Day_5_Answers.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "continuing-sperm",
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   "source": [
    "# Machine Learning Basics with Scikit-learn: Day 5\n",
    "In our last session, we will build a model to predict the chronic kidney disease. The data was taken over a 2-month period in India with 25 features ( eg, red blood cell count, white blood cell count, etc). The target is the 'classification', which is either 'ckd' or 'notckd' - ckd=chronic kidney disease. We will use machine learning techniques to predict if a patient is suffering from a chronic kidney disease or not. This dataset has misssing data, features in different scales, and 25 features. For more information click [here](https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease). \n",
    "\n",
    "We will perform the following actions:\n",
    "1. Import the dataset\n",
    "2. Analyze the dataset\n",
    "3. Imput missing values\n",
    "4. Scale the data\n",
    "5. Create training and testing datasets\n",
    "6. Train a neural network with the training dataset\n",
    "7. Evaluate the model with the testing dataset\n",
    "\n",
    "Let's start loading the functions and libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "czech-headline",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lesser-temple",
   "metadata": {},
   "source": [
    "# 1. Import the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "honey-burner",
   "metadata": {},
   "source": [
    "We import the dataset. This time, we will use a CSV file which contains the required data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "comparative-sender",
   "metadata": {},
   "outputs": [
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       "      <td>117.0</td>\n",
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       "      <td>ckd</td>\n",
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       "      <td>106.0</td>\n",
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       "      <td>114.0</td>\n",
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       "      <td>no</td>\n",
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       "      <th>399</th>\n",
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       "      <td>80.0</td>\n",
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       "      <td>normal</td>\n",
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       "      <td>notpresent</td>\n",
       "      <td>131.0</td>\n",
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       "      <td>53</td>\n",
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       "      <td>6.1</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>notckd</td>\n",
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      "text/plain": [
       "      age    bp     sg   al   su     rbc        pc         pcc          ba  \\\n",
       "id                                                                           \n",
       "0    48.0  80.0  1.020  1.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "1     7.0  50.0  1.020  4.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "2    62.0  80.0  1.010  2.0  3.0  normal    normal  notpresent  notpresent   \n",
       "3    48.0  70.0  1.005  4.0  0.0  normal  abnormal     present  notpresent   \n",
       "4    51.0  80.0  1.010  2.0  0.0  normal    normal  notpresent  notpresent   \n",
       "..    ...   ...    ...  ...  ...     ...       ...         ...         ...   \n",
       "395  55.0  80.0  1.020  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "396  42.0  70.0  1.025  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "397  12.0  80.0  1.020  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "398  17.0  60.0  1.025  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "399  58.0  80.0  1.025  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "\n",
       "       bgr  ...  pcv    wc   rc  htn   dm cad appet   pe  ane classification  \n",
       "id          ...                                                               \n",
       "0    121.0  ...   44  7800  5.2  yes  yes  no  good   no   no            ckd  \n",
       "1      NaN  ...   38  6000  NaN   no   no  no  good   no   no            ckd  \n",
       "2    423.0  ...   31  7500  NaN   no  yes  no  poor   no  yes            ckd  \n",
       "3    117.0  ...   32  6700  3.9  yes   no  no  poor  yes  yes            ckd  \n",
       "4    106.0  ...   35  7300  4.6   no   no  no  good   no   no            ckd  \n",
       "..     ...  ...  ...   ...  ...  ...  ...  ..   ...  ...  ...            ...  \n",
       "395  140.0  ...   47  6700  4.9   no   no  no  good   no   no         notckd  \n",
       "396   75.0  ...   54  7800  6.2   no   no  no  good   no   no         notckd  \n",
       "397  100.0  ...   49  6600  5.4   no   no  no  good   no   no         notckd  \n",
       "398  114.0  ...   51  7200  5.9   no   no  no  good   no   no         notckd  \n",
       "399  131.0  ...   53  6800  6.1   no   no  no  good   no   no         notckd  \n",
       "\n",
       "[400 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kidney_df = pd.read_csv(\"https://raw.githubusercontent.com/dgzara/nuit_machine_learning_workshop/main/kidney_disease.csv\", index_col=0)\n",
    "kidney_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flying-popularity",
   "metadata": {},
   "source": [
    "The target column was coded using strings (`notckd` and `ckd`). We can replace them with binary values (0 and 1).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "trained-occasions",
   "metadata": {},
   "outputs": [],
   "source": [
    "kidney_df['classification'] = kidney_df['classification'].str.replace('notckd', '0')\n",
    "kidney_df['classification'] = kidney_df['classification'].str.replace('ckd', '1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "varying-spending",
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</td>\n",
       "      <td>4.6</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>55.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>140.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>6700</td>\n",
       "      <td>4.9</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>42.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>75.0</td>\n",
       "      <td>...</td>\n",
       "      <td>54</td>\n",
       "      <td>7800</td>\n",
       "      <td>6.2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>6600</td>\n",
       "      <td>5.4</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>114.0</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>7200</td>\n",
       "      <td>5.9</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>58.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>131.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>6800</td>\n",
       "      <td>6.1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age    bp     sg   al   su     rbc        pc         pcc          ba  \\\n",
       "id                                                                           \n",
       "0    48.0  80.0  1.020  1.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "1     7.0  50.0  1.020  4.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "2    62.0  80.0  1.010  2.0  3.0  normal    normal  notpresent  notpresent   \n",
       "3    48.0  70.0  1.005  4.0  0.0  normal  abnormal     present  notpresent   \n",
       "4    51.0  80.0  1.010  2.0  0.0  normal    normal  notpresent  notpresent   \n",
       "..    ...   ...    ...  ...  ...     ...       ...         ...         ...   \n",
       "395  55.0  80.0  1.020  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "396  42.0  70.0  1.025  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "397  12.0  80.0  1.020  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "398  17.0  60.0  1.025  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "399  58.0  80.0  1.025  0.0  0.0  normal    normal  notpresent  notpresent   \n",
       "\n",
       "       bgr  ...  pcv    wc   rc  htn   dm cad appet   pe  ane classification  \n",
       "id          ...                                                               \n",
       "0    121.0  ...   44  7800  5.2  yes  yes  no  good   no   no              1  \n",
       "1      NaN  ...   38  6000  NaN   no   no  no  good   no   no              1  \n",
