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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oNssL9U4ldo1"
+ },
+ "source": [
+ "# Project Description ⛳\n",
+ "<hr>\n",
+ "\n",
+ "**Your final project will involve all topics covered from Week 2 to 8 by using data to solve a real-life problem. Remember you're doing this with your team**.\n",
+ "\n",
+ "You’ve learned a ton about data collection and cleaning, visualization and insight, machine leearning, and model evaluation in this course. The final project is your chance to solve a problem with these from scratch.\n",
+ "\n",
+ "\n",
+ "`Use the rubric below as a guideline for your project as this will be used in grading your submissions`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Import necessary libraries\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.impute import SimpleImputer\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.preprocessing import OneHotEncoder\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Load the datasets provided\n",
+ "\n",
+ "- We have been provided with two datasets in the form of csv files. \n",
+ "- 1. IDsmapping.csv which contains the mapping of different ids. \n",
+ "- 2. Diabetic_data.csv that contains the information related to diabetes of different patients. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IDs Mapping:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>admission_type_id</th>\n",
+ " <th>description</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>Emergency</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>Urgent</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>Elective</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>Newborn</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>Not Available</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " admission_type_id description\n",
+ "0 1 Emergency\n",
+ "1 2 Urgent\n",
+ "2 3 Elective\n",
+ "3 4 Newborn\n",
+ "4 5 Not Available"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load the datasets\n",
+ "ids_mapping = pd.read_csv('IDs_mapping.csv')\n",
+ "diabetic_data = pd.read_csv('diabetic_data.csv')\n",
+ "\n",
+ "# Display the first few rows of each dataset\n",
+ "print(\"IDs Mapping:\")\n",
+ "ids_mapping.head()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Diabetic Data:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>encounter_id</th>\n",
+ " <th>patient_nbr</th>\n",
+ " <th>race</th>\n",
+ " <th>gender</th>\n",
+ " <th>age</th>\n",
+ " <th>weight</th>\n",
+ " <th>admission_type_id</th>\n",
+ " <th>discharge_disposition_id</th>\n",
+ " <th>admission_source_id</th>\n",
+ " <th>time_in_hospital</th>\n",
+ " <th>...</th>\n",
+ " <th>citoglipton</th>\n",
+ " <th>insulin</th>\n",
+ " <th>glyburide-metformin</th>\n",
+ " <th>glipizide-metformin</th>\n",
+ " <th>glimepiride-pioglitazone</th>\n",
+ " <th>metformin-rosiglitazone</th>\n",
+ " <th>metformin-pioglitazone</th>\n",
+ " <th>change</th>\n",
+ " <th>diabetesMed</th>\n",
+ " <th>readmitted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2278392</td>\n",
+ " <td>8222157</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[0-10)</td>\n",
+ " <td>?</td>\n",
+ " <td>6</td>\n",
+ " <td>25</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>149190</td>\n",
+ " <td>55629189</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[10-20)</td>\n",
+ " <td>?</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>7</td>\n",
+ " <td>3</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Up</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>64410</td>\n",
+ " <td>86047875</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Female</td>\n",
+ " <td>[20-30)</td>\n",
+ " <td>?</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>500364</td>\n",
+ " <td>82442376</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[30-40)</td>\n",
+ " <td>?</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Up</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>16680</td>\n",
+ " <td>42519267</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[40-50)</td>\n",
+ " <td>?</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 50 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " encounter_id patient_nbr race gender age weight \\\n",
+ "0 2278392 8222157 Caucasian Female [0-10) ? \n",
+ "1 149190 55629189 Caucasian Female [10-20) ? \n",
+ "2 64410 86047875 AfricanAmerican Female [20-30) ? \n",
+ "3 500364 82442376 Caucasian Male [30-40) ? \n",
+ "4 16680 42519267 Caucasian Male [40-50) ? \n",
+ "\n",
+ " admission_type_id discharge_disposition_id admission_source_id \\\n",
+ "0 6 25 1 \n",
+ "1 1 1 7 \n",
+ "2 1 1 7 \n",
+ "3 1 1 7 \n",
+ "4 1 1 7 \n",
+ "\n",
+ " time_in_hospital ... citoglipton insulin glyburide-metformin \\\n",
+ "0 1 ... No No No \n",
+ "1 3 ... No Up No \n",
+ "2 2 ... No No No \n",
+ "3 2 ... No Up No \n",
+ "4 1 ... No Steady No \n",
+ "\n",
+ " glipizide-metformin glimepiride-pioglitazone metformin-rosiglitazone \\\n",
+ "0 No No No \n",
+ "1 No No No \n",
+ "2 No No No \n",
+ "3 No No No \n",
+ "4 No No No \n",
+ "\n",
+ " metformin-pioglitazone change diabetesMed readmitted \n",
+ "0 No No No NO \n",
+ "1 No Ch Yes >30 \n",
+ "2 No No Yes NO \n",
+ "3 No Ch Yes NO \n",
+ "4 No Ch Yes NO \n",
+ "\n",
+ "[5 rows x 50 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print(\"\\nDiabetic Data:\")\n",
