"T_095c0_row6_col3\" class=\"data row6 col3\" >0.149242</td>\n",
       "      <td id=\"T_095c0_row6_col4\" class=\"data row6 col4\" >0.165046</td>\n",
       "      <td id=\"T_095c0_row6_col5\" class=\"data row6 col5\" >0.068831</td>\n",
       "      <td id=\"T_095c0_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row6_col7\" class=\"data row6 col7\" >-0.072290</td>\n",
       "      <td id=\"T_095c0_row6_col8\" class=\"data row6 col8\" >0.081874</td>\n",
       "      <td id=\"T_095c0_row6_col9\" class=\"data row6 col9\" >0.113726</td>\n",
       "      <td id=\"T_095c0_row6_col10\" class=\"data row6 col10\" >0.135141</td>\n",
       "      <td id=\"T_095c0_row6_col11\" class=\"data row6 col11\" >0.129021</td>\n",
       "      <td id=\"T_095c0_row6_col12\" class=\"data row6 col12\" >0.018795</td>\n",
       "      <td id=\"T_095c0_row6_col13\" class=\"data row6 col13\" >0.193404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row7\" class=\"row_heading level0 row7\" >thalach</th>\n",
       "      <td id=\"T_095c0_row7_col0\" class=\"data row7 col0\" >-0.394563</td>\n",
       "      <td id=\"T_095c0_row7_col1\" class=\"data row7 col1\" >-0.060496</td>\n",
       "      <td id=\"T_095c0_row7_col2\" class=\"data row7 col2\" >-0.339308</td>\n",
       "      <td id=\"T_095c0_row7_col3\" class=\"data row7 col3\" >-0.049108</td>\n",
       "      <td id=\"T_095c0_row7_col4\" class=\"data row7 col4\" >-0.000075</td>\n",
       "      <td id=\"T_095c0_row7_col5\" class=\"data row7 col5\" >-0.007842</td>\n",
       "      <td id=\"T_095c0_row7_col6\" class=\"data row7 col6\" >-0.072290</td>\n",
       "      <td id=\"T_095c0_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row7_col8\" class=\"data row7 col8\" >-0.384368</td>\n",
       "      <td id=\"T_095c0_row7_col9\" class=\"data row7 col9\" >-0.347640</td>\n",
       "      <td id=\"T_095c0_row7_col10\" class=\"data row7 col10\" >-0.389307</td>\n",
       "      <td id=\"T_095c0_row7_col11\" class=\"data row7 col11\" >-0.268727</td>\n",
       "      <td id=\"T_095c0_row7_col12\" class=\"data row7 col12\" >-0.274831</td>\n",
       "      <td id=\"T_095c0_row7_col13\" class=\"data row7 col13\" >-0.426599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row8\" class=\"row_heading level0 row8\" >exang</th>\n",
       "      <td id=\"T_095c0_row8_col0\" class=\"data row8 col0\" >0.096489</td>\n",
       "      <td id=\"T_095c0_row8_col1\" class=\"data row8 col1\" >0.143581</td>\n",
       "      <td id=\"T_095c0_row8_col2\" class=\"data row8 col2\" >0.377525</td>\n",
       "      <td id=\"T_095c0_row8_col3\" class=\"data row8 col3\" >0.066691</td>\n",
       "      <td id=\"T_095c0_row8_col4\" class=\"data row8 col4\" >0.059339</td>\n",
       "      <td id=\"T_095c0_row8_col5\" class=\"data row8 col5\" >-0.000893</td>\n",
       "      <td id=\"T_095c0_row8_col6\" class=\"data row8 col6\" >0.081874</td>\n",
       "      <td id=\"T_095c0_row8_col7\" class=\"data row8 col7\" >-0.384368</td>\n",
       "      <td id=\"T_095c0_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row8_col9\" class=\"data row8 col9\" >0.289310</td>\n",
       "      <td id=\"T_095c0_row8_col10\" class=\"data row8 col10\" >0.250572</td>\n",
       "      <td id=\"T_095c0_row8_col11\" class=\"data row8 col11\" >0.148232</td>\n",
       "      <td id=\"T_095c0_row8_col12\" class=\"data row8 col12\" >0.326927</td>\n",
       "      <td id=\"T_095c0_row8_col13\" class=\"data row8 col13\" >0.409193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row9\" class=\"row_heading level0 row9\" >oldpeak</th>\n",
       "      <td id=\"T_095c0_row9_col0\" class=\"data row9 col0\" >0.197123</td>\n",
       "      <td id=\"T_095c0_row9_col1\" class=\"data row9 col1\" >0.106567</td>\n",
       "      <td id=\"T_095c0_row9_col2\" class=\"data row9 col2\" >0.203244</td>\n",
       "      <td id=\"T_095c0_row9_col3\" class=\"data row9 col3\" >0.191243</td>\n",
       "      <td id=\"T_095c0_row9_col4\" class=\"data row9 col4\" >0.038596</td>\n",
       "      <td id=\"T_095c0_row9_col5\" class=\"data row9 col5\" >0.008311</td>\n",
       "      <td id=\"T_095c0_row9_col6\" class=\"data row9 col6\" >0.113726</td>\n",
       "      <td id=\"T_095c0_row9_col7\" class=\"data row9 col7\" >-0.347640</td>\n",
       "      <td id=\"T_095c0_row9_col8\" class=\"data row9 col8\" >0.289310</td>\n",
       "      <td id=\"T_095c0_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row9_col10\" class=\"data row9 col10\" >0.579037</td>\n",
       "      <td id=\"T_095c0_row9_col11\" class=\"data row9 col11\" >0.294452</td>\n",
       "      <td id=\"T_095c0_row9_col12\" class=\"data row9 col12\" >0.344976</td>\n",
