Hospitalization-Elapse-Time.ipynb

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    "# Machine Learning Engineer Nanodegree\n",
    "## Captone Project\n",
    "## Predicting Hospitalization Time Using The Body Mass Index\n",
    "\n",
    "Doctors in the health industry consider obesity a risk factor for multiple types of illness.\n",
    "Conditions like diabetes type 2, hypertension and cardiovascular diseases and various types of tumors, in overweight patients [1] are associated with higher rates of mortality. It seems clear that obesity has a negative impact on humans.\n",
    "\n",
    "In the medical field, there is a hypothesis know as The obesity paradox [2]. The obesity\n",
    "paradox hypothesis state that overweight elderly patients have a better chance to survive\n",
    "illness like the ones describes in the previous paragraph than underweight patients.\n",
    "Multiples studies try to proof or invalidate such hypothesis [3, 4, 8]. Some of the studies have found evidence that there is indeed some time of protection granted by a high BMI (body mass index) and others argue that confounding variables and lack of follow up invalidate such proofs [5]. One thing is sure, that despite which study is the correct all of them prove one way or another that there is a correlation between the: BMI, the age, the medical condition and the smoking habits; and that this association influence negative or positive the resilient capacity of a human being.\n",
    "\n",
    "My proposal is based on the background knowledge of the correlation of the features\n",
    "described in the previous paragraph; it should be possible to train a learner to predict how much time a patient will stay hospitalized (including ICU time) due to surgery, based in the afford mentioned features.\n",
    "\n",
    "The main reason why I decided to work on this problem is that I am a firm believer that the medical field can benefit so much using technology that the sky is the limit. Patient diagnostic tools have improved way faster than the diagnostic method itself, so much is still to do in this field."
   ]
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    "## Proposed Solution\n",
    "As was described at the beginning of this notebook, obesity is considered a health factor risk in some cases and a protection feature for some others. Previous medical studies are proof one versus the other with no clear conclusion. My study proposes not agreed with any of the two positions but to use the existing information to measure how resilient the body is to surgery using the features of those previous studies. The metric is the time a patient spent hospitalized. \n",
    "My solution to the described problem, consists in determine the relation of the medical conditions, age, hospitalization time and the \"Body Mass Index.\" If such correlation is strong enough, it may be possible to train a computer model to predict the time a patient will spend hospitalized based on these features. Note that I do not intend to perform another study to proof or disproof the paradox, but to create a tool that can be used in future experiments to predict the hospitalization days and compare the prediction with the reality. \n",
    "My model has to predict a value that of the type continue, so the choice of algorithm will be a regression."
   ]
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    "## Strategy\n",
    "\n",
    "The proposed strategy to accomplish the solution is to get from the raw dataset, a set of features related to the one discuss in the studies, documented in this project proposal. Once this subset is selected, we need to compare and understand the relation between such features and the hospitalization time. Some of the features are continues which facility the identification of such connection by using statistical methods (ex. correlation). Other features like the medical condition and the gender are categorical data, in this case, the complexity of understanding their relation to the hospitalization time increases, this is where machine learning algorithms like \"Decision Tree\" can be used to find connections I can not discover using other methods.\n",
    "The use of the above strategy and some data preparation techniques like encoding,  outliers identification, balancing and data cluster identification will be utilized during this project to prepare the data as much as possible for the prediction model ingestion."
   ]
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    "## Dataset\n",
    "\n",
    "To train and test our predictor we will use some of the features of the MIMIC-III (MIT Lab for Computational Physiology) dataset. The MIMIC-III dataset contains more than 40,000 critical patients records. Information and details related to it can be found on the website https://mimic.physionet.org; this is not an open source dataset so access to it must be requested. I selected the sample of the dataset by answering the following questions:\n",
    "\n",
    "    Did the patient survive, yes or no?\n",
    "    Does the patient has pre-existing conditions, other than the one selected in the obesity paradox studies?\n",
    "    Does the patient has both metrics recorded, height and weight.\n",
    "    The MIMICIII database contains information on each visit of the patient to the hospital, for the purpose of this project we only selected the first visit information.\n",
    "\n",
    "The resultant dataset has 6862 patients. The data set was exported to a CSV file so it can be loaded into a pandas data frame.\n",
    "\n",
    "!!!! NOTE: Once the data set was in the CSV format I noticed that there were patients with age set to 300. Further reading of the MIMICIII database description shown that patients that were 89 years old or greater have their age set to 300. While the dataset was in the CSV format, we replaced all the Ages with 300 in its value by the number 89.\n",
    "### FEATURES SELECTED\n",
    "\n",
    "Body Mass Index: CHARTEVENTS table Sub-Set of Medical Conditions: DIAGNOSESICD table Age: PATIENT table Sex: PATIENT table\n",
    "### LABEL\n",
    "\n",
    "Admission Time Discharge Time (MIMIC admissions table): ADMISSION table 2 NOTE: The ”Body Mass Index” needs to be calculated from the height and weight values of the patients' data. The formula we will use is:\n",
    "\n",
    "BMI = WeightKg/(Heightmtrs) 2\n",
    "\n",
    "The features were selected based on the medical studies related to the Obesity Paradox.’ The selected label is \"continue\" as most of the features values. The only feature that requires encoding is the Sex and Icd9 codes.\n",
    "\n",
    "I will perform the following steps to prepare the data set for exploration:\n",
    "\n",
    "1. load the dataset into a panda data frame.\n",
    "2. Encode the SEX and PAtient_Diagnoctic_Icd9 features. The icd9 codes look like continues data, but in reality, they are classes of the medical conditions observed in the obesity paradox studies.\n",
    "3. We will drop the Hospital_Stay_Days from our dataset and stored in a separate variable. Hospital_Stay_Days is the variables we want to predict.\n"
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Patient_Gender</th>\n",
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       "  Patient_Gender  Patient_Diagnostic_Icd9  Patient_Age   BIM\n",
       "0              M                     4019           71  29.0\n",
       "1              M                    27401           71  29.0\n",
       "2              M                    42731           71  29.0\n",
       "3              M                     4019           68  30.0\n",
       "4              M                     2724           68  30.0"
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       "      <th>Patient_Age</th>\n",
       "      <th>BIM</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>cardiovascular_disorders</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>lipid_disorders</th>\n",
       "      <th>neoplasia</th>\n",
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       "   Patient_Age   BIM    F    M  cardiovascular_disorders  diabetes  \\\n",
       "0           71  29.0  0.0  1.0                       0.0       0.0   \n",
       "1           71  29.0  0.0  1.0                       1.0       0.0   \n",
       "2           68  30.0  0.0  1.0                       0.0       0.0   \n",
       "3           68  30.0  0.0  1.0                       0.0       0.0   \n",
       "4           68  30.0  0.0  1.0                       0.0       1.0   \n",
       "\n",
       "   hypertension  lipid_disorders  neoplasia  \n",
       "0           1.0              0.0        0.0  \n",
       "1           0.0              0.0        0.0  \n",
       "2           1.0              0.0        0.0  \n",
       "3           0.0              1.0        0.0  \n",
       "4           0.0              0.0        0.0  "
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   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from IPython.display import display\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Load the Obesity Paradox Dataset\n",
    "data = pd.read_csv('obesity-paradox-dataset.csv')\n",
    "\n",
    "# Labels outcome separation\n",
    "day_in_hospital = data['Hospital_Stay_Days']\n",
    "data = data.drop('Hospital_Stay_Days', axis = 1)\n",
    "\n",
    "\n",
    "\n",
    "# Drop the patient ID\n",
    "data = data.drop('Patient_ID', axis = 1)\n",
    "features = data.copy()\n",
    "\n",
    "# Data set first 5 rows preview before encoding\n",
    "display(data.head())\n",
    "\n",
    "# Patient_Gender encoder\n",
    "patient_gender_encoded = pd.get_dummies(data['Patient_Gender'])\n",
    "data = (pd.concat([data,patient_gender_encoded], axis = 1)).drop('Patient_Gender', axis = 1)\n",
    "\n",
    "# Patient medical coditions encoding.\n",
    "patient_diagnostic_code = data['Patient_Diagnostic_Icd9']\n",
    "data = data.rename(columns = {'Patient_Diagnostic_Icd9':'Icd9_Code'})\n",
    "\n",
    "# Icd9 to lables\n",
    "# icd9Codes module contains a list of the deseases that will study and\n",
    "# a function that converts the icd9 codes to its names.\n",
    "import icd9Codes\n",
    "patient_diagnostic_code\n",
    "# Merge patient_diagnostic_code back and drop patient_diagnostic_icd9 column\n",
    "data = (pd.concat([data,patient_diagnostic_code.apply(icd9Codes.assign_label)], axis = 1)).drop('Icd9_Code', axis = 1)\n",
    "\n",
    "# I have to get rid of the NaN ICD9 codes which does not belogns to the list\n",
    "# of medical conditions we are studing. Before doing that I have to merge the label back to data\n",
    "# to keep records consistency.\n",
    "data = pd.concat([data,day_in_hospital], axis = 1)\n",
    "data = data.dropna().reset_index(drop = True)\n",
    "\n",
    "\n",
    "day_in_hospital = data['Hospital_Stay_Days']\n",
    "data = data.drop('Hospital_Stay_Days', axis = 1)\n",
    "\n",
    "# Finally we encode the Patient_Diagnosctic_Icd9 column\n",
    "patient_diagnostic_encoded = pd.get_dummies(data['Patient_Diagnostic_Icd9'])\n",
    "data = (pd.concat([data,patient_diagnostic_encoded], axis = 1)).drop('Patient_Diagnostic_Icd9', axis = 1)\n",
    "\n",
    "display(data.head())\n",
    "\n",
    "\n"
   ]
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   "source": [
    "## Data Exploration\n",
    "After applying most of the transformation, I will explore the data by calculating its minimum, maximum, mean, median, and standard deviation. I will also explore the correlation between the BMI and the hospitalization time.\n",
    "\n",
    "I will perform the following steps.\n",
    "- Calculate statistics metrics\n",
    "- Plot the correlation between the continues features and the label.\n",
    "- Present the conclusion of my observations."