+ "diabetic_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Missing values before handling:\n",
+ "encounter_id 0\n",
+ "patient_nbr 0\n",
+ "race 0\n",
+ "gender 0\n",
+ "age 0\n",
+ "weight 0\n",
+ "admission_type_id 0\n",
+ "discharge_disposition_id 0\n",
+ "admission_source_id 0\n",
+ "time_in_hospital 0\n",
+ "payer_code 0\n",
+ "medical_specialty 0\n",
+ "num_lab_procedures 0\n",
+ "num_procedures 0\n",
+ "num_medications 0\n",
+ "number_outpatient 0\n",
+ "number_emergency 0\n",
+ "number_inpatient 0\n",
+ "diag_1 0\n",
+ "diag_2 0\n",
+ "diag_3 0\n",
+ "number_diagnoses 0\n",
+ "max_glu_serum 96420\n",
+ "A1Cresult 84748\n",
+ "metformin 0\n",
+ "repaglinide 0\n",
+ "nateglinide 0\n",
+ "chlorpropamide 0\n",
+ "glimepiride 0\n",
+ "acetohexamide 0\n",
+ "glipizide 0\n",
+ "glyburide 0\n",
+ "tolbutamide 0\n",
+ "pioglitazone 0\n",
+ "rosiglitazone 0\n",
+ "acarbose 0\n",
+ "miglitol 0\n",
+ "troglitazone 0\n",
+ "tolazamide 0\n",
+ "examide 0\n",
+ "citoglipton 0\n",
+ "insulin 0\n",
+ "glyburide-metformin 0\n",
+ "glipizide-metformin 0\n",
+ "glimepiride-pioglitazone 0\n",
+ "metformin-rosiglitazone 0\n",
+ "metformin-pioglitazone 0\n",
+ "change 0\n",
+ "diabetesMed 0\n",
+ "readmitted 0\n",
+ "dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print missing values before handling\n",
+ "print(\"Missing values before handling:\")\n",
+ "print(diabetic_data.isnull().sum())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Hrx5R7uVjevj"
+ },
+ "source": [
+ "# Data cleaning & preprocessing\n",
+ "\n",
+ "- Demonstrate clear understanding of different data cleaning and preprocessing techniques by applying them to your dataset.\n",
+ "- Clearly document (within the notebook) all cleaning and preprocessing steps."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Missing values after handling:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "encounter_id 0\n",
+ "patient_nbr 0\n",
+ "race 0\n",
+ "gender 0\n",
+ "age 0\n",
+ "weight 0\n",
+ "admission_type_id 0\n",
+ "discharge_disposition_id 0\n",
+ "admission_source_id 0\n",
+ "time_in_hospital 0\n",
+ "payer_code 0\n",
+ "medical_specialty 0\n",
+ "num_lab_procedures 0\n",
+ "num_procedures 0\n",
+ "num_medications 0\n",
+ "number_outpatient 0\n",
+ "number_emergency 0\n",
+ "number_inpatient 0\n",
+ "diag_1 0\n",
+ "diag_2 0\n",
+ "diag_3 0\n",
+ "number_diagnoses 0\n",
+ "max_glu_serum 0\n",
+ "A1Cresult 0\n",
+ "metformin 0\n",
+ "repaglinide 0\n",
+ "nateglinide 0\n",
+ "chlorpropamide 0\n",
+ "glimepiride 0\n",
+ "acetohexamide 0\n",
+ "glipizide 0\n",
+ "glyburide 0\n",
+ "tolbutamide 0\n",
+ "pioglitazone 0\n",
+ "rosiglitazone 0\n",
+ "acarbose 0\n",
+ "miglitol 0\n",
+ "troglitazone 0\n",
+ "tolazamide 0\n",
+ "examide 0\n",
+ "citoglipton 0\n",
+ "insulin 0\n",
+ "glyburide-metformin 0\n",
+ "glipizide-metformin 0\n",
+ "glimepiride-pioglitazone 0\n",
+ "metformin-rosiglitazone 0\n",
+ "metformin-pioglitazone 0\n",
+ "change 0\n",
+ "diabetesMed 0\n",
+ "readmitted 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1 (a). Data cleaning by handling missing values in all columns:\n",
+ "\n",
+ "# Define columns with missing values \n",
+ "columns_with_missing = diabetic_data.columns[diabetic_data.isnull().any()].tolist()\n",
+ "\n",
+ "# Create a SimpleImputer object with strategy based on column type\n",
+ "numerical_imputer = SimpleImputer(strategy='median')\n",
+ "categorical_imputer = SimpleImputer(strategy='most_frequent')\n",
+ "\n",
+ "# Apply imputation to numerical and categorical columns separately\n",
+ "numerical_columns = diabetic_data.select_dtypes(include=['int64', 'float64']).columns\n",
+ "categorical_columns = diabetic_data.select_dtypes(include=['object']).columns\n",
+ "\n",
+ "diabetic_data[numerical_columns] = numerical_imputer.fit_transform(diabetic_data[numerical_columns])\n",
+ "diabetic_data[categorical_columns] = categorical_imputer.fit_transform(diabetic_data[categorical_columns])\n",
+ "\n",
+ "# Display information about missing values after handling\n",
+ "print(\"\\nMissing values after handling:\")\n",
+ "diabetic_data.isnull().sum()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Columns with '?' values: ['race', 'weight', 'payer_code', 'medical_specialty', 'diag_1', 'diag_2', 'diag_3']\n",
+ "\n",
+ "Count and Percentage of '?' in each column:\n",
+ " Column Count Percentage\n",
+ "0 race 2273 2.233555\n",
+ "1 weight 98569 96.858479\n",
+ "2 payer_code 40256 39.557416\n",
+ "3 medical_specialty 49949 49.082208\n",
+ "4 diag_1 21 0.020636\n",
+ "5 diag_2 358 0.351787\n",
+ "6 diag_3 1423 1.398306\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 1 (b) Data cleaning by handling ? in all columns with ? values:\n",
+ "# Identify columns with '?' values\n",
+ "columns_with_question_mark = diabetic_data.columns[diabetic_data.isin(['?']).any()].tolist()\n",
+ "\n",
+ "print(\"Columns with '?' values:\", columns_with_question_mark)\n",
+ "\n",
+ "# Count the number of '?' in each column\n",
+ "question_mark_counts = diabetic_data[columns_with_question_mark].apply(lambda x: x.value_counts()['?'])\n",
+ "\n",
+ "# Calculate the percentage of '?' in each column\n",
+ "question_mark_percentage = (question_mark_counts / len(diabetic_data)) * 100\n",
+ "\n",
+ "# Create a DataFrame to store the results and reset the index\n",