       "      <td id=\"T_095c0_row9_col13\" class=\"data row9 col13\" >0.471619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row10\" class=\"row_heading level0 row10\" >slope</th>\n",
       "      <td id=\"T_095c0_row10_col0\" class=\"data row10 col0\" >0.159405</td>\n",
       "      <td id=\"T_095c0_row10_col1\" class=\"data row10 col1\" >0.033345</td>\n",
       "      <td id=\"T_095c0_row10_col2\" class=\"data row10 col2\" >0.151079</td>\n",
       "      <td id=\"T_095c0_row10_col3\" class=\"data row10 col3\" >0.121172</td>\n",
       "      <td id=\"T_095c0_row10_col4\" class=\"data row10 col4\" >-0.009215</td>\n",
       "      <td id=\"T_095c0_row10_col5\" class=\"data row10 col5\" >0.047819</td>\n",
       "      <td id=\"T_095c0_row10_col6\" class=\"data row10 col6\" >0.135141</td>\n",
       "      <td id=\"T_095c0_row10_col7\" class=\"data row10 col7\" >-0.389307</td>\n",
       "      <td id=\"T_095c0_row10_col8\" class=\"data row10 col8\" >0.250572</td>\n",
       "      <td id=\"T_095c0_row10_col9\" class=\"data row10 col9\" >0.579037</td>\n",
       "      <td id=\"T_095c0_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row10_col11\" class=\"data row10 col11\" >0.109761</td>\n",
       "      <td id=\"T_095c0_row10_col12\" class=\"data row10 col12\" >0.279688</td>\n",
       "      <td id=\"T_095c0_row10_col13\" class=\"data row10 col13\" >0.361500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row11\" class=\"row_heading level0 row11\" >ca</th>\n",
       "      <td id=\"T_095c0_row11_col0\" class=\"data row11 col0\" >0.362210</td>\n",
       "      <td id=\"T_095c0_row11_col1\" class=\"data row11 col1\" >0.091925</td>\n",
       "      <td id=\"T_095c0_row11_col2\" class=\"data row11 col2\" >0.235644</td>\n",
       "      <td id=\"T_095c0_row11_col3\" class=\"data row11 col3\" >0.097954</td>\n",
       "      <td id=\"T_095c0_row11_col4\" class=\"data row11 col4\" >0.115945</td>\n",
       "      <td id=\"T_095c0_row11_col5\" class=\"data row11 col5\" >0.152086</td>\n",
       "      <td id=\"T_095c0_row11_col6\" class=\"data row11 col6\" >0.129021</td>\n",
       "      <td id=\"T_095c0_row11_col7\" class=\"data row11 col7\" >-0.268727</td>\n",
       "      <td id=\"T_095c0_row11_col8\" class=\"data row11 col8\" >0.148232</td>\n",
       "      <td id=\"T_095c0_row11_col9\" class=\"data row11 col9\" >0.294452</td>\n",
       "      <td id=\"T_095c0_row11_col10\" class=\"data row11 col10\" >0.109761</td>\n",
       "      <td id=\"T_095c0_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row11_col12\" class=\"data row11 col12\" >0.256382</td>\n",
       "      <td id=\"T_095c0_row11_col13\" class=\"data row11 col13\" >0.503863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row12\" class=\"row_heading level0 row12\" >thal</th>\n",
       "      <td id=\"T_095c0_row12_col0\" class=\"data row12 col0\" >0.126586</td>\n",
       "      <td id=\"T_095c0_row12_col1\" class=\"data row12 col1\" >0.383652</td>\n",
       "      <td id=\"T_095c0_row12_col2\" class=\"data row12 col2\" >0.268500</td>\n",
       "      <td id=\"T_095c0_row12_col3\" class=\"data row12 col3\" >0.138183</td>\n",
       "      <td id=\"T_095c0_row12_col4\" class=\"data row12 col4\" >0.010859</td>\n",
       "      <td id=\"T_095c0_row12_col5\" class=\"data row12 col5\" >0.062209</td>\n",
       "      <td id=\"T_095c0_row12_col6\" class=\"data row12 col6\" >0.018795</td>\n",
       "      <td id=\"T_095c0_row12_col7\" class=\"data row12 col7\" >-0.274831</td>\n",
       "      <td id=\"T_095c0_row12_col8\" class=\"data row12 col8\" >0.326927</td>\n",
       "      <td id=\"T_095c0_row12_col9\" class=\"data row12 col9\" >0.344976</td>\n",
       "      <td id=\"T_095c0_row12_col10\" class=\"data row12 col10\" >0.279688</td>\n",
       "      <td id=\"T_095c0_row12_col11\" class=\"data row12 col11\" >0.256382</td>\n",
       "      <td id=\"T_095c0_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "      <td id=\"T_095c0_row12_col13\" class=\"data row12 col13\" >0.523566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_095c0_level0_row13\" class=\"row_heading level0 row13\" >target</th>\n",
       "      <td id=\"T_095c0_row13_col0\" class=\"data row13 col0\" >0.210373</td>\n",
       "      <td id=\"T_095c0_row13_col1\" class=\"data row13 col1\" >0.248591</td>\n",
       "      <td id=\"T_095c0_row13_col2\" class=\"data row13 col2\" >0.405773</td>\n",
       "      <td id=\"T_095c0_row13_col3\" class=\"data row13 col3\" >0.159274</td>\n",
       "      <td id=\"T_095c0_row13_col4\" class=\"data row13 col4\" >0.057668</td>\n",
       "      <td id=\"T_095c0_row13_col5\" class=\"data row13 col5\" >0.028376</td>\n",
       "      <td id=\"T_095c0_row13_col6\" class=\"data row13 col6\" >0.193404</td>\n",