       "2    423.0  ...   31  7500  NaN   no  yes  no  poor   no  yes              1  \n",
       "3    117.0  ...   32  6700  3.9  yes   no  no  poor  yes  yes              1  \n",
       "4    106.0  ...   35  7300  4.6   no   no  no  good   no   no              1  \n",
       "..     ...  ...  ...   ...  ...  ...  ...  ..   ...  ...  ...            ...  \n",
       "395  140.0  ...   47  6700  4.9   no   no  no  good   no   no              0  \n",
       "396   75.0  ...   54  7800  6.2   no   no  no  good   no   no              0  \n",
       "397  100.0  ...   49  6600  5.4   no   no  no  good   no   no              0  \n",
       "398  114.0  ...   51  7200  5.9   no   no  no  good   no   no              0  \n",
       "399  131.0  ...   53  6800  6.1   no   no  no  good   no   no              0  \n",
       "\n",
       "[400 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kidney_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "welsh-medicine",
   "metadata": {},
   "source": [
    "We split the dataset into `X` and `y` variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "transparent-interaction",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = kidney_df.drop(['classification'], axis=1)\n",
    "y = kidney_df['classification'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fifty-morning",
   "metadata": {},
   "source": [
    "## 2. Analyze the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gorgeous-sociology",
   "metadata": {},
   "source": [
    "According to the dataset's documentation, the features available are:\n",
    "1. age - age\n",
    "2. bp - blood pressure\n",
    "3. sg - specific gravity\n",
    "4. al - albumin\n",
    "5. su - sugar\n",
    "6. rbc - red blood cells\n",
    "7. pc - pus cell\n",
    "8. pcc - pus cell clumps\n",
    "9. ba - bacteria\n",
    "10. bgr - blood glucose random\n",
    "11. bu - blood urea\n",
    "12. sc - serum creatinine\n",
    "13. sod - sodium\n",
    "14. pot - potassium\n",
    "15. hemo - hemoglobin\n",
    "16. pcv - packed cell volume\n",
    "17. wc - white blood cell count\n",
    "18. rc - red blood cell count\n",
    "19. htn - hypertension\n",
    "20. dm - diabetes mellitus\n",
    "21. cad - coronary artery disease\n",
    "22. appet - appetite\n",
    "23. pe - pedal edema\n",
    "24. ane - anemia\n",
    "\n",
    "We check the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "expired-evolution",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc', 'ba', 'bgr', 'bu',\n",
       "       'sc', 'sod', 'pot', 'hemo', 'pcv', 'wc', 'rc', 'htn', 'dm', 'cad',\n",
       "       'appet', 'pe', 'ane'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "overall-radius",
   "metadata": {},
   "source": [
    "Are the columns numeric or categorical?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "excellent-boring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age      float64\n",
       "bp       float64\n",
       "sg       float64\n",
       "al       float64\n",
       "su       float64\n",
       "rbc       object\n",
       "pc        object\n",
       "pcc       object\n",
       "ba        object\n",
       "bgr      float64\n",
       "bu       float64\n",
       "sc       float64\n",
       "sod      float64\n",
       "pot      float64\n",
       "hemo     float64\n",
       "pcv       object\n",
       "wc        object\n",
       "rc        object\n",
       "htn       object\n",
       "dm        object\n",
       "cad       object\n",
       "appet     object\n",
       "pe        object\n",
       "ane       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "sorted-stroke",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        9\n",
       "bp        12\n",
       "sg        47\n",
       "al        46\n",
       "su        49\n",
       "rbc      152\n",
       "pc        65\n",
       "pcc        4\n",
       "ba         4\n",
       "bgr       44\n",
       "bu        19\n",
       "sc        17\n",
       "sod       87\n",
       "pot       88\n",
       "hemo      52\n",
       "pcv       70\n",
       "wc       105\n",
       "rc       130\n",
       "htn        2\n",
       "dm         2\n",
       "cad        2\n",
       "appet      1\n",
       "pe         1\n",
       "ane        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How much data is missing? \n",
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "excited-spelling",
   "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>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>391.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.483376</td>\n",
       "      <td>76.469072</td>\n",
       "      <td>1.017408</td>\n",
       "      <td>1.016949</td>\n",
       "      <td>0.450142</td>\n",
       "      <td>148.036517</td>\n",
       "      <td>57.425722</td>\n",
       "      <td>3.072454</td>\n",
       "      <td>137.528754</td>\n",
       "      <td>4.627244</td>\n",
       "      <td>12.526437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.169714</td>\n",
       "      <td>13.683637</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>1.352679</td>\n",
       "      <td>1.099191</td>\n",
       "      <td>79.281714</td>\n",
       "      <td>50.503006</td>\n",
       "      <td>5.741126</td>\n",
       "      <td>10.408752</td>\n",
       "      <td>3.193904</td>\n",
       "      <td>2.912587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>10.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>12.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.025000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age          bp          sg          al          su         bgr  \\\n",
       "count  391.000000  388.000000  353.000000  354.000000  351.000000  356.000000   \n",
       "mean    51.483376   76.469072    1.017408    1.016949    0.450142  148.036517   \n",
       "std     17.169714   13.683637    0.005717    1.352679    1.099191   79.281714   \n",
       "min      2.000000   50.000000    1.005000    0.000000    0.000000   22.000000   \n",
       "25%     42.000000   70.000000    1.010000    0.000000    0.000000   99.000000   \n",
       "50%     55.000000   80.000000    1.020000    0.000000    0.000000  121.000000   \n",
       "75%     64.500000   80.000000    1.020000    2.000000    0.000000  163.000000   \n",
       "max     90.000000  180.000000    1.025000    5.000000    5.000000  490.000000   \n",
       "\n",
       "               bu          sc         sod         pot        hemo  \n",
       "count  381.000000  383.000000  313.000000  312.000000  348.000000  \n",
       "mean    57.425722    3.072454  137.528754    4.627244   12.526437  \n",
       "std     50.503006    5.741126   10.408752    3.193904    2.912587  \n",
       "min      1.500000    0.400000    4.500000    2.500000    3.100000  \n",
       "25%     27.000000    0.900000  135.000000    3.800000   10.300000  \n",
       "50%     42.000000    1.300000  138.000000    4.400000   12.650000  \n",
       "75%     66.000000    2.800000  142.000000    4.900000   15.000000  \n",
       "max    391.000000   76.000000  163.000000   47.000000   17.800000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the main descriptive statistics of this dataset?\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "urban-speaker",
   "metadata": {},
   "source": [
    "The first finding is that some columns have many missing observations. For example, potassium (`pot`) has 88 missing values. \n",
    "\n",
    "How is the numeric data distributed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "rural-paraguay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.hist(figsize=(15,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worth-bryan",
   "metadata": {},
   "source": [
    "We are going to select the numeric columns and categorical columns separately for our transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "russian-prospect",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'bp', 'sg', 'al', 'su', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_columns = X.select_dtypes(include=np.number).columns.tolist()\n",
    "numeric_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "sustainable-summer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dm',\n",
       " 'pc',\n",
       " 'rc',\n",
       " 'pe',\n",
       " 'ba',\n",
       " 'pcv',\n",