+ "question_mark_results = pd.DataFrame({'Column': columns_with_question_mark, 'Count': question_mark_counts, 'Percentage': question_mark_percentage}).reset_index(drop=True)\n",
+ "\n",
+ "# Display the results\n",
+ "print(\"\\nCount and Percentage of '?' in each column:\")\n",
+ "print(question_mark_results)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "DataFrame after filling remaining '?' values:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>encounter_id</th>\n",
+ " <th>patient_nbr</th>\n",
+ " <th>race</th>\n",
+ " <th>gender</th>\n",
+ " <th>age</th>\n",
+ " <th>admission_type_id</th>\n",
+ " <th>discharge_disposition_id</th>\n",
+ " <th>admission_source_id</th>\n",
+ " <th>time_in_hospital</th>\n",
+ " <th>num_lab_procedures</th>\n",
+ " <th>...</th>\n",
+ " <th>citoglipton</th>\n",
+ " <th>insulin</th>\n",
+ " <th>glyburide-metformin</th>\n",
+ " <th>glipizide-metformin</th>\n",
+ " <th>glimepiride-pioglitazone</th>\n",
+ " <th>metformin-rosiglitazone</th>\n",
+ " <th>metformin-pioglitazone</th>\n",
+ " <th>change</th>\n",
+ " <th>diabetesMed</th>\n",
+ " <th>readmitted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2278392.0</td>\n",
+ " <td>8222157.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[0-10)</td>\n",
+ " <td>6.0</td>\n",
+ " <td>25.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>41.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>149190.0</td>\n",
+ " <td>55629189.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[10-20)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>59.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Up</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>64410.0</td>\n",
+ " <td>86047875.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Female</td>\n",
+ " <td>[20-30)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>500364.0</td>\n",
+ " <td>82442376.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[30-40)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>44.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Up</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>16680.0</td>\n",
+ " <td>42519267.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[40-50)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>51.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>35754.0</td>\n",
+ " <td>82637451.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[50-60)</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>31.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>55842.0</td>\n",
+ " <td>84259809.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[60-70)</td>\n",
+ " <td>3.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>70.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>63768.0</td>\n",
+ " <td>114882984.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[70-80)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>73.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>12522.0</td>\n",
+ " <td>48330783.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[80-90)</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>13.0</td>\n",
+ " <td>68.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>15738.0</td>\n",
+ " <td>63555939.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[90-100)</td>\n",
+ " <td>3.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>12.0</td>\n",
+ " <td>33.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>28236.0</td>\n",
+ " <td>89869032.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Female</td>\n",
+ " <td>[40-50)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>9.0</td>\n",
+ " <td>47.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>36900.0</td>\n",
+ " <td>77391171.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Male</td>\n",
+ " <td>[60-70)</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>62.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td><30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>40926.0</td>\n",
+ " <td>85504905.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[40-50)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>60.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Down</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td><30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>42570.0</td>\n",
+ " <td>77586282.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[80-90)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>10.0</td>\n",
+ " <td>55.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>62256.0</td>\n",
+ " <td>49726791.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Female</td>\n",
+ " <td>[60-70)</td>\n",
+ " <td>3.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>49.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>73578.0</td>\n",
+ " <td>86328819.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Male</td>\n",
+ " <td>[60-70)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>12.0</td>\n",
+ " <td>75.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Up</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>77076.0</td>\n",
+ " <td>92519352.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Male</td>\n",
+ " <td>[50-60)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>45.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td><30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>84222.0</td>\n",