       "      <td id=\"T_095c0_row13_col7\" class=\"data row13 col7\" >-0.426599</td>\n",
       "      <td id=\"T_095c0_row13_col8\" class=\"data row13 col8\" >0.409193</td>\n",
       "      <td id=\"T_095c0_row13_col9\" class=\"data row13 col9\" >0.471619</td>\n",
       "      <td id=\"T_095c0_row13_col10\" class=\"data row13 col10\" >0.361500</td>\n",
       "      <td id=\"T_095c0_row13_col11\" class=\"data row13 col11\" >0.503863</td>\n",
       "      <td id=\"T_095c0_row13_col12\" class=\"data row13 col12\" >0.523566</td>\n",
       "      <td id=\"T_095c0_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1d7d00ed4b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation Matrix\n",
    "#plot correlation matrix.\n",
    "corr = heart_df.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1cead8a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jude\\AppData\\Local\\Temp\\ipykernel_23080\\2184756728.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  min_df['target'] = heart_df['target']\n"
     ]
    },
    {
     "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>CP</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CP  thalach  exang  oldpeak  ca  thal  target\n",
       "0   1      150      0      2.3   0     6       0\n",
       "1   4      108      1      1.5   3     3       1\n",
       "2   3      187      0      3.5   0     3       0\n",
       "3   2      172      0      1.4   0     3       0\n",
       "4   2      178      0      0.8   0     3       0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select data\n",
    "# prepare dataset again\n",
    "\n",
    "min_df = heart_df[['CP', 'thalach', 'exang', 'oldpeak', 'ca','thal']]\n",
    "min_df['target'] = heart_df['target']\n",
    "min_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c47e5e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    160\n",
       "1     89\n",
       "2     48\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change to Binary Classification\n",
    "min_df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "65d0f8df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jude\\AppData\\Local\\Temp\\ipykernel_23080\\746968394.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  min_df['target'] = min_df['target'].apply(str)\n",
      "C:\\Users\\Jude\\AppData\\Local\\Temp\\ipykernel_23080\\746968394.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  min_df['target'] = min_df['target'].map(col_map)\n"
     ]
    }
   ],
   "source": [
    "#convert target to str\n",
    "min_df['target'] = min_df['target'].apply(str)\n",
    "\n",
    "col_map = {'0':0, '1':1, '2':1}\n",
    "min_df['target'] = min_df['target'].map(col_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c87345db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jude\\AppData\\Local\\Temp\\ipykernel_23080\\526615400.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  min_df['target'] = min_df['target'].apply(int)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CP           int64\n",
       "thalach      int64\n",
       "exang        int64\n",
       "oldpeak    float64\n",
       "ca           int64\n",
       "thal         int64\n",
       "target       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now convert the data type back to int\n",
    "min_df['target'] = min_df['target'].apply(int)\n",
    "min_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f27c965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    160\n",
       "1    137\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "029d9020",
   "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>CP</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CP  thalach  exang  oldpeak  ca  thal  target\n",
       "0   1      150      0      2.3   0     6       0\n",
       "1   4      108      1      1.5   3     3       1\n",
       "2   3      187      0      3.5   0     3       0\n",
       "3   2      172      0      1.4   0     3       0\n",
       "4   2      178      0      0.8   0     3       0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "252fb1b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 297 entries, 0 to 296\n",
      "Data columns (total 7 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CP       297 non-null    int64  \n",
      " 1   thalach  297 non-null    int64  \n",
      " 2   exang    297 non-null    int64  \n",
      " 3   oldpeak  297 non-null    float64\n",
      " 4   ca       297 non-null    int64  \n",
      " 5   thal     297 non-null    int64  \n",
      " 6   target   297 non-null    int64  \n",
      "dtypes: float64(1), int64(6)\n",
      "memory usage: 16.4 KB\n"
     ]
    }
   ],
   "source": [
    "min_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ffc7589",
   "metadata": {},
   "source": [