       " 'appet',\n",
       " 'wc',\n",
       " 'cad',\n",
       " 'ane',\n",
       " 'rbc',\n",
       " 'pcc',\n",
       " 'htn']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns = list(set(X.columns) - set(numeric_columns))\n",
    "categorical_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greater-egypt",
   "metadata": {},
   "source": [
    "## 3. Imput missing values\n",
    "\n",
    "We will use the `SimpleImputer` class, which provides basic strategies for imputing missing values. Missing values can be imputed with a provided constant value, or using the statistics (mean, median or most frequent) of each column in which the missing values are located. This class also allows for different missing values encodings.\n",
    "* For numeric attributes, we can use `mean` to replace `NaN` values.\n",
    "* For categorical attributes, we can use `most_frequent` to replace `NaN` values.\n",
    "\n",
    "Let's start imputing the missing numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "involved-significance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will copy the X dataframe to X_imp and keep the original dataframe X separated\n",
    "X_imp = X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dangerous-technique",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the SimpleImputer object\n",
    "imp = SimpleImputer(missing_values=np.nan, strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "presidential-aviation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the imputer to fit and transform the numeric data. Replace the \n",
    "X_imp[numeric_columns] = imp.fit_transform(X_imp[numeric_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "auburn-jurisdiction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>15.4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>11.3</td>\n",
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       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>423.000000</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>137.528754</td>\n",
       "      <td>4.627244</td>\n",
       "      <td>9.6</td>\n",
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       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>56.0</td>\n",
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       "      <td>111.000000</td>\n",
       "      <td>2.500000</td>\n",
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       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>137.528754</td>\n",
       "      <td>4.627244</td>\n",
       "      <td>11.6</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>49.0</td>\n",
       "      <td>0.5</td>\n",
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       "      <th>396</th>\n",
       "      <td>42.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>31.0</td>\n",
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       "      <td>141.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>16.5</td>\n",
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       "      <th>397</th>\n",
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       "      <td>1.020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>4.400000</td>\n",
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       "      <td>135.000000</td>\n",
       "      <td>4.900000</td>\n",
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       "      <th>399</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>141.000000</td>\n",
       "      <td>3.500000</td>\n",
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       "<p>400 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "      age    bp     sg   al   su         bgr    bu   sc         sod       pot  \\\n",
       "id                                                                              \n",
       "0    48.0  80.0  1.020  1.0  0.0  121.000000  36.0  1.2  137.528754  4.627244   \n",
       "1     7.0  50.0  1.020  4.0  0.0  148.036517  18.0  0.8  137.528754  4.627244   \n",
       "2    62.0  80.0  1.010  2.0  3.0  423.000000  53.0  1.8  137.528754  4.627244   \n",
       "3    48.0  70.0  1.005  4.0  0.0  117.000000  56.0  3.8  111.000000  2.500000   \n",
       "4    51.0  80.0  1.010  2.0  0.0  106.000000  26.0  1.4  137.528754  4.627244   \n",
       "..    ...   ...    ...  ...  ...         ...   ...  ...         ...       ...   \n",
       "395  55.0  80.0  1.020  0.0  0.0  140.000000  49.0  0.5  150.000000  4.900000   \n",
       "396  42.0  70.0  1.025  0.0  0.0   75.000000  31.0  1.2  141.000000  3.500000   \n",
       "397  12.0  80.0  1.020  0.0  0.0  100.000000  26.0  0.6  137.000000  4.400000   \n",
       "398  17.0  60.0  1.025  0.0  0.0  114.000000  50.0  1.0  135.000000  4.900000   \n",
       "399  58.0  80.0  1.025  0.0  0.0  131.000000  18.0  1.1  141.000000  3.500000   \n",
       "\n",
       "     hemo  \n",
       "id         \n",
       "0    15.4  \n",
       "1    11.3  \n",
       "2     9.6  \n",
       "3    11.2  \n",
       "4    11.6  \n",
       "..    ...  \n",
       "395  15.7  \n",
       "396  16.5  \n",
       "397  15.8  \n",
       "398  14.2  \n",
       "399  15.8  \n",
       "\n",
       "[400 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_imp[numeric_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "instrumental-dimension",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        0\n",
       "bp         0\n",
       "sg         0\n",
       "al         0\n",
       "su         0\n",
       "rbc      152\n",
       "pc        65\n",
       "pcc        4\n",
       "ba         4\n",
       "bgr        0\n",
       "bu         0\n",
       "sc         0\n",
       "sod        0\n",
       "pot        0\n",
       "hemo       0\n",
       "pcv       70\n",
       "wc       105\n",
       "rc       130\n",
       "htn        2\n",
       "dm         2\n",
       "cad        2\n",
       "appet      1\n",
       "pe         1\n",
       "ane        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how many missing values each feature has. \n",
    "X_imp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sensitive-olympus",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Create an `SimpleImputer` object and impute the missing categorical data. Use `most_frequent` as the strategy. Do not forget to use `X_imp` for this operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "swedish-report",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the imputer using `most_frequent` strategy\n",
    "imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "infrared-melissa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute the missing categorical values of X_imp\n",
    "X_imp[categorical_columns] = imp.fit_transform(X_imp[categorical_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "interesting-hopkins",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      dm        pc   rc   pe          ba pcv appet    wc cad  ane     rbc  \\\n",
       "id                                                                          \n",
       "0    yes    normal  5.2   no  notpresent  44  good  7800  no   no  normal   \n",
       "1     no    normal  5.2   no  notpresent  38  good  6000  no   no  normal   \n",
       "2    yes    normal  5.2   no  notpresent  31  poor  7500  no  yes  normal   \n",
       "3     no  abnormal  3.9  yes  notpresent  32  poor  6700  no  yes  normal   \n",
       "4     no    normal  4.6   no  notpresent  35  good  7300  no   no  normal   \n",
       "..   ...       ...  ...  ...         ...  ..   ...   ...  ..  ...     ...   \n",
       "395   no    normal  4.9   no  notpresent  47  good  6700  no   no  normal   \n",
       "396   no    normal  6.2   no  notpresent  54  good  7800  no   no  normal   \n",
       "397   no    normal  5.4   no  notpresent  49  good  6600  no   no  normal   \n",
       "398   no    normal  5.9   no  notpresent  51  good  7200  no   no  normal   \n",
       "399   no    normal  6.1   no  notpresent  53  good  6800  no   no  normal   \n",
       "\n",
       "            pcc  htn  \n",
       "id                    \n",
       "0    notpresent  yes  \n",
       "1    notpresent   no  \n",
       "2    notpresent   no  \n",
       "3       present  yes  \n",
       "4    notpresent   no  \n",
       "..          ...  ...  \n",
       "395  notpresent   no  \n",
       "396  notpresent   no  \n",