+ " <td>108662661.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[50-60)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>29.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>89682.0</td>\n",
+ " <td>107389323.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Male</td>\n",
+ " <td>[70-80)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>35.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>148530.0</td>\n",
+ " <td>69422211.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[70-80)</td>\n",
+ " <td>3.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>42.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>150006.0</td>\n",
+ " <td>22864131.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[50-60)</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>66.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Down</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>150048.0</td>\n",
+ " <td>21239181.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[60-70)</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>36.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>182796.0</td>\n",
+ " <td>63000108.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Female</td>\n",
+ " <td>[70-80)</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>47.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>183930.0</td>\n",
+ " <td>107400762.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[80-90)</td>\n",
+ " <td>2.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>42.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>216156.0</td>\n",
+ " <td>62718876.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Female</td>\n",
+ " <td>[70-80)</td>\n",
+ " <td>3.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>19.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>221634.0</td>\n",
+ " <td>21861756.0</td>\n",
+ " <td>Other</td>\n",
+ " <td>Female</td>\n",
+ " <td>[50-60)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>33.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>236316.0</td>\n",
+ " <td>40523301.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[80-90)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>64.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>248916.0</td>\n",
+ " <td>115196778.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[50-60)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>25.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>250872.0</td>\n",
+ " <td>41606064.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[20-30)</td>\n",
+ " <td>2.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>10.0</td>\n",
+ " <td>53.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Down</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>252822.0</td>\n",
+ " <td>18196434.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[80-90)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>52.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>30 rows × 47 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " encounter_id patient_nbr race gender age \\\n",
+ "0 2278392.0 8222157.0 Caucasian Female [0-10) \n",
+ "1 149190.0 55629189.0 Caucasian Female [10-20) \n",
+ "2 64410.0 86047875.0 AfricanAmerican Female [20-30) \n",
+ "3 500364.0 82442376.0 Caucasian Male [30-40) \n",
+ "4 16680.0 42519267.0 Caucasian Male [40-50) \n",
+ "5 35754.0 82637451.0 Caucasian Male [50-60) \n",
+ "6 55842.0 84259809.0 Caucasian Male [60-70) \n",
+ "7 63768.0 114882984.0 Caucasian Male [70-80) \n",
+ "8 12522.0 48330783.0 Caucasian Female [80-90) \n",
+ "9 15738.0 63555939.0 Caucasian Female [90-100) \n",
+ "10 28236.0 89869032.0 AfricanAmerican Female [40-50) \n",
+ "11 36900.0 77391171.0 AfricanAmerican Male [60-70) \n",
+ "12 40926.0 85504905.0 Caucasian Female [40-50) \n",
+ "13 42570.0 77586282.0 Caucasian Male [80-90) \n",
+ "14 62256.0 49726791.0 AfricanAmerican Female [60-70) \n",
+ "15 73578.0 86328819.0 AfricanAmerican Male [60-70) \n",
+ "16 77076.0 92519352.0 AfricanAmerican Male [50-60) \n",
+ "17 84222.0 108662661.0 Caucasian Female [50-60) \n",
+ "18 89682.0 107389323.0 AfricanAmerican Male [70-80) \n",
+ "19 148530.0 69422211.0 Caucasian Male [70-80) \n",
+ "20 150006.0 22864131.0 Caucasian Female [50-60) \n",
+ "21 150048.0 21239181.0 Caucasian Male [60-70) \n",
+ "22 182796.0 63000108.0 AfricanAmerican Female [70-80) \n",
+ "23 183930.0 107400762.0 Caucasian Female [80-90) \n",
+ "24 216156.0 62718876.0 AfricanAmerican Female [70-80) \n",
+ "25 221634.0 21861756.0 Other Female [50-60) \n",
+ "26 236316.0 40523301.0 Caucasian Male [80-90) \n",
+ "27 248916.0 115196778.0 Caucasian Female [50-60) \n",
+ "28 250872.0 41606064.0 Caucasian Male [20-30) \n",
+ "29 252822.0 18196434.0 Caucasian Female [80-90) \n",
+ "\n",
+ " admission_type_id discharge_disposition_id admission_source_id \\\n",
+ "0 6.0 25.0 1.0 \n",
+ "1 1.0 1.0 7.0 \n",
+ "2 1.0 1.0 7.0 \n",
+ "3 1.0 1.0 7.0 \n",
+ "4 1.0 1.0 7.0 \n",
+ "5 2.0 1.0 2.0 \n",
+ "6 3.0 1.0 2.0 \n",
+ "7 1.0 1.0 7.0 \n",
+ "8 2.0 1.0 4.0 \n",
+ "9 3.0 3.0 4.0 \n",
+ "10 1.0 1.0 7.0 \n",
+ "11 2.0 1.0 4.0 \n",
+ "12 1.0 3.0 7.0 \n",
+ "13 1.0 6.0 7.0 \n",
+ "14 3.0 1.0 2.0 \n",
+ "15 1.0 3.0 7.0 \n",
+ "16 1.0 1.0 7.0 \n",
+ "17 1.0 1.0 7.0 \n",
+ "18 1.0 1.0 7.0 \n",
+ "19 3.0 6.0 2.0 \n",
+ "20 2.0 1.0 4.0 \n",
+ "21 2.0 1.0 4.0 \n",
+ "22 2.0 1.0 4.0 \n",
+ "23 2.0 6.0 1.0 \n",
+ "24 3.0 1.0 2.0 \n",
+ "25 1.0 1.0 7.0 \n",
+ "26 1.0 3.0 7.0 \n",