   ]
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     "output_type": "stream",
     "text": [
      "Statistics for Obesity Paradox dataset:\n",
      "\n",
      "Minimum time in days: 0.00\n",
      "Maximum time in days: 88.00\n",
      "Mean time: 8.16\n",
      "Median time 6.00\n",
      "Standard deviation of time: 6.91\n"
     ]
    },
    {
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ndK5Fiy7MAw88tP9cixYtyK5d9zU3LAAAAJNuMm6pG32xOUl+oNZ6Z631qiTz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116acc4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S9PX16YMCAADOClY4AWeNiy66KE8/fV6Sx5JckuQ/J7kkF174/Tz11FON3WfV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\n8+c+97mDrtHT05Ndu3aNu86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JkiRJ3Zuh4ZKUsKeB4tXV1YnxV6fHV1dXs2TJkkTfaODP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\nmDVrFqNHj87ZNxp7wgkncPXVV7Ns2TKWLVuWfq6Oq6urY9a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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f61dbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Minimum hospitalization time\n",
    "minimum_days = np.amin(day_in_hospital)\n",
    "\n",
    "# Maximum hospitalization_time\n",
    "maximum_days = np.amax(day_in_hospital)\n",
    "\n",
    "# Mean hospitalization time\n",
    "mean_days = np.mean(day_in_hospital)\n",
    "\n",
    "# Median hospitalization time\n",
    "median_days = np.median(day_in_hospital)\n",
    "\n",
    "# Standard deviation of hospitalization time\n",
    "std_days = np.std(day_in_hospital)\n",
    "\n",
    "# Show the calculated statistics\n",
    "print \"Statistics for Obesity Paradox dataset:\\n\"\n",
    "print \"Minimum time in days: {:.2f}\".format(minimum_days)\n",
    "print \"Maximum time in days: {:.2f}\".format(maximum_days)\n",
    "print \"Mean time: {:.2f}\".format(mean_days)\n",
    "print \"Median time {:.2f}\".format(median_days)\n",
    "print \"Standard deviation of time: {:.2f}\".format(std_days)\n",
    "\n",
    "# Plot the correlations\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# Drop categorical features\n",
    "features = data.drop(['F','M','cardiovascular_disorders','diabetes','hypertension','lipid_disorders','neoplasia'], axis = 1)\n",
    "\n",
    "\n",
    "# See the correlation between the remaining features\n",
    "for col in features:\n",
    "    fig,ax = plt.subplots(figsize=(20,10))\n",
    "    fit = np.polyfit(features[col], day_in_hospital, deg=1)\n",
    "    ax.scatter(features[col], day_in_hospital)\n",
    "    plt.plot(features[col], day_in_hospital, 'o', markersize=0.1, color='black')\n",
    "    ax.plot(features[col], fit[0] * features[col] + fit[1], color='blue', linewidth=0.5)\n",
    "    plt.title('DAYS vs '+ str(col))\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('DAYS')\n",
    "    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Observation conclusions\n",
    "As a reminder, we are using four features: `'Patient_Age'`,`'Patient_Gender'`, `'BMI'` and `'Medical_Condition'`. For each data point (hospital first visit):\n",
    "- `'Patient_Age'` Is the patient age in years at the moment of its first admission.\n",
    "- `'Patient_Gender'` Is the patient gender.\n",
    "- `'BMI'` BMI is an estimate of body fat and a good measure of your patients' risk for diseases that can occur with overweight and obesity. For adults, a healthy weight is defined as the appropriate body weight in relation to height. This ratio of weight to height is known as the body mass index (BMI). People who are overweight (BMI of 25–29.9) have too much body weight for their height. People who are obese (BMI of 30 or above) almost always have a large amount of body fat in relation to their height (https://www.nhlbi.nih.gov/health/educational/lose_wt/bmitools.htm).\n",
    "- `'Medical Condition'` The medical conditions diagnosed at the moment of the first admission.\n",
    "\n",
    "From the data exploration exercise below, I can determine the following.\n",
    "- The dataset label, \"days_in_hospital\" this means that most of its values are very close to each other. Most of the stays days are contained in one standard deviation (2 to 14 days). There are some data points further from the cluster, but they are not too many; this could be an indication of BIAS produce by the fact that I selected only the first visit to the hospital. In average, a person stays eight days in the hospital the first visit no matter the condition or the BMI. The predictor, if performs well should produce values between two and fourteen days (one standard deviation).\n",
    "- A negative correlation exists between the age and the hospital staying; this makes sense because the older a person is, the slower is the recovery.\n",
    "- It doesn't seem to be a high degree correlation between the BMI and the hospital stay; that said you can see that BMIs between 0 and 50 stays between 0 a 20 days. As per the BMI index description, most of the data points accounts for people that are overweight or obese. \n",
    "- I also see outliers; these will be removed further than this analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Metric Function\n",
    "\n",
    "To measure our model’s accuracy, we will use the R2 or the coefficient of determination. This metric tells us how much our prediction is explained by the vector features. The highest good value for R2 is 1.0, and the worst can be negative. A model that predicts correctly not matter what features are input will always give an R2 of 0; this is a clear indication of an overfitted model. The Mathematically operation to calculate R2 is as follow: the square difference between the real label value and the predicted value,’ divided by the square variance of the original label’s distribution minus 1. I am dividing the actual error of my prediction between the spread of the values of the fitted data distribution, in and an ideal scenario where the prediction is close to the real value the different between one and the division is close to 1. R2 metric help to tune the model parameters so this error gets lower, and the R2 gets higher. The mathematical representation of the R2 is:\n",
    "\n",
    "R (y, yˆ) = 1 − Σ(yi, yi) /Σ(yi, y ̄)\n",
    "\n",
    "I will perform the following steps:\n",
    "\n",
    "- Create a fuction that will be used as score function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: Import 'r2_score'\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def performance_metric(y_true, y_predict):\n",
    "    \"\"\" Calculates and returns the performance score between \n",
    "        true and predicted values based on the metric chosen. \"\"\"\n",
    "    \n",
    "    # TODO: Calculate the performance score between 'y_true' and 'y_predict'\n",
    "    score = r2_score(y_true,y_predict)\n",
    "    \n",
    "    # Return the score\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning The Data\n",
    "After applying the changes to explore our data we still have approximately 5000 records.  My next step is to get rid of the outliers. During the data exploration, we plotted the data points. Is clear that are outliers that need to be removed. I will use the quartile strategy to get rid of the outliers.\n",
    "\n",
    "I will perform the following steps.\n",
    "\n",
    "- Detect outliers and get read of them using the quartiles of the data set and getting rid of the 1st and 3rd quartile.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Hospital_Stay_Days Q1 is: 5.0\n",
      "The Hospital_Stay_Days Q3 is: 9.0\n",
      "Data points considered outliers for the feature 'Hospital_Stay_Days':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hospital_Stay_Days</th>\n",
       "      <th>Patient_Age</th>\n",
       "      <th>BIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>18</td>\n",
       "      <td>78</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>16</td>\n",
       "      <td>49</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>16</td>\n",
       "      <td>49</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>16</td>\n",
       "      <td>49</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>16</td>\n",
       "      <td>65</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>16</td>\n",
       "      <td>65</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>16</td>\n",
       "      <td>65</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>16</td>\n",
       "      <td>65</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>21</td>\n",
       "      <td>58</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>21</td>\n",
       "      <td>58</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>36</td>\n",
       "      <td>74</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4642</th>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4643</th>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4644</th>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4764</th>\n",
       "      <td>36</td>\n",
       "      <td>73</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4765</th>\n",
       "      <td>36</td>\n",
       "      <td>73</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4766</th>\n",
       "      <td>36</td>\n",
       "      <td>73</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4767</th>\n",
       "      <td>36</td>\n",
       "      <td>73</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4873</th>\n",
       "      <td>18</td>\n",
       "      <td>79</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4874</th>\n",
       "      <td>18</td>\n",
       "      <td>79</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4875</th>\n",
       "      <td>18</td>\n",
       "      <td>79</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4876</th>\n",
       "      <td>18</td>\n",
       "      <td>79</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4930</th>\n",
       "      <td>16</td>\n",
       "      <td>59</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4931</th>\n",
       "      <td>16</td>\n",
       "      <td>59</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5012</th>\n",
       "      <td>21</td>\n",
       "      <td>66</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>19</td>\n",
       "      <td>48</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094</th>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5095</th>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5096</th>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5145</th>\n",
       "      <td>26</td>\n",
       "      <td>63</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5146</th>\n",
       "      <td>26</td>\n",
       "      <td>63</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5147</th>\n",
       "      <td>26</td>\n",
       "      <td>63</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5148</th>\n",
       "      <td>26</td>\n",
       "      <td>63</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5149</th>\n",
       "      <td>26</td>\n",
       "      <td>63</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5236</th>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237</th>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5281</th>\n",
       "      <td>21</td>\n",
       "      <td>74</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5282</th>\n",
       "      <td>21</td>\n",
       "      <td>74</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5283</th>\n",
       "      <td>21</td>\n",
       "      <td>74</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5284</th>\n",
       "      <td>21</td>\n",
       "      <td>74</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5327</th>\n",
       "      <td>17</td>\n",
       "      <td>53</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>445 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hospital_Stay_Days  Patient_Age   BIM\n",