       "397  notpresent   no  \n",
       "398  notpresent   no  \n",
       "399  notpresent   no  \n",
       "\n",
       "[400 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the results\n",
    "X_imp[categorical_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "private-least",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age      0\n",
       "bp       0\n",
       "sg       0\n",
       "al       0\n",
       "su       0\n",
       "rbc      0\n",
       "pc       0\n",
       "pcc      0\n",
       "ba       0\n",
       "bgr      0\n",
       "bu       0\n",
       "sc       0\n",
       "sod      0\n",
       "pot      0\n",
       "hemo     0\n",
       "pcv      0\n",
       "wc       0\n",
       "rc       0\n",
       "htn      0\n",
       "dm       0\n",
       "cad      0\n",
       "appet    0\n",
       "pe       0\n",
       "ane      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how many missing values each feature has. All values should be zero now. \n",
    "X_imp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "positive-albany",
   "metadata": {},
   "source": [
    "## 4. Scale the data\n",
    "Yesterday we discussed the importance of scaling numeric data to (a) handle outliers and (b) compare the effect of increasing one unit of each feature on the dependent variable. We will standarize the numeric features present in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "useful-desktop",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We copy the imputed data to a new dataframe.\n",
    "X_scaled = X_imp.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "settled-brisbane",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Create the scaler using `preprocessing.StandardScaler()` and then replace the numeric columns of `X_scaled` with the scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "thermal-corruption",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create the scaler using preprocessing.StandardScaler()\n",
    "scaler = preprocessing.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "assured-sodium",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use the scaler to fit and transform only the numeric features (`X_imp[numeric_columns]`)\n",
    "X_scaled[numeric_columns] = scaler.fit_transform(X_imp[numeric_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "acquired-oliver",
   "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>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.92</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-14.47</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-3.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.27</td>\n",
       "      <td>7.69</td>\n",
       "      <td>1.42</td>\n",
       "      <td>3.13</td>\n",
       "      <td>4.43</td>\n",
       "      <td>4.58</td>\n",
       "      <td>6.78</td>\n",
       "      <td>13.00</td>\n",
       "      <td>2.77</td>\n",
       "      <td>15.05</td>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age      bp      sg      al      su     bgr      bu      sc     sod  \\\n",
       "count  400.00  400.00  400.00  400.00  400.00  400.00  400.00  400.00  400.00   \n",
       "mean     0.00    0.00    0.00    0.00   -0.00   -0.00    0.00    0.00    0.00   \n",
       "std      1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00   \n",
       "min     -2.92   -1.97   -2.31   -0.80   -0.44   -1.69   -1.14   -0.48  -14.47   \n",
       "25%     -0.56   -0.48   -0.45   -0.80   -0.44   -0.63   -0.62   -0.39   -0.28   \n",
       "50%      0.15    0.13    0.00   -0.01   -0.44   -0.30   -0.27   -0.30    0.00   \n",
       "75%      0.74    0.26    0.48    0.77   -0.00    0.03    0.09    0.00    0.38   \n",
       "max      2.27    7.69    1.42    3.13    4.43    4.58    6.78   13.00    2.77   \n",
       "\n",
       "          pot    hemo  \n",
       "count  400.00  400.00  \n",
       "mean    -0.00    0.00  \n",
       "std      1.00    1.00  \n",
       "min     -0.76   -3.47  \n",
       "25%     -0.22   -0.61  \n",
       "50%      0.00    0.00  \n",
       "75%      0.06    0.77  \n",
       "max     15.05    1.94  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We print the descriptive statistics\n",
    "round(X_scaled.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "interracial-stadium",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We plot the distributions and check the scales\n",
    "X_scaled.hist(figsize=(15,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "norman-medication",
   "metadata": {},
   "source": [
    "### Handling categorical data\n",
    "These are transformers that are not intended to be used on features, only on supervised learning targets. For example, categorical data is not mathematically represented in the dataset. \n",
    "\n",
    "To handle this situation, scikit-learn provides the function `LabelEncoder`, which is a utility class to help normalize labels from categorical features. It will replace the classes with integers that represent each class. In R, this is also known as *factors.* `LabelEncoder` can be used as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "liked-classic",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create the `LabelEncoder`\n",
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "retained-arthritis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['high', 'low', 'medium']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a toy example. First, we will encode the classes and detect the unique classes\n",
    "le.fit([\"low\", \"medium\", \"high\", \"medium\"])\n",
    "list(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cardiac-homeless",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Based on this Encoder, we took the vector of categorical data and transform it to numeric data\n",
    "le.transform([\"low\", \"low\", \"high\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "tough-authentication",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['low', 'low', 'high']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can decode a vector of numeric values to obtain the original classes\n",
    "list(le.inverse_transform([1, 1, 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prostate-yorkshire",
   "metadata": {},
   "source": [
    "Now, let's get back to our dataset. We need to transform the categorical features and encode them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "following-immigration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dm',\n",
       " 'pc',\n",
       " 'rc',\n",
       " 'pe',\n",
       " 'ba',\n",
       " 'pcv',\n",
       " 'appet',\n",
       " 'wc',\n",
       " 'cad',\n",
       " 'ane',\n",
       " 'rbc',\n",
       " 'pcc',\n",
       " 'htn']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confidential-terry",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Since we have 13 categorical features, we will create a Python loop and save the encoders in a dictionary. You will have to create inside of the loop the Encoder and transform the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fitting-remains",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary of encoders\n",
    "encoders = {}\n",
    "\n",
    "# Run the loop\n",
    "for column in categorical_columns:\n",
    "    # Create the LabelEncoder\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    \n",
    "    # Train the LabelEncoder using the X[column] \n",
    "    le.fit(X_scaled[column])\n",
    "    \n",
    "    # Replace the X_scale[column] with the transformed data\n",
    "    X_scaled[column] = le.transform(X_scaled[column])\n",
    "    \n",
    "    # Append the Encoder to the dictionary\n",
    "    encoders[column] = le"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "potential-projector",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dm': LabelEncoder(),\n",
       " 'pc': LabelEncoder(),\n",
       " 'rc': LabelEncoder(),\n",
       " 'pe': LabelEncoder(),\n",
       " 'ba': LabelEncoder(),\n",
       " 'pcv': LabelEncoder(),\n",
       " 'appet': LabelEncoder(),\n",
       " 'wc': LabelEncoder(),\n",
       " 'cad': LabelEncoder(),\n",
       " 'ane': LabelEncoder(),\n",
       " 'rbc': LabelEncoder(),\n",
       " 'pcc': LabelEncoder(),\n",
       " 'htn': LabelEncoder()}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the dictionary of encoders\n",