+ "27 1.0 1.0 1.0 \n",
+ "28 2.0 1.0 2.0 \n",
+ "29 1.0 2.0 7.0 \n",
+ "\n",
+ " time_in_hospital num_lab_procedures ... citoglipton insulin \\\n",
+ "0 1.0 41.0 ... No No \n",
+ "1 3.0 59.0 ... No Up \n",
+ "2 2.0 11.0 ... No No \n",
+ "3 2.0 44.0 ... No Up \n",
+ "4 1.0 51.0 ... No Steady \n",
+ "5 3.0 31.0 ... No Steady \n",
+ "6 4.0 70.0 ... No Steady \n",
+ "7 5.0 73.0 ... No No \n",
+ "8 13.0 68.0 ... No Steady \n",
+ "9 12.0 33.0 ... No Steady \n",
+ "10 9.0 47.0 ... No Steady \n",
+ "11 7.0 62.0 ... No Steady \n",
+ "12 7.0 60.0 ... No Down \n",
+ "13 10.0 55.0 ... No Steady \n",
+ "14 1.0 49.0 ... No Steady \n",
+ "15 12.0 75.0 ... No Up \n",
+ "16 4.0 45.0 ... No Steady \n",
+ "17 3.0 29.0 ... No No \n",
+ "18 5.0 35.0 ... No Steady \n",
+ "19 6.0 42.0 ... No Steady \n",
+ "20 2.0 66.0 ... No Down \n",
+ "21 2.0 36.0 ... No Steady \n",
+ "22 2.0 47.0 ... No No \n",
+ "23 11.0 42.0 ... No No \n",
+ "24 3.0 19.0 ... No Steady \n",
+ "25 1.0 33.0 ... No No \n",
+ "26 6.0 64.0 ... No No \n",
+ "27 2.0 25.0 ... No Steady \n",
+ "28 10.0 53.0 ... No Down \n",
+ "29 5.0 52.0 ... No No \n",
+ "\n",
+ " glyburide-metformin glipizide-metformin glimepiride-pioglitazone \\\n",
+ "0 No No No \n",
+ "1 No No No \n",
+ "2 No No No \n",
+ "3 No No No \n",
+ "4 No No No \n",
+ "5 No No No \n",
+ "6 No No No \n",
+ "7 No No No \n",
+ "8 No No No \n",
+ "9 No No No \n",
+ "10 No No No \n",
+ "11 No No No \n",
+ "12 No No No \n",
+ "13 No No No \n",
+ "14 No No No \n",
+ "15 No No No \n",
+ "16 No No No \n",
+ "17 No No No \n",
+ "18 No No No \n",
+ "19 No No No \n",
+ "20 No No No \n",
+ "21 No No No \n",
+ "22 No No No \n",
+ "23 No No No \n",
+ "24 No No No \n",
+ "25 No No No \n",
+ "26 No No No \n",
+ "27 No No No \n",
+ "28 No No No \n",
+ "29 No No No \n",
+ "\n",
+ " metformin-rosiglitazone metformin-pioglitazone change diabetesMed \\\n",
+ "0 No No No No \n",
+ "1 No No Ch Yes \n",
+ "2 No No No Yes \n",
+ "3 No No Ch Yes \n",
+ "4 No No Ch Yes \n",
+ "5 No No No Yes \n",
+ "6 No No Ch Yes \n",
+ "7 No No No Yes \n",
+ "8 No No Ch Yes \n",
+ "9 No No Ch Yes \n",
+ "10 No No No Yes \n",
+ "11 No No Ch Yes \n",
+ "12 No No Ch Yes \n",
+ "13 No No No Yes \n",
+ "14 No No No Yes \n",
+ "15 No No Ch Yes \n",
+ "16 No No Ch Yes \n",
+ "17 No No No Yes \n",
+ "18 No No No Yes \n",
+ "19 No No Ch Yes \n",
+ "20 No No Ch Yes \n",
+ "21 No No Ch Yes \n",
+ "22 No No No No \n",
+ "23 No No No No \n",
+ "24 No No Ch Yes \n",
+ "25 No No No Yes \n",
+ "26 No No Ch Yes \n",
+ "27 No No No Yes \n",
+ "28 No No Ch Yes \n",
+ "29 No No Ch Yes \n",
+ "\n",
+ " readmitted \n",
+ "0 NO \n",
+ "1 >30 \n",
+ "2 NO \n",
+ "3 NO \n",
+ "4 NO \n",
+ "5 >30 \n",
+ "6 NO \n",
+ "7 >30 \n",
+ "8 NO \n",
+ "9 NO \n",
+ "10 >30 \n",
+ "11 <30 \n",
+ "12 <30 \n",
+ "13 NO \n",
+ "14 >30 \n",
+ "15 NO \n",
+ "16 <30 \n",
+ "17 NO \n",
+ "18 >30 \n",
+ "19 NO \n",
+ "20 NO \n",
+ "21 NO \n",
+ "22 NO \n",
+ "23 >30 \n",
+ "24 NO \n",
+ "25 NO \n",
+ "26 NO \n",
+ "27 >30 \n",
+ "28 >30 \n",
+ "29 >30 \n",
+ "\n",
+ "[30 rows x 47 columns]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1 (c) Data cleaning by dropping ? in all columns with ? values:\n",
+ "# Define a threshold for dropping columns\n",
+ "threshold = 30 # Drop columns with more than 30% '?' values\n",
+ "\n",
+ "# Identify columns to drop\n",
+ "columns_to_drop = question_mark_results['Column'][question_mark_results['Percentage'] > threshold]\n",
+ "\n",
+ "# Drop columns\n",
+ "clean_diabetic_data = diabetic_data.drop(columns=columns_to_drop)\n",
+ "\n",
+ "# Fill remaining '?' values with the most frequent value in each column\n",
+ "clean_diabetic_data = clean_diabetic_data.replace('?', np.nan)\n",
+ "clean_diabetic_data = clean_diabetic_data.apply(lambda x: x.fillna(x.value_counts().idxmax()))\n",
+ "\n",
+ "# Display information about the DataFrame after filling remaining '?' values\n",
+ "print(\"\\nDataFrame after filling remaining '?' values:\")\n",
+ "clean_diabetic_data.head(30)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of duplicate records before dropping: 0\n",
+ "Number of duplicate records after dropping: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 2. Data cleaning by handling duplicates in the dataset. \n",
+ "# Check for duplicate records\n",
+ "num_duplicates_before = clean_diabetic_data.duplicated().sum()\n",
+ "print(f\"Number of duplicate records before dropping: {num_duplicates_before}\")\n",
+ "\n",
+ "# Drop duplicate records\n",
+ "clean_diabetic_data.drop_duplicates(inplace=True)\n",
+ "\n",
+ "# Check for duplicate records after dropping\n",
+ "num_duplicates_after = clean_diabetic_data.duplicated().sum()\n",
+ "print(f\"Number of duplicate records after dropping: {num_duplicates_after}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Data after handling outliers with IQR:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>encounter_id</th>\n",
+ " <th>patient_nbr</th>\n",
+ " <th>race</th>\n",
+ " <th>gender</th>\n",
+ " <th>age</th>\n",
+ " <th>admission_type_id</th>\n",
+ " <th>discharge_disposition_id</th>\n",
+ " <th>admission_source_id</th>\n",
+ " <th>time_in_hospital</th>\n",
+ " <th>num_lab_procedures</th>\n",
+ " <th>...</th>\n",
+ " <th>citoglipton</th>\n",
+ " <th>insulin</th>\n",
+ " <th>glyburide-metformin</th>\n",
+ " <th>glipizide-metformin</th>\n",
+ " <th>glimepiride-pioglitazone</th>\n",