       "21                    18           78  27.0\n",
       "22                    18           78  27.0\n",
       "23                    18           78  27.0\n",
       "24                    18           78  27.0\n",
       "25                    18           78  27.0\n",
       "26                    18           78  27.0\n",
       "27                    18           78  27.0\n",
       "28                    17           40  34.0\n",
       "81                    16           49  27.0\n",
       "82                    16           49  27.0\n",
       "83                    16           49  27.0\n",
       "85                    16           65  26.0\n",
       "86                    16           65  26.0\n",
       "87                    16           65  26.0\n",
       "88                    16           65  26.0\n",
       "217                   21           58  29.0\n",
       "218                   21           58  29.0\n",
       "313                   36           74  23.0\n",
       "314                   36           74  23.0\n",
       "315                   36           74  23.0\n",
       "316                   36           74  23.0\n",
       "317                   36           74  23.0\n",
       "318                   36           74  23.0\n",
       "319                   36           74  23.0\n",
       "320                   36           74  23.0\n",
       "321                   36           74  23.0\n",
       "322                   36           74  23.0\n",
       "367                   25           38  34.0\n",
       "368                   25           38  34.0\n",
       "369                   25           38  34.0\n",
       "...                  ...          ...   ...\n",
       "4642                  31           65  33.0\n",
       "4643                  31           65  33.0\n",
       "4644                  31           65  33.0\n",
       "4764                  36           73  30.0\n",
       "4765                  36           73  30.0\n",
       "4766                  36           73  30.0\n",
       "4767                  36           73  30.0\n",
       "4873                  18           79  19.0\n",
       "4874                  18           79  19.0\n",
       "4875                  18           79  19.0\n",
       "4876                  18           79  19.0\n",
       "4930                  16           59  26.0\n",
       "4931                  16           59  26.0\n",
       "5012                  21           66  17.0\n",
       "5041                  19           48  26.0\n",
       "5094                  38           65  23.0\n",
       "5095                  38           65  23.0\n",
       "5096                  38           65  23.0\n",
       "5145                  26           63  31.0\n",
       "5146                  26           63  31.0\n",
       "5147                  26           63  31.0\n",
       "5148                  26           63  31.0\n",
       "5149                  26           63  31.0\n",
       "5236                  29           61  33.0\n",
       "5237                  29           61  33.0\n",
       "5281                  21           74  24.0\n",
       "5282                  21           74  24.0\n",
       "5283                  21           74  24.0\n",
       "5284                  21           74  24.0\n",
       "5327                  17           53  31.0\n",
       "\n",
       "[445 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Patient_Age Q1 is: 60.0\n",
      "The Patient_Age Q3 is: 76.0\n",
      "Data points considered outliers for the feature 'Patient_Age':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hospital_Stay_Days</th>\n",
       "      <th>Patient_Age</th>\n",
       "      <th>BIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>7</td>\n",
       "      <td>306</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1376</th>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1420</th>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1421</th>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1561</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1562</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1575</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1786</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2038</th>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039</th>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3056</th>\n",
       "      <td>6</td>\n",
       "      <td>309</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3057</th>\n",
       "      <td>6</td>\n",
       "      <td>309</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3162</th>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3229</th>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3680</th>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3681</th>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3682</th>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3784</th>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3785</th>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3786</th>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3875</th>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4084</th>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4085</th>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4267</th>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4474</th>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4545</th>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4546</th>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4609</th>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4900</th>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4914</th>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5331</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5332</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5333</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5334</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5335</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5336</th>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hospital_Stay_Days  Patient_Age   BIM\n",
       "31                     7           33  35.0\n",
       "66                     7           35  23.0\n",
       "67                     7           35  23.0\n",
       "68                     7           35  23.0\n",
       "866                    4           23  25.0\n",
       "867                    4           23  25.0\n",
       "868                    4           23  25.0\n",
       "1242                   7          306  20.0\n",
       "1277                   2           24  24.0\n",
       "1376                   4           25  33.0\n",
       "1377                   4           25  33.0\n",
       "1378                   4           25  33.0\n",
       "1420                   7           35  38.0\n",
       "1421                   7           35  38.0\n",
       "1489                   6           31  28.0\n",
       "1490                   6           31  28.0\n",
       "1561                  15           34  20.0\n",
       "1562                  15           34  20.0\n",
       "1563                  15           34  20.0\n",
       "1575                   4           35  50.0\n",
       "1786                   4           35  27.0\n",
       "1992                   5           20  29.0\n",
       "1993                   5           20  29.0\n",
       "1994                   5           20  29.0\n",
       "2001                   8           32  38.0\n",
       "2002                   8           32  38.0\n",
       "2003                   8           32  38.0\n",
       "2004                   8           32  38.0\n",
       "2038                   7           28  24.0\n",
       "2039                   7           28  24.0\n",
       "3056                   6          309  23.0\n",
       "3057                   6          309  23.0\n",
       "3162                   4           32  42.0\n",
       "3229                   7           29  28.0\n",
       "3680                  12           34  25.0\n",
       "3681                  12           34  25.0\n",
       "3682                  12           34  25.0\n",
       "3683                  12           34  25.0\n",
       "3784                  13           24  63.0\n",
       "3785                  13           24  63.0\n",
       "3786                  13           24  63.0\n",
       "3875                   3           30  30.0\n",
       "4084                   5           23  25.0\n",
       "4085                   4           34  40.0\n",
       "4267                   3           34  25.0\n",
       "4474                  24           32  28.0\n",
       "4545                   7           21  26.0\n",
       "4546                   7           21  26.0\n",
       "4609                   4           34  33.0\n",
       "4900                   5           22  27.0\n",
       "4914                   8           34  41.0\n",
       "5331                  15           34  20.0\n",
       "5332                  15           34  20.0\n",
       "5333                  15           34  20.0\n",
       "5334                  15           34  20.0\n",
       "5335                  15           34  20.0\n",
       "5336                  15           34  20.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The BIM Q1 is: 25.0\n",
      "The BIM Q3 is: 32.0\n",
      "Data points considered outliers for the feature 'BIM':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hospital_Stay_Days</th>\n",
       "      <th>Patient_Age</th>\n",
       "      <th>BIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>7</td>\n",
       "      <td>71</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>7</td>\n",
       "      <td>71</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>7</td>\n",
       "      <td>71</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>7</td>\n",
       "      <td>71</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4564</th>\n",
       "      <td>14</td>\n",
       "      <td>74</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565</th>\n",
       "      <td>14</td>\n",
       "      <td>74</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4768</th>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4769</th>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4770</th>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4771</th>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4772</th>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4773</th>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4774</th>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4830</th>\n",
       "      <td>9</td>\n",
       "      <td>57</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5016</th>\n",
       "      <td>7</td>\n",
       "      <td>61</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5017</th>\n",
       "      <td>7</td>\n",
       "      <td>61</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5018</th>\n",
       "      <td>7</td>\n",
       "      <td>61</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5056</th>\n",
       "      <td>12</td>\n",
       "      <td>58</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5057</th>\n",
       "      <td>12</td>\n",
       "      <td>58</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5058</th>\n",
       "      <td>12</td>\n",
       "      <td>58</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5059</th>\n",
       "      <td>12</td>\n",
       "      <td>58</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5129</th>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5130</th>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5131</th>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5174</th>\n",
       "      <td>5</td>\n",
       "      <td>59</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5175</th>\n",