    "# Model Preparation (using sklearn's test_split_train) with Unscaled Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7adc5a80",
   "metadata": {},
   "source": [
    "#### Needed Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0676025",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "\n",
    "#SkLearn ML General\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, recall_score, roc_curve, auc, precision_score\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import f1_score, classification_report\n",
    "\n",
    "#SkLearn ML Algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import svm\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfdc2a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Input and Output Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7bb5353d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#separate inputs and outputs\n",
    "\n",
    "x = min_df.drop(['target'], axis = 1)\n",
    "y = min_df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e2f31ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the dataset into training and test sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=13)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "61a53a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 60 points:\n",
      "classifier_NB: 5\n",
      "Classifier_LR: 7\n",
      "classifier_SVM: 16\n",
      "classifier_KNN: 11\n",
      "classifier_DT: 12\n",
      "classifier_RF: 8\n",
      "Classifier_ANN: 5\n"
     ]
    }
   ],
   "source": [
    "# Define the classifiers\n",
    "classifiers = {\n",
    "    \"classifier_NB\":    GaussianNB(),\n",
    "    \"Classifier_LR\":    LogisticRegression(max_iter=300),\n",
    "    \"classifier_SVM\":   SVC(gamma = 2, C = 1),\n",
    "    \"classifier_KNN\":   KNeighborsClassifier(n_neighbors = 3),\n",
    "    \"classifier_DT\":    DecisionTreeClassifier(max_depth=5),\n",
    "    \"classifier_RF\":    RandomForestClassifier(max_depth = 5, n_estimators =10, max_features=1),\n",
    "    \"Classifier_ANN\":   MLPClassifier(alpha = 0.01, max_iter=1000, hidden_layer_sizes=[3], activation = 'tanh')\n",
    "}\n",
    "results = {}\n",
    "print(\"Number of mislabeled points out of a total %d points:\" % x_test.shape[0])\n",
    "# Train the classifiers on the training set and test classifiers on unseen test data\n",
    "for clf_name, clf_model in classifiers.items():\n",
    "    clf_model.fit(x_train, y_train)\n",
    "    clf_model.predict(x_test)\n",
    "    print(clf_name + \": %d\" % (y_test != clf_model.predict(x_test)).sum())\n",
    "    results[clf_name] = clf_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c1d5915d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance Scores:\n",
      "classifier_NB:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.91      0.93        35\n",
      "           1       0.88      0.92      0.90        25\n",
      "\n",
      "    accuracy                           0.92        60\n",
      "   macro avg       0.91      0.92      0.91        60\n",
      "weighted avg       0.92      0.92      0.92        60\n",
      "\n",
      "Classifier_LR:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.86      0.90        35\n",
      "           1       0.82      0.92      0.87        25\n",
      "\n",
      "    accuracy                           0.88        60\n",
      "   macro avg       0.88      0.89      0.88        60\n",
      "weighted avg       0.89      0.88      0.88        60\n",
      "\n",
      "classifier_SVM:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.60      0.72        35\n",
      "           1       0.62      0.92      0.74        25\n",
      "\n",
      "    accuracy                           0.73        60\n",
      "   macro avg       0.77      0.76      0.73        60\n",
      "weighted avg       0.79      0.73      0.73        60\n",
      "\n",
      "classifier_KNN:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.83      0.84        35\n",
      "           1       0.77      0.80      0.78        25\n",
      "\n",
      "    accuracy                           0.82        60\n",
      "   macro avg       0.81      0.81      0.81        60\n",
      "weighted avg       0.82      0.82      0.82        60\n",
      "\n",
      "classifier_DT:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.86      0.83        35\n",
      "           1       0.78      0.72      0.75        25\n",
      "\n",
      "    accuracy                           0.80        60\n",
      "   macro avg       0.80      0.79      0.79        60\n",
      "weighted avg       0.80      0.80      0.80        60\n",
      "\n",
      "classifier_RF:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.86      0.88        35\n",