+ " <th>metformin-rosiglitazone</th>\n",
+ " <th>metformin-pioglitazone</th>\n",
+ " <th>change</th>\n",
+ " <th>diabetesMed</th>\n",
+ " <th>readmitted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2278392.0</td>\n",
+ " <td>8222157.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[0-10)</td>\n",
+ " <td>6.0</td>\n",
+ " <td>25.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>41.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>149190.0</td>\n",
+ " <td>55629189.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[10-20)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>59.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Up</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>64410.0</td>\n",
+ " <td>86047875.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Female</td>\n",
+ " <td>[20-30)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>500364.0</td>\n",
+ " <td>82442376.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[30-40)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>44.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Up</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>16680.0</td>\n",
+ " <td>42519267.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[40-50)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>51.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 47 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " encounter_id patient_nbr race gender age \\\n",
+ "0 2278392.0 8222157.0 Caucasian Female [0-10) \n",
+ "1 149190.0 55629189.0 Caucasian Female [10-20) \n",
+ "2 64410.0 86047875.0 AfricanAmerican Female [20-30) \n",
+ "3 500364.0 82442376.0 Caucasian Male [30-40) \n",
+ "4 16680.0 42519267.0 Caucasian Male [40-50) \n",
+ "\n",
+ " admission_type_id discharge_disposition_id admission_source_id \\\n",
+ "0 6.0 25.0 1.0 \n",
+ "1 1.0 1.0 7.0 \n",
+ "2 1.0 1.0 7.0 \n",
+ "3 1.0 1.0 7.0 \n",
+ "4 1.0 1.0 7.0 \n",
+ "\n",
+ " time_in_hospital num_lab_procedures ... citoglipton insulin \\\n",
+ "0 1.0 41.0 ... No No \n",
+ "1 3.0 59.0 ... No Up \n",
+ "2 2.0 11.0 ... No No \n",
+ "3 2.0 44.0 ... No Up \n",
+ "4 1.0 51.0 ... No Steady \n",
+ "\n",
+ " glyburide-metformin glipizide-metformin glimepiride-pioglitazone \\\n",
+ "0 No No No \n",
+ "1 No No No \n",
+ "2 No No No \n",
+ "3 No No No \n",
+ "4 No No No \n",
+ "\n",
+ " metformin-rosiglitazone metformin-pioglitazone change diabetesMed \\\n",
+ "0 No No No No \n",
+ "1 No No Ch Yes \n",
+ "2 No No No Yes \n",
+ "3 No No Ch Yes \n",
+ "4 No No Ch Yes \n",
+ "\n",
+ " readmitted \n",
+ "0 NO \n",
+ "1 >30 \n",
+ "2 NO \n",
+ "3 NO \n",
+ "4 NO \n",
+ "\n",
+ "[5 rows x 47 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 3. Data cleaning by handling outliers in the dataset using the Interquartile Range (IQR) method\n",
+ "\n",
+ "# Function to handle outliers using IQR method\n",
+ "def handle_outliers_iqr(df, column):\n",
+ " Q1 = df[column].quantile(0.25)\n",
+ " Q3 = df[column].quantile(0.75)\n",
+ " IQR = Q3 - Q1\n",
+ " lower_limit = Q1 - 1.5 * IQR\n",
+ " upper_limit = Q3 + 1.5 * IQR\n",
+ " \n",
+ " # Replace values outside the acceptable range with the nearest acceptable value\n",
+ " df[column] = df[column].apply(lambda x: lower_limit if x < lower_limit else (upper_limit if x > upper_limit else x))\n",
+ "\n",
+ "# Apply outlier handling to numerical columns\n",
+ "for col in numerical_columns:\n",
+ " handle_outliers_iqr(diabetic_data, col)\n",
+ "\n",
+ "# Display the data after handling outliers\n",
+ "print(\"\\nData after handling outliers with IQR:\")\n",
+ "clean_diabetic_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Data after handling outliers with winsorization:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>encounter_id</th>\n",
+ " <th>patient_nbr</th>\n",
+ " <th>race</th>\n",
+ " <th>gender</th>\n",
+ " <th>age</th>\n",
+ " <th>admission_type_id</th>\n",
+ " <th>discharge_disposition_id</th>\n",
+ " <th>admission_source_id</th>\n",
+ " <th>time_in_hospital</th>\n",
+ " <th>num_lab_procedures</th>\n",
+ " <th>...</th>\n",
+ " <th>citoglipton</th>\n",
+ " <th>insulin</th>\n",
+ " <th>glyburide-metformin</th>\n",
+ " <th>glipizide-metformin</th>\n",
+ " <th>glimepiride-pioglitazone</th>\n",
+ " <th>metformin-rosiglitazone</th>\n",
+ " <th>metformin-pioglitazone</th>\n",
+ " <th>change</th>\n",
+ " <th>diabetesMed</th>\n",
+ " <th>readmitted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>27170784.0</td>\n",
+ " <td>8222157.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[0-10)</td>\n",
+ " <td>6.0</td>\n",
+ " <td>18.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>41.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>27170784.0</td>\n",
+ " <td>55629189.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Female</td>\n",
+ " <td>[10-20)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>59.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Up</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>>30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>27170784.0</td>\n",
+ " <td>86047875.0</td>\n",
+ " <td>AfricanAmerican</td>\n",
+ " <td>Female</td>\n",
+ " <td>[20-30)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>11.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>27170784.0</td>\n",