       "      <td>5</td>\n",
       "      <td>59</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5181</th>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5182</th>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5183</th>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5285</th>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5286</th>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5287</th>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5288</th>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5289</th>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>227 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hospital_Stay_Days  Patient_Age    BIM\n",
       "7                      3           49   44.0\n",
       "8                      7           65   49.0\n",
       "9                      7           65   49.0\n",
       "10                     7           65   49.0\n",
       "11                     7           65   49.0\n",
       "117                    7           57   44.0\n",
       "118                    7           57   44.0\n",
       "119                    7           57   44.0\n",
       "120                    7           57   44.0\n",
       "121                    7           57   44.0\n",
       "122                    7           57   44.0\n",
       "123                    7           57   44.0\n",
       "124                    7           57   44.0\n",
       "140                    6           85    4.0\n",
       "141                    6           85    4.0\n",
       "277                   11           42   43.0\n",
       "278                   11           42   43.0\n",
       "484                    5           57   43.0\n",
       "485                    5           57   43.0\n",
       "486                    5           57   43.0\n",
       "487                    5           57   43.0\n",
       "610                    7           40   46.0\n",
       "611                    7           40   46.0\n",
       "810                    1           48  207.0\n",
       "915                    7           71   48.0\n",
       "916                    7           71   48.0\n",
       "917                    7           71   48.0\n",
       "918                    7           71   48.0\n",
       "941                    7           66   45.0\n",
       "942                    7           66   45.0\n",
       "...                  ...          ...    ...\n",
       "4564                  14           74   64.0\n",
       "4565                  14           74   64.0\n",
       "4768                   4           68   11.0\n",
       "4769                   4           68   11.0\n",
       "4770                   4           68   11.0\n",
       "4771                   4           68   11.0\n",
       "4772                   4           68   11.0\n",
       "4773                   4           68   11.0\n",
       "4774                   4           68   11.0\n",
       "4830                   9           57   46.0\n",
       "5016                   7           61   43.0\n",
       "5017                   7           61   43.0\n",
       "5018                   7           61   43.0\n",
       "5056                  12           58   49.0\n",
       "5057                  12           58   49.0\n",
       "5058                  12           58   49.0\n",
       "5059                  12           58   49.0\n",
       "5129                   4           61   57.0\n",
       "5130                   4           61   57.0\n",
       "5131                   4           61   57.0\n",
       "5174                   5           59   64.0\n",
       "5175                   5           59   64.0\n",
       "5181                  10           48   43.0\n",
       "5182                  10           48   43.0\n",
       "5183                  10           48   43.0\n",
       "5285                   9           43   43.0\n",
       "5286                   9           43   43.0\n",
       "5287                   9           43   43.0\n",
       "5288                   9           43   43.0\n",
       "5289                   9           43   43.0\n",
       "\n",
       "[227 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Following records are outliers for more than one feature: [2816, 2817, 2818, 2062, 1575, 1203, 4288, 4289, 4290, 3784, 3785, 3786, 987, 988, 4459, 4461, 4463, 4465, 4467, 4469, 4474, 2815]\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hospital_Stay_Days</th>\n",
       "      <th>Patient_Age</th>\n",
       "      <th>BIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2062</th>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2062</th>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "      <td>54.0</td>\n",
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       "    <tr>\n",
       "      <th>4463</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>4463</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4465</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4465</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4288</th>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4288</th>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4289</th>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4289</th>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4290</th>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4290</th>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4474</th>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4474</th>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4459</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4459</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4461</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4461</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4467</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4467</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4469</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4469</th>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2815</th>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2815</th>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2816</th>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2816</th>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2817</th>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2817</th>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>30</td>\n",
       "      <td>57</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>34</td>\n",
       "      <td>59</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>34</td>\n",
       "      <td>59</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>34</td>\n",
       "      <td>59</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>34</td>\n",
       "      <td>59</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1575</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1575</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3784</th>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3784</th>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3785</th>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3785</th>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3786</th>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>63.0</td>\n",
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       "    <tr>\n",
       "      <th>3786</th>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hospital_Stay_Days  Patient_Age   BIM\n",
       "2062                  30           51  54.0\n",
       "2062                  30           51  54.0\n",
       "4463                  28           54   9.0\n",
       "4463                  28           54   9.0\n",
       "4465                  28           54   9.0\n",
       "4465                  28           54   9.0\n",
       "4288                  18           71  43.0\n",
       "4288                  18           71  43.0\n",
       "4289                  18           71  43.0\n",
       "4289                  18           71  43.0\n",
       "4290                  18           71  43.0\n",
       "4290                  18           71  43.0\n",
       "4474                  24           32  28.0\n",
       "4474                  24           32  28.0\n",
       "4459                  28           54   9.0\n",
       "4459                  28           54   9.0\n",
       "4461                  28           54   9.0\n",
       "4461                  28           54   9.0\n",
       "4467                  28           54   9.0\n",
       "4467                  28           54   9.0\n",
       "4469                  28           54   9.0\n",
       "4469                  28           54   9.0\n",
       "2815                  30           57  44.0\n",
       "2815                  30           57  44.0\n",
       "2816                  30           57  44.0\n",
       "2816                  30           57  44.0\n",
       "2817                  30           57  44.0\n",
       "2817                  30           57  44.0\n",
       "2818                  30           57  44.0\n",
       "2818                  30           57  44.0\n",
       "987                   34           59  51.0\n",
       "987                   34           59  51.0\n",
       "988                   34           59  51.0\n",
       "988                   34           59  51.0\n",
       "1203                  25           50  44.0\n",
       "1203                  25           50  44.0\n",
       "1575                   4           35  50.0\n",
       "1575                   4           35  50.0\n",
       "3784                  13           24  63.0\n",
       "3784                  13           24  63.0\n",
       "3785                  13           24  63.0\n",
       "3785                  13           24  63.0\n",
       "3786                  13           24  63.0\n",
       "3786                  13           24  63.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hospital_Stay_Days  Patient_Age   BIM    F    M  cardiovascular_disorders  \\\n",
      "0                   6           71  29.0  0.0  1.0                       0.0   \n",
      "1                   6           71  29.0  0.0  1.0                       1.0   \n",
      "2                   4           68  30.0  0.0  1.0                       0.0   \n",
      "3                   4           68  30.0  0.0  1.0                       0.0   \n",
      "4                   4           68  30.0  0.0  1.0                       0.0   \n",
      "\n",
      "   diabetes  hypertension  lipid_disorders  neoplasia  \n",
      "0       0.0           1.0              0.0        0.0  \n",
      "1       0.0           0.0              0.0        0.0  \n",
      "2       0.0           1.0              0.0        0.0  \n",
      "3       0.0           0.0              1.0        0.0  \n",
      "4       1.0           0.0              0.0        0.0  \n"
     ]
    }
   ],
   "source": [
    "# We first join the label back to features and reset the index.\n",
    "data_with_outliers = pd.concat([day_in_hospital,data], axis = 1)\n",
    "data_with_outliers.reset_index(drop = True)\n",
    "\n",
    "# We drop the discrete features.\n",
    "data_with_outliers = data_with_outliers.drop(data_with_outliers.columns[3:10], axis = 1)\n",
    "\n",
    "\n",
    "# Let us detect the outliers\n",
    "index_list = []\n",
    "for col in data_with_outliers.keys():\n",
    "    # Calculate 1st Quartile\n",
    "    Q1 = np.percentile(data_with_outliers[col],25)\n",
    "    print \"The {} Q1 is: {}\".format(col,Q1)\n",
    "    # Calculate 3rd Quartile\n",
    "    Q3 = np.percentile(data_with_outliers[col],75)\n",
    "    print \"The {} Q3 is: {}\".format(col,Q3)\n",
    "    # Any index that is greater than or lesser than 1.5 times the difference between the value of the Q3 minus\n",
    "    # Q1 range is considered an outlier.\n",