    "encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "express-mouth",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age      float64\n",
       "bp       float64\n",
       "sg       float64\n",
       "al       float64\n",
       "su       float64\n",
       "rbc        int64\n",
       "pc         int64\n",
       "pcc        int64\n",
       "ba         int64\n",
       "bgr      float64\n",
       "bu       float64\n",
       "sc       float64\n",
       "sod      float64\n",
       "pot      float64\n",
       "hemo     float64\n",
       "pcv        int64\n",
       "wc         int64\n",
       "rc         int64\n",
       "htn        int64\n",
       "dm         int64\n",
       "cad        int64\n",
       "appet      int64\n",
       "pe         int64\n",
       "ane        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We check the columns, we will see that now all columns are numeric.\n",
    "X_scaled.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "union-reserve",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "          age        bp        sg        al        su  rbc  pc  pcc  ba  \\\n",
       "id                                                                        \n",
       "0   -0.205464  0.262338  0.483355 -0.013338 -0.437797    1   1    0   0   \n",
       "1   -2.623810 -1.966580  0.483355  2.347516 -0.437797    1   1    0   0   \n",
       "2    0.620313  0.262338 -1.381391  0.773613  2.479925    1   1    0   0   \n",
       "3   -0.205464 -0.480635 -2.313764  2.347516 -0.437797    1   0    1   0   \n",
       "4   -0.028511  0.262338 -1.381391  0.773613 -0.437797    1   1    0   0   \n",
       "..        ...       ...       ...       ...       ...  ...  ..  ...  ..   \n",
       "395  0.207425  0.262338  0.483355 -0.800290 -0.437797    1   1    0   0   \n",
       "396 -0.559368 -0.480635  1.415727 -0.800290 -0.437797    1   1    0   0   \n",
       "397 -2.328890  0.262338  0.483355 -0.800290 -0.437797    1   1    0   0   \n",
       "398 -2.033969 -1.223607  1.415727 -0.800290 -0.437797    1   1    0   0   \n",
       "399  0.384377  0.262338  1.415727 -0.800290 -0.437797    1   1    0   0   \n",
       "\n",
       "          bgr  ...      hemo  pcv  wc  rc  htn  dm  cad  appet  pe  ane  \n",
       "id             ...                                                       \n",
       "0   -0.361987  ...  1.059271   32  72  34    1   4    1      0   0    0  \n",
       "1    0.000000  ... -0.452097   26  56  34    0   3    1      0   0    0  \n",
       "2    3.681441  ... -1.078762   19  70  34    0   4    1      1   0    1  \n",
       "3   -0.415543  ... -0.488960   20  62  19    1   3    1      1   1    1  \n",
       "4   -0.562820  ... -0.341509   23  68  27    0   3    1      0   0    0  \n",
       "..        ...  ...       ...  ...  ..  ..  ...  ..  ...    ...  ..  ...  \n",
       "395 -0.107600  ...  1.169859   35  62  30    0   3    1      0   0    0  \n",
       "396 -0.977874  ...  1.464760   42  72  44    0   3    1      0   0    0  \n",
       "397 -0.643153  ...  1.206722   37  61  36    0   3    1      0   0    0  \n",
       "398 -0.455709  ...  0.616920   39  67  41    0   3    1      0   0    0  \n",
       "399 -0.228099  ...  1.206722   41  63  43    0   3    1      0   0    0  \n",
       "\n",
       "[400 rows x 24 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can print the final dataframe\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hungarian-pharmacy",
   "metadata": {},
   "source": [
    "## 5. Create training and testing datasets\n",
    "We split our dataset into two portions: training dataset and testing dataset. We will use the former to train the predictive model, and the latter to evaluate the model's accuracy. To split the data, we use `train_test_split` function provided by scikit-learn library. There are four parameters of this function\n",
    "* The imput data (X_scaled)\n",
    "* The target vector (y)\n",
    "* `test_size`: The dataset's percentage that will be used for testing. We will use `0.20`.\n",
    "* `random_state`: Random seed. We can keep it fixed so we can reproduce the random numbers across multiple calls. We will use [42](https://hitchhikers.fandom.com/wiki/42). \n",
    "\n",
    "### Excercise\n",
    "Create the training and testing datasets. Remember that this method returns 4 variables (`X_train`, `X_test`, `y_train`, `y_test`). You can check the function's [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) to check how the parameters are set up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "premier-tonight",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create here the training and testing datasets using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "color-malpractice",
   "metadata": {},
   "outputs": [
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       "      <td>-0.509265</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.263075</td>\n",
       "      <td>14</td>\n",
       "      <td>84</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.446090e+00</td>\n",
       "      <td>-0.480635</td>\n",
       "      <td>-0.449018</td>\n",
       "      <td>-0.013338</td>\n",
       "      <td>-4.377969e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.750264</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.820723</td>\n",
       "      <td>20</td>\n",
       "      <td>90</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>1.328122e+00</td>\n",
       "      <td>-1.223607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.398871e-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.536042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>90</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>-3.234318e-01</td>\n",
       "      <td>2.491257</td>\n",
       "      <td>-0.449018</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-4.377969e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.241488</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>90</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>5.023450e-01</td>\n",
       "      <td>-0.480635</td>\n",
       "      <td>-1.381391</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-4.377969e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.107600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>90</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>7.972653e-01</td>\n",
       "      <td>1.748284</td>\n",
       "      <td>-0.449018</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-4.377969e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.777041</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.263075</td>\n",
       "      <td>16</td>\n",
       "      <td>51</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>5.023450e-01</td>\n",
       "      <td>-1.966580</td>\n",
       "      <td>-1.381391</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-4.377969e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.512449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>4.433610e-01</td>\n",
       "      <td>-0.480635</td>\n",
       "      <td>-1.381391</td>\n",
       "      <td>-0.013338</td>\n",
       "      <td>2.479925e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.694830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027117</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>-2.851147e-02</td>\n",
       "      <td>0.262338</td>\n",
       "      <td>0.483355</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-4.377969e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.723486</td>\n",
       "      <td>...</td>\n",
       "      <td>1.096134</td>\n",
       "      <td>34</td>\n",
       "      <td>87</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        bp        sg        al            su  rbc  pc  pcc  \\\n",
       "id                                                                            \n",
       "205  5.613291e-01 -0.480635  0.000000  0.000000 -5.398871e-17    1   1    0   \n",
       "191  4.191069e-16 -0.480635 -1.381391  1.560565 -4.377969e-01    1   1    0   \n",
       "36   1.446090e+00 -0.480635 -0.449018 -0.013338 -4.377969e-01    1   1    0   \n",
       "222  1.328122e+00 -1.223607  0.000000  0.000000 -5.398871e-17    1   1    0   \n",
       "192 -3.234318e-01  2.491257 -0.449018 -0.800290 -4.377969e-01    1   1    0   \n",