+ " <td>82442376.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[30-40)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>44.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Up</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>27170784.0</td>\n",
+ " <td>42519267.0</td>\n",
+ " <td>Caucasian</td>\n",
+ " <td>Male</td>\n",
+ " <td>[40-50)</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>51.0</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>Steady</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Ch</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 47 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " encounter_id patient_nbr race gender age \\\n",
+ "0 27170784.0 8222157.0 Caucasian Female [0-10) \n",
+ "1 27170784.0 55629189.0 Caucasian Female [10-20) \n",
+ "2 27170784.0 86047875.0 AfricanAmerican Female [20-30) \n",
+ "3 27170784.0 82442376.0 Caucasian Male [30-40) \n",
+ "4 27170784.0 42519267.0 Caucasian Male [40-50) \n",
+ "\n",
+ " admission_type_id discharge_disposition_id admission_source_id \\\n",
+ "0 6.0 18.0 1.0 \n",
+ "1 1.0 1.0 7.0 \n",
+ "2 1.0 1.0 7.0 \n",
+ "3 1.0 1.0 7.0 \n",
+ "4 1.0 1.0 7.0 \n",
+ "\n",
+ " time_in_hospital num_lab_procedures ... citoglipton insulin \\\n",
+ "0 1.0 41.0 ... No No \n",
+ "1 3.0 59.0 ... No Up \n",
+ "2 2.0 11.0 ... No No \n",
+ "3 2.0 44.0 ... No Up \n",
+ "4 1.0 51.0 ... No Steady \n",
+ "\n",
+ " glyburide-metformin glipizide-metformin glimepiride-pioglitazone \\\n",
+ "0 No No No \n",
+ "1 No No No \n",
+ "2 No No No \n",
+ "3 No No No \n",
+ "4 No No No \n",
+ "\n",
+ " metformin-rosiglitazone metformin-pioglitazone change diabetesMed \\\n",
+ "0 No No No No \n",
+ "1 No No Ch Yes \n",
+ "2 No No No Yes \n",
+ "3 No No Ch Yes \n",
+ "4 No No Ch Yes \n",
+ "\n",
+ " readmitted \n",
+ "0 NO \n",
+ "1 >30 \n",
+ "2 NO \n",
+ "3 NO \n",
+ "4 NO \n",
+ "\n",
+ "[5 rows x 47 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 3. Data cleaning by handling outliers in the dataset using the winsorization method\n",
+ "# Define the quantiles for winsorization\n",
+ "lower_quantile = 0.05\n",
+ "upper_quantile = 0.95\n",
+ "\n",
+ "# Identify numerical columns\n",
+ "numerical_columns = clean_diabetic_data.select_dtypes(include=['int64', 'float64']).columns\n",
+ "\n",
+ "# Apply winsorization to all numerical columns\n",
+ "for col in numerical_columns:\n",
+ " lower_threshold = clean_diabetic_data[col].quantile(lower_quantile)\n",
+ " upper_threshold = clean_diabetic_data[col].quantile(upper_quantile)\n",
+ " \n",
+ " # Winsorize the data\n",
+ " clean_diabetic_data[col] = clean_diabetic_data[col].clip(lower=lower_threshold, upper=upper_threshold)\n",
+ "\n",
+ "# Print the updated dataset\n",
+ "print(\"\\nData after handling outliers with winsorization:\")\n",
+ "clean_diabetic_data.head()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Mc_79H2OjxQZ"
+ },
+ "source": [
+ "# Exploratory Data Analysis\n",
+ "- Apply both measure of central tendency and dispersion to understand the data.\n",
+ "- Perform corellation analysis of the dependent and independent variables\n",
+ "- What does the corellation analysis says about the dependent and independent variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "faZkxe0ckHCC"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Measures of Central Tendency:\n",
+ " Mean Median Mode\n",
+ "encounter_id 1.641807e+08 152388987.0 27170784.00\n",
+ "patient_nbr 5.311885e+07 45505143.0 1456971.75\n",
+ "admission_type_id 2.017511e+00 1.0 1.00\n",
+ "discharge_disposition_id 3.531985e+00 1.0 1.00\n",
+ "admission_source_id 5.748944e+00 7.0 7.00\n",
+ "time_in_hospital 4.327261e+00 4.0 3.00\n",
+ "num_lab_procedures 4.285068e+01 44.0 73.00\n",
+ "num_procedures 1.291050e+00 1.0 0.00\n",
+ "num_medications 1.576892e+01 15.0 6.00\n",
+ "number_outpatient 2.449836e-01 0.0 0.00\n",
+ "number_emergency 1.118546e-01 0.0 0.00\n",
+ "number_inpatient 5.483167e-01 0.0 0.00\n",
+ "number_diagnoses 7.471719e+00 8.0 9.00\n"
+ ]
+ }
+ ],
+ "source": [
+ "#EDA task 1. Measure of Central Tendency \n",
+ "\n",
+ "# Select relevant numerical features\n",
+ "numerical_features = clean_diabetic_data.select_dtypes(include=['int64', 'float64']).columns\n",
+ "\n",
+ "# Calculate Mean, Median, and Mode\n",
+ "mean_values = clean_diabetic_data[numerical_features].mean()\n",
+ "median_values = clean_diabetic_data[numerical_features].median()\n",
+ "mode_values = clean_diabetic_data[numerical_features].mode().iloc[0] # Use iloc[0] to get the first mode if multiple\n",
+ "\n",
+ "# Create a DataFrame to display the results\n",
+ "central_tendency = pd.DataFrame({'Mean': mean_values, 'Median': median_values, 'Mode': mode_values})\n",
+ "print(\"\\nMeasures of Central Tendency:\")\n",
+ "print(central_tendency)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Measures of Dispersion:\n",
+ " Range Variance Std Deviation\n",
+ "encounter_id 378950321.0 9.614896e+15 9.805558e+07\n",
+ "patient_nbr 111480138.0 1.287103e+15 3.587621e+07\n",
+ "admission_type_id 5.0 2.024712e+00 1.422924e+00\n",
+ "discharge_disposition_id 17.0 2.155106e+01 4.642312e+00\n",
+ "admission_source_id 16.0 1.637473e+01 4.046571e+00\n",
+ "time_in_hospital 10.0 7.844455e+00 2.800795e+00\n",
+ "num_lab_procedures 72.0 3.502529e+02 1.871505e+01\n",
+ "num_procedures 5.0 2.502357e+00 1.581884e+00\n",
+ "num_medications 30.0 4.740614e+01 6.885212e+00\n",
+ "number_outpatient 2.0 3.459668e-01 5.881894e-01\n",