      "           1       0.81      0.88      0.85        25\n",
      "\n",
      "    accuracy                           0.87        60\n",
      "   macro avg       0.86      0.87      0.86        60\n",
      "weighted avg       0.87      0.87      0.87        60\n",
      "\n",
      "Classifier_ANN:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.91      0.93        35\n",
      "           1       0.88      0.92      0.90        25\n",
      "\n",
      "    accuracy                           0.92        60\n",
      "   macro avg       0.91      0.92      0.91        60\n",
      "weighted avg       0.92      0.92      0.92        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Performance Scores:\")\n",
    "for clf_name, clf_model in results.items():\n",
    "    \n",
    "    print(clf_name + \":\", classification_report(y_test, results[clf_name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "945cff5b",
   "metadata": {},
   "source": [
    "#### Observation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d30356",
   "metadata": {},
   "source": [
    "We can see vary good performance and good scores on this for precision, recall and F1. The besr performance is obtained using either the Gaussian Classifier or the Neural Classifier. It should be noted that the data used here was not scaled in any way.\n",
    "The next section will focus on using scaled data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b763069e",
   "metadata": {},
   "source": [
    "# Working with Scaled Data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e992f3ac",
   "metadata": {},
   "source": [
    "#### Define function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "385e3b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define function that scales the data using min-max\n",
    "def scale_dataset(dataframe, oversample=False):\n",
    "    x = dataframe[dataframe.columns[:-1]].values\n",
    "    y = dataframe[dataframe.columns[-1]].values\n",
    "    \n",
    "    scaler = MinMaxScaler()\n",
    "    x = scaler.fit_transform(x)\n",
    "    \n",
    "    #Equalize the normal of the classes in the dataset\n",
    "    if oversample:\n",
    "        ros = RandomOverSampler()\n",
    "        x,y = ros.fit_resample(x,y)\n",
    "    \n",
    "    data = np.hstack((x,np.reshape(y,(-1,1))))\n",
    "                     \n",
    "    return data, x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "877eac00",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_min = scale_dataset(min_df, oversample=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7648f1b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set training, and test set\n",
    "#split the dataset into training and test sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "625c48cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of No disease class: 237\n",
      "Length of Diseased class: 60\n"
     ]
    }
   ],
   "source": [
    "# check the length of the data\n",
    "print('Length of No disease class:',len(x_train))\n",
    "print('Length of Diseased class:',len(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0759a33",
   "metadata": {},
   "source": [
    "The data is almost balanced. So i will not match the classses."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d716c9fc",
   "metadata": {},
   "source": [
    "# Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fcc3d48a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 60 points:\n",
      "classifier_NB: 5\n",
      "Classifier_LR: 7\n",
      "classifier_SVM: 16\n",
      "classifier_KNN: 11\n",
      "classifier_DT: 13\n",
      "classifier_RF: 7\n",
      "Classifier_ANN: 4\n"
     ]
    }
   ],
   "source": [
    "# Define the classifiers\n",
    "classifiers = {\n",
    "    \"classifier_NB\":    GaussianNB(),\n",
    "    \"Classifier_LR\":    LogisticRegression(max_iter=300),\n",
    "    \"classifier_SVM\":   SVC(gamma = 2, C = 1),\n",
    "    \"classifier_KNN\":   KNeighborsClassifier(n_neighbors = 3),\n",
    "    \"classifier_DT\":    DecisionTreeClassifier(max_depth=5),\n",
    "    \"classifier_RF\":    RandomForestClassifier(max_depth = 5, n_estimators =10, max_features=1),\n",
    "    \"Classifier_ANN\":   MLPClassifier(alpha = 0.01, max_iter=1000, hidden_layer_sizes=[3], activation = 'tanh')\n",
    "}\n",
    "results = {}\n",
    "print(\"Number of mislabeled points out of a total %d points:\" % x_test.shape[0])\n",
    "# Train the classifiers on the training set and test classifiers on unseen test data\n",
    "for clf_name, clf_model in classifiers.items():\n",
    "    clf_model.fit(x_train, y_train)\n",
    "    clf_model.predict(x_test)\n",