    "    step = 1.5*(Q3 - Q1)\n",
    "    print \"Data points considered outliers for the feature '{}':\".format(col)\n",
    "    display(data_with_outliers[~((data_with_outliers[col] >= Q1 - step) & (data_with_outliers[col] <= Q3 + step))])\n",
    "    # Capture outliers index in a list\n",
    "    index_list.append(data_with_outliers[~((data_with_outliers[col] >= Q1 - step) & (data_with_outliers[col] <= Q3 + step))].index.tolist())\n",
    "\n",
    "# Let us see which records contains more than one outlier.\n",
    "outliers  = [y for x in index_list for y in x]\n",
    "repeated_outliers=[]\n",
    "from collections import Counter\n",
    "cnt = Counter(outliers)\n",
    "for el in cnt.elements():\n",
    "    if cnt[el] > 1:\n",
    "        repeated_outliers.append(el)\n",
    "print \"Following records are outliers for more than one feature:\", list(set(repeated_outliers))\n",
    "display(data_with_outliers.loc[repeated_outliers])\n",
    "\n",
    "\n",
    "# Let us remove the outliers\n",
    "# First let us add the categorical columns\n",
    "temp_data = data.drop(data.columns[0:2], axis = 1)\n",
    "data_with_outliers = pd.concat([data_with_outliers,temp_data], axis = 1)\n",
    "good_data = data_with_outliers.drop(data_with_outliers.index[repeated_outliers]).reset_index(drop = True)\n",
    "\n",
    "\n",
    "# RESULT GOOD_DATA\n",
    "print good_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting the Training Sample\n",
    "Now that my dataset is clean, it is time to sample the data that I will use to train the model. I can just select an even number of records making sure that the dataset contains:\n",
    "- An even number of Male and Females.\n",
    "- An even number of each type of medical conditions.\n",
    "\n",
    "Sample my data this way won't be enough, so I will go even further to use a soft clustering model to identify groups using the hospitalization time, age and BMI.\n",
    "I will use a Gaussian Mixture soft cluster instead of a hard cluster, the data variance is small, so my data points are too close to each other to create clear boundaries.\n",
    "I will use the cluster's silhouette as a metric to determine the number of clusters.\n",
    "\n",
    "I will perform the following steps:\n",
    "\n",
    "- Sample some data of the data set.\n",
    "- Make sure the samples don't be similar but ranking them using the dataset percentile.\n",
    "- Scale and review the data correlation and distribution.\n",
    "- Reduce the continues data to two dimensions to see if our samples below to multiple classes.\n",
    "- Conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Samples\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hospital_Stay_Days</th>\n",
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      "text/plain": [
       "   Hospital_Stay_Days  Patient_Age   BIM\n",
       "0                   5           78  28.0\n",
       "1                  10           63  31.0\n",
       "2                   5           48  27.0\n",
       "3                   7           74  33.0\n",
       "4                   5           56  27.0"
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     },
     "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "Sample percentile ranking\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hospital_Stay_Days</th>\n",
       "      <th>Patient_Age</th>\n",
       "      <th>BIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>32.5</td>\n",
       "      <td>79.0</td>\n",
       "      <td>51.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>79.8</td>\n",
       "      <td>34.1</td>\n",
       "      <td>71.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3134</th>\n",
       "      <td>32.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>59.5</td>\n",
       "      <td>68.9</td>\n",
       "      <td>79.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>32.5</td>\n",
       "      <td>16.4</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hospital_Stay_Days  Patient_Age   BIM\n",
       "1650                32.5         79.0  51.2\n",
       "166                 79.8         34.1  71.8\n",
       "3134                32.5          5.3  42.9\n",
       "2489                59.5         68.9  79.8\n",
       "2650                32.5         16.4  42.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of element cluster 0: 4007 Number of Elements Cluster 1: 1318\n",
      "[[-0.14512061  0.01090803]\n",
      " [ 0.26058315 -0.0195868 ]]\n"
     ]
    },
    {
     "data": {
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r+0puq8A6hBB/AxIKQggNCQUhhIaEghBCQ0JBCKEhoSCE0JBQEEJoSCgIITQk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\n/h+suloxqRU7QQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120be5350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3iTd3t/Otv72EStn3I8S888Szr6JJKyXsbuYbd555idroF528TMtJX3AAXnzz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\nNX1HxssQnRBCCHFhk+RHCCFmUDSRJZsvUuOoOKvXazVqljU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fvp0nn3ySDz74gF/+8pdk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\nkcw3HQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120cb8f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VjG8+orJ1UFPyLj3N5RQdhbggO5/YczVFFSXEBdnpqj3uyTpHOPtoKj2IeqB9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qpaATnpeIxU2I/POUYilUBuqmfY4UFlLCeRmiuU9E70pU1oS+qIqsqYHLyGfegsiVQMaTrK4NXX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\n8U32MCmV4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lDBhDdKS0MFg+MzqLRFSGUS4oEZZCotNa3Hsd14nWA0ijoWI+icxKsyEHJN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\nKjbBVCReteoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12131fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get data and drop the discret outputs to calculate the percentil ranks. This wiil allow me to\n",
    "# see the similarity between the sample data points I selected in the previous section.\n",
    "# I will choose five samples around the middle of the dataset\n",
    "indices = [1650,166,3134,2489,2650 ]\n",
    "# I will create a dataframe with these samples.\n",
    "samples = (pd.DataFrame(good_data.loc[indices], columns = good_data.keys()).reset_index(drop=True)).drop(['F','M','cardiovascular_disorders','diabetes','hypertension','lipid_disorders','neoplasia'],axis = 1)                       \n",
    "# I will create the ranking of the sample in relations to the dataset\n",
    "print \"Selected Samples\"\n",
    "display(samples)\n",
    "pcts = 100. * (good_data.drop(['F','M','cardiovascular_disorders','diabetes','hypertension','lipid_disorders','neoplasia'],axis = 1)).rank(axis=0, pct=True).iloc[indices].round(decimals=3)\n",
    "print \"Sample percentile ranking\"\n",
    "display(pcts)                                                                               \n",
    "                                                                                \n",
    "# Let us visualize the result to see how different each on is respect to the other.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.heatmap(pcts, annot=True, linewidth=.1, vmax=99, fmt='.1f', cmap='YlOrRd', square=True, cbar=False)\n",
    "plt.yticks([4.5,3.5,2.5,1.5,.5], ['Index '+str(x) for x in indices], rotation='horizontal')\n",
    "plt.xticks(rotation=45, ha='center')\n",
    "plt.title('Percentile ranks of\\nsamples\\' category spending');\n",
    "\n",
    "\n",
    "# Let us review the correlation and data distrbution of the points now that they are scale.\n",
    "temp_data_ii = good_data.drop(['F','M','cardiovascular_disorders','diabetes','hypertension','lipid_disorders','neoplasia'],axis = 1)\n",
    "pd.scatter_matrix(temp_data_ii, alpha = 0.3, figsize = (14,8), diagonal = 'kde');\n",
    "# Scale data\n",
    "temp_data_ii_scaled = np.log(temp_data_ii)\n",
    "# Drop index with infinite value\n",
    "temp_data_ii_scaled = temp_data_ii_scaled.drop(good_data.index[2240])\n",
    "# Plot correlation and data distribution data scaled\n",
    "pd.scatter_matrix(temp_data_ii_scaled, alpha = 0.3, figsize = (14,8), diagonal = 'kde');\n",
    "\n",
    "\n",
    "\n",
    "# Let apply a cluster algorithm to see how the continus data can be grouped. But first we will reduce the data\n",
    "# to 2 dimensions to help us vizualize the resulting cluster and tune it\n",
    "\n",
    "# Data reduction to two dimensions\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca = pca.fit(temp_data_ii_scaled)\n",
    "reduced_data = pca.transform(temp_data_ii_scaled)\n",
    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])\n",
    "pca_samples = pca.transform(np.log(samples))\n",
    "\n",
    "\n",
    "# Apply the clustering algorithm\n",
    "from sklearn import mixture\n",
    "clusterer = mixture.GMM(n_components=2)\n",
    "clusterer.fit(reduced_data)\n",
    "\n",
    "# Predict the cluster for each data point\n",
    "preds = clusterer.predict(reduced_data)\n",
    "centers = clusterer.means_\n",
    "# Predic the sample points\n",
    "sample_preds = clusterer.predict(pca_samples)\n",
    "print \"Number of element cluster 0: {} Number of Elements Cluster 1: {}\".format(len(reduced_data) - np.sum(preds), np.sum(preds))\n",
    "print centers\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion of applying the clustering\n",
    "\n",
    "I sampled the original dataset to use the samples later to describe the clusters. I compared the samples with each other using ranking to make sure my sample weren't similar to each other; its similarity can be seen using the heatmap above. Follow the result:\n",
    "\n",
    "- Patients (2489, 166) are within the highest BMI in the dataset \n",
    "- Patients (2489, 1650) are the oldest patients in the dataset\n",
    "- Patients (2489, 166) are within the longest stay in the dataset\n",
    "\n",
    "When I see the features data distribution, I can tell that the variance of the age and BMI are small which can cause a high BIAS error in the training models.\n",
    "\n",
    "Next, I will see in which cluster the sample points belongs too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Silhouette Score: 0.463118163922\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hospital_Stay_Days</th>\n",
       "      <th>Patient_Age</th>\n",
       "      <th>BIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Patient 0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient 1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hospital_Stay_Days  Patient_Age   BIM\n",
       "Patient 0                 6.0         67.0  28.0\n",
       "Patient 1                 9.0         66.0  29.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Hospital_Stay_Days</th>\n",
       "      <th>Patient_Age</th>\n",
       "      <th>BIM</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>cardiovascular_disorders</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>lipid_disorders</th>\n",
       "      <th>neoplasia</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "      Hospital_Stay_Days  Patient_Age   BIM    F    M  \\\n",
       "1650                   5           78  28.0  0.0  1.0   \n",
       "166                   10           63  31.0  0.0  1.0   \n",
       "3134                   5           48  27.0  0.0  1.0   \n",
       "2489                   7           74  33.0  0.0  1.0   \n",
       "2650                   5           56  27.0  0.0  1.0   \n",
       "\n",
       "      cardiovascular_disorders  diabetes  hypertension  lipid_disorders  \\\n",
       "1650                       0.0       0.0           0.0              1.0   \n",
       "166                        0.0       0.0           1.0              0.0   \n",
       "3134                       1.0       0.0           0.0              0.0   \n",
       "2489                       0.0       0.0           0.0              1.0   \n",
       "2650                       1.0       0.0           0.0              0.0   \n",