       "..            ...       ...       ...       ...           ...  ...  ..  ...   \n",
       "119  5.023450e-01 -0.480635 -1.381391 -0.800290 -4.377969e-01    1   1    0   \n",
       "124  7.972653e-01  1.748284 -0.449018 -0.800290 -4.377969e-01    1   1    0   \n",
       "175  5.023450e-01 -1.966580 -1.381391 -0.800290 -4.377969e-01    1   1    0   \n",
       "248  4.433610e-01 -0.480635 -1.381391 -0.013338  2.479925e+00    0   0    0   \n",
       "388 -2.851147e-02  0.262338  0.483355 -0.800290 -4.377969e-01    1   1    0   \n",
       "\n",
       "     ba       bgr  ...      hemo  pcv  wc  rc  htn  dm  cad  appet  pe  ane  \n",
       "id                 ...                                                       \n",
       "205   0 -0.643153  ...  0.027117   31  90  34    1   4    1      0   0    0  \n",
       "191   0 -0.509265  ... -1.263075   14  84  14    1   4    1      1   0    0  \n",
       "36    0 -0.750264  ... -0.820723   20  90  34    1   3    1      0   0    0  \n",
       "222   0 -0.536042  ...  0.000000   29  90  34    1   4    1      0   0    0  \n",
       "192   0 -0.241488  ...  0.000000   29  90  34    0   3    1      0   0    0  \n",
       "..   ..       ...  ...       ...  ...  ..  ..  ...  ..  ...    ...  ..  ...  \n",
       "119   0 -0.107600  ...  0.000000   29  90  34    0   3    1      0   0    0  \n",
       "124   0 -0.777041  ... -1.263075   16  51  16    1   3    1      0   0    0  \n",
       "175   0  1.512449  ...  0.000000   29  41  14    1   3    1      0   0    0  \n",
       "248   0  3.694830  ...  0.027117   25   3  22    1   4    2      0   0    0  \n",
       "388   0 -0.723486  ...  1.096134   34  87  46    0   3    1      0   0    0  \n",
       "\n",
       "[320 rows x 24 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We print the training dataset. We will have 320 observations.\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "labeled-warner",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>...</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.387106</td>\n",
       "      <td>2.623384e-01</td>\n",
       "      <td>-0.449018</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-0.437797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.106622</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.341509</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.561329</td>\n",
       "      <td>2.623384e-01</td>\n",
       "      <td>0.483355</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-0.437797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.228099</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.452097</td>\n",
       "      <td>23</td>\n",
       "      <td>90</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>0.738281</td>\n",
       "      <td>-4.806345e-01</td>\n",
       "      <td>0.483355</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-0.437797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.683319</td>\n",
       "      <td>...</td>\n",
       "      <td>0.469469</td>\n",
       "      <td>37</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>-0.618352</td>\n",
       "      <td>-4.806345e-01</td>\n",
       "      <td>0.483355</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-0.437797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.308432</td>\n",
       "      <td>...</td>\n",
       "      <td>1.575348</td>\n",
       "      <td>29</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.621081</td>\n",
       "      <td>1.055828e-15</td>\n",
       "      <td>-0.449018</td>\n",
       "      <td>0.773613</td>\n",
       "      <td>3.452500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.507386</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046608</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>-1.621081</td>\n",
       "      <td>2.623384e-01</td>\n",
       "      <td>0.483355</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-0.437797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.736875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>-0.087496</td>\n",
       "      <td>-4.806345e-01</td>\n",
       "      <td>0.483355</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-0.437797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.522653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>90</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-0.146480</td>\n",
       "      <td>1.748284e+00</td>\n",
       "      <td>-1.381391</td>\n",
       "      <td>1.560565</td>\n",
       "      <td>-0.437797</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.254877</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.631701</td>\n",
       "      <td>12</td>\n",
       "      <td>88</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>1.151170</td>\n",
       "      <td>-1.223607e+00</td>\n",
       "      <td>1.415727</td>\n",
       "      <td>-0.800290</td>\n",
       "      <td>-0.437797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.985546</td>\n",
       "      <td>30</td>\n",
       "      <td>71</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1.151170</td>\n",
       "      <td>-1.223607e+00</td>\n",
       "      <td>-0.449018</td>\n",
       "      <td>2.347516</td>\n",
       "      <td>-0.437797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.402154</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.415234</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age            bp        sg        al        su  rbc  pc  pcc  ba  \\\n",
       "id                                                                            \n",
       "26   1.387106  2.623384e-01 -0.449018 -0.800290 -0.437797    1   1    0   0   \n",
       "182  0.561329  2.623384e-01  0.483355 -0.800290 -0.437797    1   1    0   0   \n",
       "377  0.738281 -4.806345e-01  0.483355 -0.800290 -0.437797    1   1    0   0   \n",
       "296 -0.618352 -4.806345e-01  0.483355 -0.800290 -0.437797    1   1    0   0   \n",
       "7   -1.621081  1.055828e-15 -0.449018  0.773613  3.452500    1   0    0   0   \n",
       "..        ...           ...       ...       ...       ...  ...  ..  ...  ..   \n",
       "365 -1.621081  2.623384e-01  0.483355 -0.800290 -0.437797    1   1    0   0   \n",
       "89  -0.087496 -4.806345e-01  0.483355 -0.800290 -0.437797    1   1    0   0   \n",
       "196 -0.146480  1.748284e+00 -1.381391  1.560565 -0.437797    0   0    0   0   \n",
       "378  1.151170 -1.223607e+00  1.415727 -0.800290 -0.437797    1   1    0   0   \n",
       "127  1.151170 -1.223607e+00 -0.449018  2.347516 -0.437797    1   1    0   0   \n",
       "\n",
       "          bgr  ...      hemo  pcv  wc  rc  htn  dm  cad  appet  pe  ane  \n",
       "id             ...                                                       \n",
       "26   0.106622  ... -0.341509   23   4  20    1   4    1      1   0    0  \n",
       "182 -0.228099  ... -0.452097   23  90  34    0   3    1      0   0    0  \n",
       "377 -0.683319  ...  0.469469   37  59  29    0   3    1      0   0    0  \n",
       "296 -0.308432  ...  1.575348   29  58  41    0   3    1      0   0    0  \n",
       "7    3.507386  ... -0.046608   32  64  31    0   4    1      0   1    0  \n",
       "..        ...  ...       ...  ...  ..  ..  ...  ..  ...    ...  ..  ...  \n",
       "365 -0.736875  ...  0.000000   29   7  45    0   3    1      0   0    0  \n",
       "89  -0.522653  ...  0.000000   29  90  34    0   3    1      1   0    0  \n",
       "196 -0.254877  ... -1.631701   12  88  15    1   4    1      1   1    1  \n",
       "378  0.000000  ...  0.985546   30  71  37    0   3    1      0   0    0  \n",
       "127 -0.402154  ... -0.415234   23  29  24    1   4    1      1   1    0  \n",
       "\n",
       "[80 rows x 24 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the testing dataset. We will have 80 observations\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "muslim-inventory",
   "metadata": {},
   "source": [
    "## 6. Train a neural network with the training dataset\n",
    "\n",
    "For this exercise, we will create a neural network called Multi-layer perceptron (MLP). The model learns from the dataset through three types of layers:\n",
    "* *The input layer*: The input layer receives the features to be processed.\n",
    "* *The output layer*: The prediction and classification is performed by the output layer.\n",