    "    print(clf_name + \": %d\" % (y_test != clf_model.predict(x_test)).sum())\n",
    "    results[clf_name] = clf_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dbe4749f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance Scores:\n",
      "classifier_NB:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.91      0.93        35\n",
      "           1       0.88      0.92      0.90        25\n",
      "\n",
      "    accuracy                           0.92        60\n",
      "   macro avg       0.91      0.92      0.91        60\n",
      "weighted avg       0.92      0.92      0.92        60\n",
      "\n",
      "Classifier_LR:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.86      0.90        35\n",
      "           1       0.82      0.92      0.87        25\n",
      "\n",
      "    accuracy                           0.88        60\n",
      "   macro avg       0.88      0.89      0.88        60\n",
      "weighted avg       0.89      0.88      0.88        60\n",
      "\n",
      "classifier_SVM:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.60      0.72        35\n",
      "           1       0.62      0.92      0.74        25\n",
      "\n",
      "    accuracy                           0.73        60\n",
      "   macro avg       0.77      0.76      0.73        60\n",
      "weighted avg       0.79      0.73      0.73        60\n",
      "\n",
      "classifier_KNN:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.83      0.84        35\n",
      "           1       0.77      0.80      0.78        25\n",
      "\n",
      "    accuracy                           0.82        60\n",
      "   macro avg       0.81      0.81      0.81        60\n",
      "weighted avg       0.82      0.82      0.82        60\n",
      "\n",
      "classifier_DT:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.86      0.82        35\n",
      "           1       0.77      0.68      0.72        25\n",
      "\n",
      "    accuracy                           0.78        60\n",
      "   macro avg       0.78      0.77      0.77        60\n",
      "weighted avg       0.78      0.78      0.78        60\n",
      "\n",
      "classifier_RF:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.91      0.90        35\n",
      "           1       0.88      0.84      0.86        25\n",
      "\n",
      "    accuracy                           0.88        60\n",
      "   macro avg       0.88      0.88      0.88        60\n",
      "weighted avg       0.88      0.88      0.88        60\n",
      "\n",
      "Classifier_ANN:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.94      0.94        35\n",
      "           1       0.92      0.92      0.92        25\n",
      "\n",
      "    accuracy                           0.93        60\n",
      "   macro avg       0.93      0.93      0.93        60\n",
      "weighted avg       0.93      0.93      0.93        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Performance Scores:\")\n",
    "for clf_name, clf_model in results.items():\n",
    "    \n",
    "    print(clf_name + \":\", classification_report(y_test, results[clf_name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bda888",
   "metadata": {},
   "source": [
    "#### Observation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8fd06a",
   "metadata": {},
   "source": [
    "We can see a little bit improvement in the performance on the test data. The neural network has the best classification with 93% and really good score for precision, recall and f1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad004d6",
   "metadata": {},
   "source": [
    "# Further Analysis of the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b45094",
   "metadata": {},
   "source": [
    "#### Confusion Matrix Plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "754f7377",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrices:\n",
      "classifier_NB:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier_LR:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classifier_SVM:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classifier_KNN:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classifier_DT:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classifier_RF:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier_ANN:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "print(\"Confusion Matrices:\")\n",
    "for clf_name, clf_model in results.items():\n",
    "    print(clf_name + \":\")\n",
    "    cm = confusion_matrix(y_test, results[clf_name])\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "    disp.plot()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb3d7d8b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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