       "\n",
       "      neoplasia  \n",
       "1650        0.0  \n",
       "166         0.0  \n",
       "3134        0.0  \n",
       "2489        0.0  \n",
       "2650        0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Cluster 0\n",
      "Sample point 1 predicted to be in Cluster 0\n",
      "Sample point 2 predicted to be in Cluster 0\n",
      "Sample point 3 predicted to be in Cluster 0\n",
      "Sample point 4 predicted to be in Cluster 0\n",
      "Number of element cluster 0: 4007 Number of Elements Cluster 1: 1318\n"
     ]
    },
    {
     "data": {
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C8KT9RvyN8Qr8TUHkE94zgflmtgnf/r6rc25H8FT6HuAjM8s1s+OdHyFrFP5J70b8H+gz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\nQBHwwxDCnDLP+wHQN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1221f7890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I used the cluster silohouette score to determine the number of cluster.\n",
    "# I tried sevelar cluster numbers and the best score result was 2 clusters\n",
    "from sklearn import metrics\n",
    "import visuals as vs\n",
    "score = metrics.silhouette_score(reduced_data, preds, metric='euclidean')\n",
    "print \"Best Silhouette Score: {}\".format(score)\n",
    "\n",
    "# Visualize the cluster.\n",
    "vs.cluster_results(reduced_data, preds, centers, pca_samples)\n",
    "\n",
    "# Let us recover the centers to see how they compare to the sample data.\n",
    "log_centers = pca.inverse_transform(centers)\n",
    "true_centers = np.exp(log_centers)\n",
    "# Display the true centers\n",
    "patients = ['Patient {}'.format(i) for i in range(0,len(centers))]\n",
    "true_centers = pd.DataFrame(np.round(true_centers), columns = temp_data_ii.keys())\n",
    "true_centers.index = patients\n",
    "display(true_centers)\n",
    "\n",
    "# Display the samples\n",
    "# full_sample_data = pd.DataFrame(temp_data.loc[indices],columns = temp_data.keys()).reset_index(drop = True)\n",
    "display(pd.DataFrame(good_data.loc[indices], columns = good_data.keys()))\n",
    "# Display the predictions\n",
    "for i, pred in enumerate(sample_preds):\n",
    "    print \"Sample point\", i, \"predicted to be in Cluster\", pred\n",
    "    \n",
    "# Display members of the cluster\n",
    "print \"Number of element cluster 0: {} Number of Elements Cluster 1: {}\".format(len(reduced_data) - np.sum(preds), np.sum(preds))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion of applying the clustering II\n",
    "All the selected samples belong to cluster zero.\n",
    "I got the number of cluster members. \n",
    "I can see that is unbalanced. \n",
    "Cluster \"0\" contains more data points that cluster \"1\". The variance that dominates the data set is the hospital staying days.\n",
    "Because the medical condition (discrete data) is not taking in consideration in this exercise, it is possible that there some medical conditions that account for this classification. \n",
    "I could count the numbers of medical conditions on each cluster to see which medical conditions are more frequent on each cluster, but that wouldn't change the split. I will select an even number of each cluster to balance the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Balancing the  Dataset\n",
    "\n",
    "In this section, I will balance the dataset by getting even samples of each type of clusters and features.\n",
    "\n",
    "I will perform the following steps:\n",
    "\n",
    "- Create a new dataset with the following characteristics:\n",
    "    - Even number of cluster datapoints\n",
    "    - Even number of males and females\n",
    "    - Even number of medical conditions\n",
    "\n",
    "- Create the training data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Unbalanced Dataset'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of F 974\n",
      "Number of M 1662\n",
      "Number of cardiovascular_disorders 1380\n",
      "Number of diabetes 182\n",
      "Number of hypertension 473\n",
      "Number of lipid_disorders 381\n",
      "Number of neoplasia 220\n",
      "Number of cluster_membership 1318\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Training balanced dataset'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of F 441\n",
      "Number of M 469\n",
      "Number of cardiovascular_disorders 182\n",
      "Number of diabetes 182\n",
      "Number of hypertension 182\n",
      "Number of lipid_disorders 182\n",
      "Number of neoplasia 182\n",
      "Number of cluster_membership 495\n",
      "\n",
      "Distribution of patient ages and BMI\n",
      "[[<matplotlib.axes._subplots.AxesSubplot object at 0x121967d10>\n",
      "  <matplotlib.axes._subplots.AxesSubplot object at 0x121c195d0>]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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S1G1m8dPj4hpgQz29ATi/nl6NY52kaZrSMWARsRw4E/hm3XRZRDwYER+LiAV121Jg+4jF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121967390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert the prediction to a Series to join it the the good_data dataframe later.\n",
    "cluster_membership = pd.Series(preds,name = 'cluster_membership')\n",
    "temp_data_iii = pd.concat([good_data,cluster_membership], axis = 1)\n",
    "    \n",
    "# I will  create two even cluster datasets\n",
    "cluster_0 = temp_data_iii.groupby(['cluster_membership']).get_group(0).sample(n = 1318, replace = True, random_state=200)\n",
    "cluster_1 = temp_data_iii.groupby(['cluster_membership']).get_group(1).sample(n = 1318, replace = True, random_state=200)\n",
    "\n",
    "# Join the clusters to get an even sample\n",
    "clusters_dataset = cluster_0.append(cluster_1,ignore_index=True)\n",
    "\n",
    "## Let us calulate the remaining number of categories.\n",
    "display(\"Unbalanced Dataset\")\n",
    "cols_list = ['F','M','cardiovascular_disorders','diabetes','hypertension','lipid_disorders',\n",
    "             'neoplasia','cluster_membership']\n",
    "for cols in cols_list:\n",
    "    print \"Number of {} {}\".format(cols,len(clusters_dataset.groupby([cols]).get_group(1)))\n",
    "    \n",
    "## Dataset is unbalanced, diabetes and neoplasia classes are low and the cardiovasculat is too big.\n",
    "## I will sample an even number of all the categories\n",
    "genders = []\n",
    "genders.append((clusters_dataset.groupby(['M']).get_group(1)).sample(n = 1116, replace = True, random_state=200 ))\n",
    "genders.append((clusters_dataset.groupby(['F']).get_group(1)).sample(n = 1116, replace = True, random_state=200 ))\n",
    "\n",
    "# Now I append the re-sample categories to balance the data set from the genders point of view\n",
    "genders_dataset = genders[0].append(genders[1], ignore_index=True) \n",
    "\n",
    "## Now let use select at least 120 samples of each category\n",
    "categories = []\n",
    "categories.append((genders_dataset.groupby(['cardiovascular_disorders']).get_group(1)).sample(n = 182,\n",
    "                                                                                         replace = True,\n",
    "                                                                                         random_state=200 ))\n",
    "categories.append((genders_dataset.groupby(['hypertension']).get_group(1)).sample(n = 182,\n",
    "                                                                                         replace = True,\n",
    "                                                                                         random_state=200 ))\n",
    "categories.append((genders_dataset.groupby(['lipid_disorders']).get_group(1)).sample(n = 182,\n",
    "                                                                                         replace = True,\n",
    "                                                                                         random_state=200 ))\n",
    "categories.append((genders_dataset.groupby(['diabetes']).get_group(1)).sample(n = 182,\n",
    "                                                                                         replace = True,\n",
    "                                                                                         random_state=200 ))\n",
    "categories.append((genders_dataset.groupby(['neoplasia']).get_group(1)).sample(n = 182,\n",
    "                                                                                         replace = True,\n",
    "                                                                                         random_state=200 ))\n",
    "\n",
    "\n",
    "# Finall training dataset\n",
    "training_dataset = categories[0]\n",
    "categories.pop(0)\n",
    "training_dataset = training_dataset.append(categories, ignore_index = True)\n",
    "\n",
    "\n",
    "cols_list = ['F','M','cardiovascular_disorders','diabetes','hypertension','lipid_disorders'\n",
    "             ,'neoplasia','cluster_membership']\n",
    "display(\"Training balanced dataset\")\n",
    "for cols in cols_list:\n",
    "    print \"Number of {} {}\".format(cols,len(training_dataset.groupby([cols]).get_group(1)))\n",
    "    \n",
    "\n",
    "# We will put the label in a separate series and the features in other\n",
    "training_dataset = training_dataset.sample(frac=1).reset_index(drop=True)\n",
    "training_labels = training_dataset['Hospital_Stay_Days']\n",
    "training_features = training_dataset.drop(['Hospital_Stay_Days','cluster_membership'],axis = 1)\n",
    "\n",
    "# Let visualize the distribution of ages and BMI\n",
    "print \"\"\n",
    "print \"Distribution of patient ages and BMI\"\n",
    "print (training_features.hist(training_features.columns[0:2],figsize = (10,7)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algorithms and Techniques\n",
    "\n",
    "The type of algorithm I selected for this project are of three type, instance-based learning algorithms, regression algorithms, decision tree algorithms and clustering algorithms.\n",
    "To predict a continue's label, it is necessary to select a continues prediction algorithm I choose three of these type:\n",
    "\n",
    "- Linear Regression (chosen for its simplicity)\n",
    "- Decision Tree Regression (chosen after explorer the data and understand its correlation)\n",
    "- Nearest Neighborly (chosen to benchmark the LR and DT)\n",
    "\n",
    "1.  A Linear Regressor based model is the simplest regression algorithm available; its sole parameter is the \n",
    "Normalization parameter. The normalization parameter is not useful as we will proof in the learning curve because we normalized the data way before passing it to the regressor.\n",
    "\n",
    "2. Decision Tree Regressor (DTR), we noticed early in the project that the features will be a mix of continue and categorical data, linear regressors performance when some of the features are discrete is not acceptable, a perfect replacement is the DTR. DTR's models are capable of managing numerical and categorical data at the same time. The sklearn Decision Tree Regressor uses the CART algorithm, which is based on the C4.5 algorithm that in turns is an improvement of the ID3 algorithm. The differents between the ID3 and the C4.5 algorithm is that the C4.5 is capable of use numerical features, it does that by creating a discrete range of numerical features, example patients between 30 and 50 is a range, and a value between then will test true for that range. Finally, the differences between the C4.5 and the CART is that CART does the ranges also for the target which in our case is the days in the hospital (a numeric value). Is because of the previous explanation that this algorithm was selected.\n",
    "For the DT model, the parameter selected for tuning was the tree max_depth. Another possible setting could be minimum samples per leaf, but because of the numerical nature of the target and the fact that it will become a discrete value this parameter when set beyond to 1 the model performs poorly ( it can't be that many ranges).\n",