    "* *The hidden layers*: An arbitrary number of hidden layers that are placed in between the input and output layer perform the computation. Each layer has nodes (also known as neurons) that transforms the values from the previous layer into an activation funcition. The output layer receives the activated values from the last hidden layer and transforms them into output values.\n",
    "\n",
    "<img src=\"https://static.javatpoint.com/tutorial/tensorflow/images/multi-layer-perceptron-in-tensorflow.png\" alt=\"drawing\" width=\"400\"/>\n",
    "\n",
    "Multi-layer perceptrons are networks of nodes that pass a linear combination of their inputs from one layer to another. As they do this, the nodes decide how to modify their inputs, utilizing a given activation function. The activation function of a neuron is the key here. By selecting non-linear activation functions, the neural network can embed non-linearity in its operation:\n",
    "\n",
    "<img src=\"https://www.baeldung.com/wp-content/uploads/sites/4/2020/06/Copy-of-Copy-of-Blank-Diagram1.svg\" alt=\"drawing\" width=\"400\"/>\n",
    "\n",
    "Neural networks are good for non-linear classes of problems. The first advantage of neural networks is, therefore, their flexibility in addressing problems with non-linear shapes. MLPs are implemented in diverse fields such as speech recognition, image recognition, and machine translation software. From artificial neural networks, **deep learning** has emerged as a sub-field of machine learning.\n",
    "\n",
    "Let's create a simple perceptron with 5 nodes in two layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "proud-scroll",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MLPClassifier(hidden_layer_sizes=(5, 2), random_state=1,max_iter=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "future-buying",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Use the `fit` function to train the model. Remember that it has two parameters: `X_train` and `y_train`.\n",
    "Then, use the `predict` to predict the values using `X_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "stock-tsunami",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(5, 2), max_iter=1000, random_state=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "protecting-worry",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the predict function to test the model\n",
    "y_predicted = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dirty-chile",
   "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>y_real</th>\n",
       "      <th>y_predicted</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_real  y_predicted  difference\n",
       "id                                  \n",
       "26        1            1        True\n",
       "182       1            1        True\n",
       "377       0            0        True\n",
       "296       0            0        True\n",
       "7         1            1        True\n",
       "..      ...          ...         ...\n",
       "365       0            0        True\n",
       "89        1            1        True\n",
       "196       1            1        True\n",
       "378       0            0        True\n",
       "127       1            1        True\n",
       "\n",
       "[80 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We create a dataframe with the real target and the predicted target\n",
    "y_train_df = pd.DataFrame({'y_real': y_test, 'y_predicted': y_predicted})\n",
    "y_train_df['difference'] = y_train_df.y_real == y_train_df.y_predicted \n",
    "y_train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advance-glucose",
   "metadata": {},
   "source": [
    "## 7. Evaluate the model with the testing dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "understood-consciousness",
   "metadata": {},
   "source": [
    "In binary classiifcation problems, there are two main metrics:\n",
    "* Precision: What proportion of positive identifications was actually correct?\n",
    "* Recall: What proportion of actual positives was identified correctly?\n",
    "\n",
    "Precision and recall are performance metrics that apply to data retrieved from a classification process. We can see the diferences here:\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/Precisionrecall.svg/700px-Precisionrecall.svg.png\" width=\"400px\">\n",
    "\n",
    "\n",
    "To check the mathematical formulas, check [here](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "stock-drama",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98        32\n",
      "           1       1.00      0.98      0.99        48\n",
      "\n",
      "    accuracy                           0.99        80\n",
      "   macro avg       0.98      0.99      0.99        80\n",
      "weighted avg       0.99      0.99      0.99        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# We a text report showing the main classification metrics.\n",
    "print(classification_report(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "junior-tribune",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can print these numbers as the confusion matrix.\n",
    "plot_confusion_matrix(clf, X_test, y_test, cmap=plt.cm.Blues)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "married-chester",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Create a new model using 10 layers with 10 nodes each layer. Would the performance improve? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "equipped-leave",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "coordinate-training",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MLPClassifier(hidden_layer_sizes=(10, 10), random_state=1,max_iter=1000)\n",
    "clf.fit(X_train, y_train)\n",
    "y_predicted = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "champion-engagement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98        28\n",
      "           1       1.00      0.98      0.99        52\n",
      "\n",
      "    accuracy                           0.99        80\n",
      "   macro avg       0.98      0.99      0.99        80\n",
      "weighted avg       0.99      0.99      0.99        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "australian-liberal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(clf, X_test, y_test, cmap=plt.cm.Blues)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conditional-victory",
   "metadata": {},
   "source": [
    "## What if..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interracial-circulation",
   "metadata": {},
   "source": [
    "### Using the raw data, removing the NaNs and only numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "hearing-tours",
   "metadata": {},
   "outputs": [],
   "source": [
    "kidney_no_na = kidney_df.dropna()\n",
    "X_no_na = kidney_no_na.drop(['classification'], axis=1)\n",
    "y_no_na = kidney_no_na['classification'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "considerable-transmission",
   "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>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>...</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.2</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.5</td>\n",
       "      <td>29</td>\n",
       "      <td>12100</td>\n",
       "      <td>3.7</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>63.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>380.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.8</td>\n",
       "      <td>32</td>\n",
       "      <td>4500</td>\n",
       "      <td>3.8</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>68.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>present</td>\n",
       "      <td>157.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.6</td>\n",
       "      <td>16</td>\n",
       "      <td>11000</td>\n",
       "      <td>2.6</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>61.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>173.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>24</td>\n",
       "      <td>9200</td>\n",
       "      <td>3.2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>55.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>140.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.7</td>\n",
       "      <td>47</td>\n",
       "      <td>6700</td>\n",
       "      <td>4.9</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>42.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>75.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.5</td>\n",
       "      <td>54</td>\n",
       "      <td>7800</td>\n",
       "      <td>6.2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.8</td>\n",
       "      <td>49</td>\n",
       "      <td>6600</td>\n",
       "      <td>5.4</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>114.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.2</td>\n",
       "      <td>51</td>\n",
       "      <td>7200</td>\n",
       "      <td>5.9</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>58.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>131.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.8</td>\n",
       "      <td>53</td>\n",
       "      <td>6800</td>\n",
       "      <td>6.1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age    bp     sg   al   su       rbc        pc         pcc          ba  \\\n",
       "id                                                                             \n",
       "3    48.0  70.0  1.005  4.0  0.0    normal  abnormal     present  notpresent   \n",
       "9    53.0  90.0  1.020  2.0  0.0  abnormal  abnormal     present  notpresent   \n",
       "11   63.0  70.0  1.010  3.0  0.0  abnormal  abnormal     present  notpresent   \n",
       "14   68.0  80.0  1.010  3.0  2.0    normal  abnormal     present     present   \n",
       "20   61.0  80.0  1.015  2.0  0.0  abnormal  abnormal  notpresent  notpresent   \n",
       "..    ...   ...    ...  ...  ...       ...       ...         ...         ...   \n",
       "395  55.0  80.0  1.020  0.0  0.0    normal    normal  notpresent  notpresent   \n",
       "396  42.0  70.0  1.025  0.0  0.0    normal    normal  notpresent  notpresent   \n",
       "397  12.0  80.0  1.020  0.0  0.0    normal    normal  notpresent  notpresent   \n",
       "398  17.0  60.0  1.025  0.0  0.0    normal    normal  notpresent  notpresent   \n",
       "399  58.0  80.0  1.025  0.0  0.0    normal    normal  notpresent  notpresent   \n",
       "\n",
       "       bgr  ...  hemo  pcv     wc   rc  htn   dm  cad appet   pe  ane  \n",
       "id          ...                                                        \n",
       "3    117.0  ...  11.2   32   6700  3.9  yes   no   no  poor  yes  yes  \n",
       "9     70.0  ...   9.5   29  12100  3.7  yes  yes   no  poor   no  yes  \n",
       "11   380.0  ...  10.8   32   4500  3.8  yes  yes   no  poor  yes   no  \n",
       "14   157.0  ...   5.6   16  11000  2.6  yes  yes  yes  poor  yes   no  \n",
       "20   173.0  ...   7.7   24   9200  3.2  yes  yes  yes  poor  yes  yes  \n",
       "..     ...  ...   ...  ...    ...  ...  ...  ...  ...   ...  ...  ...  \n",
       "395  140.0  ...  15.7   47   6700  4.9   no   no   no  good   no   no  \n",
       "396   75.0  ...  16.5   54   7800  6.2   no   no   no  good   no   no  \n",
       "397  100.0  ...  15.8   49   6600  5.4   no   no   no  good   no   no  \n",
       "398  114.0  ...  14.2   51   7200  5.9   no   no   no  good   no   no  \n",
       "399  131.0  ...  15.8   53   6800  6.1   no   no   no  good   no   no  \n",
       "\n",
       "[158 rows x 24 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_no_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "proud-hormone",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_no_na[numeric_columns], y_no_na, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "virtual-breathing",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MLPClassifier(alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1,max_iter=1000)\n",
    "clf.fit(X_train, y_train)\n",
    "y_predicted = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "developing-testimony",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96        23\n",
      "           1       1.00      0.78      0.88         9\n",
      "\n",
      "    accuracy                           0.94        32\n",
      "   macro avg       0.96      0.89      0.92        32\n",
      "weighted avg       0.94      0.94      0.93        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "initial-works",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(clf, X_test, y_test, cmap=plt.cm.Blues)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "short-variable",
   "metadata": {},
   "source": [
    "### No categorical attributes and no rescale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "billion-manhattan",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_imp[numeric_columns], y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "extreme-smell",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "clf = MLPClassifier(alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1,max_iter=1000)\n",
    "clf.fit(X_train, y_train)\n",
    "y_predicted = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "macro-institution",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.93      0.87        28\n",
      "           1       0.96      0.88      0.92        52\n",
      "\n",
      "    accuracy                           0.90        80\n",
      "   macro avg       0.89      0.91      0.89        80\n",
      "weighted avg       0.91      0.90      0.90        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "thousand-expansion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(clf, X_test, y_test, cmap=plt.cm.Blues)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hundred-promise",
   "metadata": {},
   "source": [
    "### Scaled, but no categorical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "anonymous-placement",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled[numeric_columns], y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "informed-subdivision",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MLPClassifier(hidden_layer_sizes=(5, 2), random_state=1, max_iter=1000)\n",
    "clf.fit(X_train, y_train)\n",
    "y_predicted = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "numerical-garden",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.96      0.95        28\n",
      "           1       0.98      0.96      0.97        52\n",
      "\n",
      "    accuracy                           0.96        80\n",
      "   macro avg       0.96      0.96      0.96        80\n",
      "weighted avg       0.96      0.96      0.96        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "hispanic-danish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(clf, X_test, y_test, cmap=plt.cm.Blues)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strong-north",
   "metadata": {},
   "source": [
    "### Only categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "czech-landing",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled[categorical_columns], y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "sacred-absolute",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MLPClassifier(alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1,max_iter=2000)\n",
    "clf.fit(X_train, y_train)\n",
    "y_predicted = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "internal-legislation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.96      0.93        28\n",
      "           1       0.98      0.94      0.96        52\n",
      "\n",
      "    accuracy                           0.95        80\n",
      "   macro avg       0.94      0.95      0.95        80\n",
      "weighted avg       0.95      0.95      0.95        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "centered-warning",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(clf, X_test, y_test, cmap=plt.cm.Blues)  \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cultural-nursery",
   "metadata": {},
   "source": [
    "## More to learn...\n",
    "* How do we estimate the correct parameters to use?: [Hyperparameter optimization](https://scikit-learn.org/stable/modules/grid_search.html)\n",
    "* How do evaluate the model? [Metrics and scoring](https://scikit-learn.org/stable/modules/model_evaluation.html)\n",
    "* How can I train a model using different partitions of my dataset? [Corss-validation](https://scikit-learn.org/stable/modules/cross_validation.html)\n",
    "* Neural Networks: [TensorFlow](https://www.tensorflow.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "absolute-ottawa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}