    "\n",
    "3. Nearest Neighborly, this is an instance base learner because it works by using the existing data a not by calculating its relation like the previous two algorithms.  It is also suitable for a mix of numerical and categorical features and target. It works by querying the existing training data using the features of the target that need to predict; then it measure the distance between the closest features, and finally, it averages the target of those features to calculate the prediction. We selected this algorithm to use it as a benchmark of the DTR and LN. The metric selected was the number of neighbors. Because of the small variance of the data we expect the algorithm to perform better than the other selected algorithms.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine model learning curve\n",
    "Now that the dataset is well balanced, I will proceed to determine the model learning curve. As I stay at the beginning of this exercise, I intended to use a linear regressor, after the initial data exploration I don't expect that this linear regressor performs well. The reason is that the data variance is too low producing a high bias in the model. I will try other two type of regressors decisionTrees and nearestNeigborh to see if I can get better results.\n",
    "\n",
    "I will perform the following steps:\n",
    "\n",
    "- I will create two training test split. One with the balanced data and what with all the data points.\n",
    "- I will create a linearRegressor and plot its learning curve.\n",
    "- I will present the analysis of the training curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training and testing split was successful.\n",
      "Length of the balanced features training split 773, length of the unbalanced features training split 4527\n"
     ]
    },
    {
     "data": {
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AbwIeCdwKfDgzv7KJdd9OUfsOwM3Aq2yTpWowFGmieDbwFGADcB7wUuBLEfEE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1214604d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import visuals as vs\n",
    "import pandas as pd\n",
    "from sklearn import cross_validation\n",
    "import sklearn.learning_curve as curves\n",
    "from sklearn import linear_model\n",
    "\n",
    "from sklearn.cross_validation import ShuffleSplit, train_test_split\n",
    "\n",
    "# Created two sets balanced and unbalanced\n",
    "X_train_b, X_test_b, y_train_b, y_test_b = cross_validation.train_test_split(training_features,training_labels,test_size=0.15,random_state=10)\n",
    "X_train_u, X_test_u, y_train_u, y_test_u = cross_validation.train_test_split(good_data.drop(['Hospital_Stay_Days'],axis = 1),good_data['Hospital_Stay_Days'],test_size=0.15,random_state=10)\n",
    "print \"Training and testing split was successful.\"\n",
    "print \"Length of the balanced features training split {}, length of the unbalanced features training split {}\".format(len(X_train_b),len(X_train_u))\n",
    "\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Produce learning curves for varying training sets using a Linear regressor\n",
    "\n",
    "def ModelLearning(X, y):\n",
    "    \"\"\" Calculates the performance of several models with varying sizes of training data.\n",
    "        The learning and testing scores for each model are then plotted. \"\"\"\n",
    "    \n",
    "    # Create 10 cross-validation sets for training and testing\n",
    "    cv = ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.2, random_state = 0)\n",
    "\n",
    "    # Generate the training set sizes increasing by 50\n",
    "    train_sizes = np.rint(np.linspace(1, X.shape[0]*0.8 - 1, 9)).astype(int)\n",
    "\n",
    "    # Create the figure window\n",
    "    fig = plt.figure(figsize=(10,7))\n",
    "\n",
    "    # Create three different models based on normalization\n",
    "    for k, norm in enumerate([True,False]):\n",
    "        \n",
    "        # Create a linear regressor at normaliztion = norm\n",
    "        regressor = linear_model.LinearRegression(normalize = norm)\n",
    "\n",
    "        # Calculate the training and testing scores\n",
    "        sizes, train_scores, test_scores = curves.learning_curve(regressor, X, y, \\\n",
    "            cv = cv, train_sizes = train_sizes, scoring = 'r2')\n",
    "        \n",
    "        # Find the mean and standard deviation for smoothing\n",
    "        train_std = np.std(train_scores, axis = 1)\n",
    "        train_mean = np.mean(train_scores, axis = 1)\n",
    "        test_std = np.std(test_scores, axis = 1)\n",
    "        test_mean = np.mean(test_scores, axis = 1)\n",
    "\n",
    "        # Subplot the learning curve \n",
    "        ax = fig.add_subplot(2, 2, k+1)\n",
    "        ax.plot(sizes, train_mean, 'o-', color = 'r', label = 'Training Score')\n",
    "        ax.plot(sizes, test_mean, 'o-', color = 'g', label = 'Testing Score')\n",
    "        ax.fill_between(sizes, train_mean - train_std, \\\n",
    "            train_mean + train_std, alpha = 0.15, color = 'r')\n",
    "        ax.fill_between(sizes, test_mean - test_std, \\\n",
    "            test_mean + test_std, alpha = 0.15, color = 'g')\n",
    "        \n",
    "        # Labels\n",
    "        ax.set_title('norm = %s'%(norm))\n",
    "        ax.set_xlabel('Number of Training Points')\n",
    "        ax.set_ylabel('Score')\n",
    "        ax.set_xlim([0, X.shape[0]*0.8])\n",
    "        ax.set_ylim([-0.05, 1.05])\n",
    "    \n",
    "    # Visual aesthetics\n",
    "    ax.legend(bbox_to_anchor=(1.05, 2.05), loc='lower left', borderaxespad = 0.)\n",
    "    fig.suptitle('Linear Regressor Learning Performances', fontsize = 16, y = 1.03)\n",
    "    fig.tight_layout()\n",
    "    fig.show()\n",
    "    \n",
    "\n",
    "ModelLearning(training_features, training_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of the learning curve\n",
    "\n",
    "The linear regressor performed very poorly. No matter the type of hyperparameter (normalization) the model after 300 samples stops learning. To fix this BIAS problem, I need to add new features. Also, the used of the normalization parameter as I stated before is not useful because the data is normalized already."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark Model\n",
    "\n",
    "We will use a supervise learning method to predict the time a patient spend in the hospital. Because our vector label continues we will use a regression predictor, and we will compare its prediction with an information based learner (K-nearest neighbor), this will allows to benchmark one model versus the other. We will use cross-validation to make sure with dont overfit the model and the R2 Coefficient of Determination to measure the accuracy of our model or what is the same how well it explains the label based on the features. Our learner contains some bias introduced during the features selection; this is because we are selecting the medical conditions used in previous obesity paradox studies, selecting only patients that survive a surgery and finally only taking in consideration the first visit to a hospital. The added bias is based on the observable features related to the paradox, and the sampling process. Introducing new features will be only a guess that can create model complexity due to the additional dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construction of the Nearest Neighbor and decisionTreeRegressor\n",
    "\n",
    "The poor performance of the previous regressor is due to the BIAS.\n",
    "I will build two more models, and I will test them using both the balanced and the unbalanced splits.\n",
    "\n",
    "I will perform the following steps:\n",
    "- Build a decisionTree and a NearestNeighborg regressor.\n",
    "- Use grid searches to look for both models best hyperparameters.\n",
    "- Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NN Score using balanced data: 0.234907650606\n",
      "NN Score using ubalanced data: 0.524965821676\n",
      "\n",
      "DT Score using balanced data: 0.0140990266578\n",
      "DT Score using ubalanced data: 0.422960446681\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "\n",
    "# Nearest Neighborg using balanced data\n",
    "neigh_b = KNeighborsRegressor(n_neighbors = 2, leaf_size = 30, p = 2)\n",
    "neigh_b.fit(X_train_b,y_train_b)\n",
    "y_predict_NN_b = neigh_b.predict(X_test_b)\n",
    "print \"NN Score using balanced data: {}\".format(performance_metric(y_test_b,y_predict_NN_b))\n",
    "\n",
    "# Nearest Neighborg using unbalanced data\n",
    "neigh_u = KNeighborsRegressor(n_neighbors = 2, leaf_size = 30, p = 2)\n",
    "neigh_u.fit(X_train_u,y_train_u)\n",
    "y_predict_NN_u = neigh_u.predict(X_test_u)\n",
    "print \"NN Score using ubalanced data: {}\".format(performance_metric(y_test_u,y_predict_NN_u))\n",
    "\n",
    "print \"\"\n",
    "\n",
    "# Decision Tree using balanced data\n",
    "tree_b = DecisionTreeRegressor(random_state=0, max_depth = 14 )\n",
    "tree_b.fit(X_train_b, y_train_b )\n",
    "y_predict_tree_b = tree_b.predict(X_test_b)\n",
    "print \"DT Score using balanced data: {}\".format(performance_metric(y_test_b,y_predict_tree_b))\n",
    "\n",
    "# Decision Tree using unbalanced data\n",
    "tree_u = DecisionTreeRegressor(random_state=0, max_depth = 14, min_samples_split = 2)\n",
    "tree_u.fit(X_train_u, y_train_u )\n",
    "y_predict_tree_u = tree_u.predict(X_test_u)\n",
    "print \"DT Score using ubalanced data: {}\".format(performance_metric(y_test_u,y_predict_tree_u))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree parameters: {'presort': False, 'splitter': 'best', 'max_leaf_nodes': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'criterion': 'gini', 'random_state': None, 'max_features': None, 'max_depth': 12, 'class_weight': None} \n",
      "\n",
      "NN parameters: {'n_neighbors': 2, 'n_jobs': 1, 'algorithm': 'auto', 'metric': 'minkowski', 'metric_params': None, 'p': 2, 'weights': 'uniform', 'leaf_size': 30} \n"
     ]
    }
   ],
   "source": [
    "# Import 'make_scorer', 'DecisionTreeRegressor', 'KNeighborsRegressor' and 'GridSearchCV'\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn import tree\n",
    "from sklearn import grid_search\n",
    "from sklearn import cross_validation\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Improving Decission Tree\n",
    "def fit_model(X, y):\n",
    "    \"\"\" Performs grid search over the 'max_depth' parameter for a \n",
    "        decision tree regressor trained on the input data [X, y]. \"\"\"\n",
    "    \n",
    "    # Create cross-validation sets from the training data\n",
    "    cv_sets = cross_validation.ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.10, random_state = 0)\n",
    "\n",
    "    # TODO: Create a decision tree regressor object\n",
    "    regressor = tree.DecisionTreeClassifier()\n",
    "\n",
    "    # TODO: Create a dictionary for the parameter 'max_depth' with a range from 1 to 10\n",
    "    params = {'max_depth':range(1,15)}\n",
    "\n",
    "    # TODO: Transform 'performance_metric' into a scoring function using 'make_scorer' \n",
    "    scoring_fnc = make_scorer(performance_metric)\n",
    "\n",
    "    # TODO: Create the grid search object\n",
    "    grid = grid_search.GridSearchCV(regressor,params,scoring=scoring_fnc,cv=cv_sets)\n",
    "\n",
    "    # Fit the grid search object to the data to compute the optimal model\n",
    "    grid = grid.fit(X, y)\n",
    "\n",
    "    # Return the optimal model after fitting the data\n",
    "    return grid.best_estimator_\n",
    "\n",
    "reg_tree_b = fit_model(X_train_b,np.array(y_train_b, dtype='int16'))\n",
    "# Produce grid parameters\n",
    "print \"Decision Tree parameters: {} \".format(reg_tree_b.get_params())\n",
    "\n",
    "print \"\"\n",
    "\n",
    "# Improving nearestNeighborgh\n",
    "#\n",
    "def fit_model(X, y):\n",
    "    \"\"\" Performs grid search over the 'max_depth' parameter for a \n",
    "        decision tree regressor trained on the input data [X, y]. \"\"\"\n",
    "    \n",
    "    # Create cross-validation sets from the training data\n",
    "    cv_sets = cross_validation.ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.10, random_state = 0)\n",
    "\n",
    "    # TODO: Create a decision tree regressor object\n",
    "    regressor = KNeighborsRegressor()\n",
    "\n",
    "    # TODO: Create a dictionary for the parameter 'max_depth' with a range from 1 to 10\n",
    "    params = {'n_neighbors':range(2,10)}\n",
    "\n",
    "    # TODO: Transform 'performance_metric' into a scoring function using 'make_scorer' \n",
    "    scoring_fnc = make_scorer(performance_metric)\n",
    "\n",
    "    # TODO: Create the grid search object\n",
    "    grid = grid_search.GridSearchCV(regressor,params,scoring=scoring_fnc,cv=cv_sets)\n",
    "\n",
    "    # Fit the grid search object to the data to compute the optimal model\n",
    "    grid = grid.fit(X, y)\n",
    "\n",
    "    # Return the optimal model after fitting the data\n",
    "    return grid.best_estimator_\n",
    "\n",
    "reg_nn_b = fit_model(X_train_b,np.array(y_train_b, dtype='int16'))\n",
    "# Produce the grid parameters\n",
    "print \"NN parameters: {} \".format(reg_nn_b.get_params())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "After training the two additional regressors with datasets balanced and unbalanced, we determined that the best regressors are the NN and DT with no balanced data.\n",
    "\n",
    "Is clear that the BIAS is affecting the models, but the only way to deal with it is finding other features that can be added to the dataset. Adding other features will invalidate the experiment so we will create a final ensemble model with the best regressors to see if we can improve the accuracy of the model. \n",
    "Another potential solution is finding patient samples with a more spread variance, but we only have access to one dataset, and we are using all the patients in it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble model of the best regressors\n",
    " \n",
    "In this section, I will create an ensemble model using the NN and DT models that score higher than 0.40 of accuracy.\n",
    "An ensemble model is just a group of models working in series where the first model make a prediction and those targets where it did not predict correctly are passed to the next model and so on as many time as estimators were selected; this type of ensemble used a method called Boosting. They are more suitable for a group of weak learners than the other method called bagging.\n",
    "\n",
    "In the previous sections of this project I used three different models, only two of them got accuracies bigger than 40%, and only one passed the 50% of precision; this by itself justifies the use of an Ensemble. The sklearn ensemble model we used in based on the Adaboost algorithm, its steps are:\n",
    "\n",
    "- Fit a sequence of weak learners repeatedly modified versions of the data. \n",
    "- The predictions from all of them are then combined through a weighted majority vote (or sum) to produce the final prediction.\n",
    "\n",
    "I will perform the following steps\n",
    "\n",
    "- Create a boost ensemble model using the best regressors from the previous section.\n",
    "- Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DT Ensemble Score using 30 estimators balanced data: 0.257476797806\n",
      "\n",
      "NN Ensemble Score using 100 estimators unbalanced data: 0.57569355522\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "# Create ensemble of decision trees\n",
    "tree_b_ensemble = AdaBoostRegressor(DecisionTreeRegressor(max_depth=10),\n",
    "                          n_estimators=30, random_state=0)\n",
    "tree_b_ensemble.fit(X_train_b, y_train_b )\n",
    "y_predict_tree_b_ensemble = tree_b_ensemble.predict(X_test_b)\n",
    "\n",
    "print \"DT Ensemble Score using 30 estimators balanced data: {}\".format(\n",
    "    performance_metric(y_test_b,y_predict_tree_b_ensemble))\n",
    "\n",
    "print \"\"\n",
    "\n",
    "# Create ensemble of Nearest Neighborg\n",
    "nn_u_ensemble = AdaBoostRegressor(KNeighborsRegressor(n_neighbors = 4),\n",
    "                          n_estimators=100, random_state=0)\n",
    "nn_u_ensemble.fit(X_train_u, y_train_u)\n",
    "y_predict_nn_u_ensemble = nn_u_ensemble.predict(X_test_u)\n",
    "\n",
    "print \"NN Ensemble Score using 100 estimators unbalanced data: {}\".format(\n",
    "    performance_metric(y_test_u,y_predict_nn_u_ensemble))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "Even after using a boosted ensemble which helps with the BIAS still the best model NN produces only a 0.57 score of accuracy.\n",
    "I believe the model is robust enough, but still can benefit from adding some extract features to help boost the precision.\n",
    "In a later exercise at the end of the project,  I tested the model with three fake patients and plotted the result over imposing to the Patients Day standard distribution curve.  The predicted values are within the norm deviations what proof the model is predicting a value that belongs to the data distribution.\n",
    "The final model is reasonable but not aligned with what I was expecting. When I started to balance the data, I used a clustering algorithm to determine if BMI variance explained the hospitalization days, but the result was negative. The purpose of my project was to proof the hypothesis of the BMI be responsible for the patient recovery speed, and the conclusions is that is not on its own.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the prediction model.\n",
    "In the next section I will used my best model to predict the stay of three patients.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "| Features | Patient 1 | Patient 2 | Patiente 3 |\n",
    "| :---: | :---: | :---: | :---: |\n",
    "| Patient_Age | 25 | 50 | 80 |\n",
    "| BIM | 20 | 45 | 35 |\n",
    "| Gender | M | M | F |\n",
    "| Medical Condition | diabities | cardiovascular disorders | high collesterol |\n",
    "\n",
    "\n",
    "I will perform the following steps\n",
    "- Using the best model predict the above patients hospital stay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted days in Hospital patient 1 5.75\n",
      "Predicted days in Hospital patient 2 3.75\n",
      "Predicted days in Hospital patient 3 8.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116d2c850>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gmGvIuW72kUezA4AMBeUDp9E34Mf/vnUc+492obPXM1VFJCKiCWCYa0gs3ewAsMzxJ3xl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\nvUIIIe655x6xfft21tOg995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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12113d450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce a matrix for patient data\n",
    "# Patient_Age,BIM,F,M,cardiovascular_disorders,diabetes,hypertension,lipid_disorders,neoplasia\n",
    "\n",
    "patient_data = [[25, 20, 0, 1, 0, 1, 0, 0, 0], # Patient 1\n",
    "               [50, 45, 0, 1, 1, 0, 0, 0, 0], # Patient 2\n",
    "               [80, 35, 1, 0, 0, 0, 0, 1, 0]]  # Client 3\n",
    "\n",
    "# Show predictions\n",
    "for i, days in enumerate(nn_u_ensemble.predict(patient_data)):\n",
    "    print \"Predicted days in Hospital patient {} {}\".format(i+1, days)\n",
    "\n",
    "# Plot the patients stayind data histogram and over impose the prediction\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.hist(good_data['Hospital_Stay_Days'], bins = 30)\n",
    "for days in nn_u_ensemble.predict(patient_data):\n",
    "    plt.axvline(days, c = 'r', lw = 3)\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "sns.distplot(good_data['Hospital_Stay_Days'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Free-Form Visualization\n",
    "\n",
    "\n",
    "I used several forms of visualization during the project.\n",
    "At the beginnings of this study, I plotted the data correlation; it helps me to understand in advanced how well the linear regressor will perform.\n",
    "When I used the clustering model to get information to balance the data. I plotted the cluster using the first two principal components to label the clusters based on the samples I took. The plot helps me to understand that the hospitalization value explains better the data than the BMI or the Age.\n",
    "I plotted the heatmap of the selected samples to help me compared the selected samples with each other and with the dataset.\n",
    "I plotted the distribution of the continues features to understand the relation with each other.\n",
    "I Final use an over imposed plotted to see if my prediction were within the normal distributions of the dataset target.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improvements\n",
    "\n",
    "\n",
    "I am not happy with 0.57 of precision. It is evident to me that the dataset requires another feature that helps to explain better the hospitalization time. One thing I would do different is work in tandem with a physician to help me understand base on his/her experience what that missing feature might be. Another thing I will be different is to select the days in \"intensive care\" instead of the hospitalization days of the patient first visits. It is strange that when I plotted the variance of the features versus the hospitalization time the patients within 0 and maybe 15 days where all group by age or BMI. After 15 days in the hospital, it seems like the age and BMI start to spread as more time the patient spent in the hospital; this could be because I selected the specific medical conditions instead of picking all type of medical conditions. Another hypothesis is that because the records come from the same hospital, such hospital has standard time for the first visit based on the patient condition. A way to overcome these problems is to collect data from different hospitals and get all medical conditions.\n",
    "Also, most of the patients in my sample were overweight or obese; very few were within the normal weight range. So more variance in the samples is also required.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capstone Project Conclusions\n",
    "\n",
    "The initial goal of the project was to create a model capable of predicting how long a patient will stay in a hospital, based on the features describe in the Obesity Paradox studies.\n",
    "The main features are the BMI, Age, Gender and four specific medical conditions.\n",
    "The exploration of the data resulted in the following findings:\n",
    "1. The correlation between the BMI and Age with the hospital time was little and did not explain the hospitalization time too well. \n",
    "2. The staying in the hospital accounted for most of the variances in the whole dataset.\n",
    "3. Data was unbalanced, having more patients with shorter stays than longer ones.\n",
    "The experiment indicates that the BMI, Age and medical conditions explain the staying in the hospital to a certain degree, but the lack of other features creates BIAS in the model, which doesn't allow it to get a better accuracy.\n",
    "Using other features will invalid the initial goal, so we didn't add any.\n",
    "The experiment can benefit from more medical domain knowledge, by adding the missing features that will improve the model accuracy.\n",
    "\n",
    "It was exciting and challenging to me work with numerical and categorical features at the same time the target is numerical. Linear regression doesn't like discrete values, so it forces me to use others models capable of predicting numerical values using a mix of numerical and categorical features, this was quite a challenge.  \n"
   ]
  }
 ],
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