+ "number_emergency 1.0 9.934416e-02 3.151891e-01\n",
+ "number_inpatient 3.0 8.119680e-01 9.010927e-01\n",
+ "number_diagnoses 7.5 3.228872e+00 1.796906e+00\n"
+ ]
+ }
+ ],
+ "source": [
+ "#EDA Task 2 - Measure of dispersion\n",
+ "\n",
+ "# Calculate Range, Variance, and Standard Deviation\n",
+ "range_values = clean_diabetic_data[numerical_features].max() - diabetic_data[numerical_features].min()\n",
+ "variance_values = clean_diabetic_data[numerical_features].var()\n",
+ "std_deviation_values = clean_diabetic_data[numerical_features].std()\n",
+ "\n",
+ "# Create a DataFrame to display the results\n",
+ "dispersion = pd.DataFrame({'Range': range_values, 'Variance': variance_values, 'Std Deviation': std_deviation_values})\n",
+ "print(\"\\nMeasures of Dispersion:\")\n",
+ "print(dispersion)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Correlation Matrix:\n",
+ " encounter_id patient_nbr admission_type_id \\\n",
+ "encounter_id 1.000000 0.489209 -0.161249 \n",
+ "patient_nbr 0.489209 1.000000 -0.005293 \n",
+ "admission_type_id -0.161249 -0.005293 1.000000 \n",
+ "discharge_disposition_id -0.140567 -0.149249 0.058985 \n",
+ "admission_source_id -0.115732 -0.031814 0.111147 \n",
+ "time_in_hospital -0.063097 -0.019969 -0.011626 \n",
+ "num_lab_procedures -0.023569 0.020376 -0.155730 \n",
+ "num_procedures -0.020661 -0.021894 0.133825 \n",
+ "num_medications 0.089883 0.034301 0.077142 \n",
+ "number_outpatient 0.134030 0.153183 0.047662 \n",
+ "number_emergency 0.119635 0.114389 -0.023605 \n",
+ "number_inpatient 0.031084 0.029656 -0.040543 \n",
+ "number_diagnoses 0.267518 0.236157 -0.117528 \n",
+ "\n",
+ " discharge_disposition_id admission_source_id \\\n",
+ "encounter_id -0.140567 -0.115732 \n",
+ "patient_nbr -0.149249 -0.031814 \n",
+ "admission_type_id 0.058985 0.111147 \n",
+ "discharge_disposition_id 1.000000 0.013284 \n",
+ "admission_source_id 0.013284 1.000000 \n",
+ "time_in_hospital 0.176231 -0.005634 \n",
+ "num_lab_procedures 0.028112 0.041391 \n",
+ "num_procedures 0.019990 -0.140538 \n",
+ "num_medications 0.107639 -0.048545 \n",
+ "number_outpatient -0.016064 0.046942 \n",
+ "number_emergency -0.028821 0.094525 \n",
+ "number_inpatient 0.037586 0.038284 \n",
+ "number_diagnoses 0.050321 0.074959 \n",
+ "\n",
+ " time_in_hospital num_lab_procedures \\\n",
+ "encounter_id -0.063097 -0.023569 \n",
+ "patient_nbr -0.019969 0.020376 \n",
+ "admission_type_id -0.011626 -0.155730 \n",
+ "discharge_disposition_id 0.176231 0.028112 \n",
+ "admission_source_id -0.005634 0.041391 \n",
+ "time_in_hospital 1.000000 0.316625 \n",
+ "num_lab_procedures 0.316625 1.000000 \n",
+ "num_procedures 0.193810 0.046424 \n",
+ "num_medications 0.472379 0.257072 \n",
+ "number_outpatient -0.018354 -0.026270 \n",
+ "number_emergency -0.002919 0.004966 \n",
+ "number_inpatient 0.088139 0.046464 \n",
+ "number_diagnoses 0.223167 0.150825 \n",
+ "\n",
+ " num_procedures num_medications number_outpatient \\\n",
+ "encounter_id -0.020661 0.089883 0.134030 \n",
+ "patient_nbr -0.021894 0.034301 0.153183 \n",
+ "admission_type_id 0.133825 0.077142 0.047662 \n",
+ "discharge_disposition_id 0.019990 0.107639 -0.016064 \n",
+ "admission_source_id -0.140538 -0.048545 0.046942 \n",
+ "time_in_hospital 0.193810 0.472379 -0.018354 \n",
+ "num_lab_procedures 0.046424 0.257072 -0.026270 \n",
+ "num_procedures 1.000000 0.360035 -0.033945 \n",
+ "num_medications 0.360035 1.000000 0.063128 \n",
+ "number_outpatient -0.033945 0.063128 1.000000 \n",
+ "number_emergency -0.050533 0.038318 0.171594 \n",
+ "number_inpatient -0.073340 0.089317 0.151528 \n",
+ "number_diagnoses 0.067025 0.273659 0.110262 \n",
+ "\n",
+ " number_emergency number_inpatient number_diagnoses \n",
+ "encounter_id 0.119635 0.031084 0.267518 \n",
+ "patient_nbr 0.114389 0.029656 0.236157 \n",
+ "admission_type_id -0.023605 -0.040543 -0.117528 \n",
+ "discharge_disposition_id -0.028821 0.037586 0.050321 \n",
+ "admission_source_id 0.094525 0.038284 0.074959 \n",
+ "time_in_hospital -0.002919 0.088139 0.223167 \n",
+ "num_lab_procedures 0.004966 0.046464 0.150825 \n",
+ "num_procedures -0.050533 -0.073340 0.067025 \n",
+ "num_medications 0.038318 0.089317 0.273659 \n",
+ "number_outpatient 0.171594 0.151528 0.110262 \n",
+ "number_emergency 1.000000 0.231034 0.089691 \n",
+ "number_inpatient 0.231034 1.000000 0.128690 \n",
+ "number_diagnoses 0.089691 0.128690 1.000000 \n"
+ ]
+ }
+ ],
+ "source": [
+ "#EDA Task 3 a - Correlation Analysis of the whole dataset\n",
+ "\n",
+ "# Exclude non-numeric columns from correlation analysis\n",
+ "numeric_data = clean_diabetic_data.select_dtypes(include=['float64', 'int64'])\n",
+ "\n",
+ "# Calculate correlation matrix\n",
+ "correlation_matrix = numeric_data.corr()\n",
+ "\n",
+ "# Display correlation matrix\n",
+ "print(\"\\nCorrelation Matrix:\")\n",
+ "print(correlation_matrix)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Correlation Matrix:\n",
+ " time_in_hospital num_medications \\\n",
+ "time_in_hospital 1.000000 0.472379 \n",
+ "num_medications 0.472379 1.000000 \n",
+ "num_lab_procedures 0.316625 0.257072 \n",
+ "number_diagnoses 0.223167 0.273659 \n",
+ "admission_type_id -0.011626 0.077142 \n",
+ "discharge_disposition_id 0.176231 0.107639 \n",
+ "admission_source_id -0.005634 -0.048545 \n",
+ "change 0.111072 0.249117 \n",
+ "readmitted 0.053492 0.059127 \n",
+ "\n",
+ " num_lab_procedures number_diagnoses \\\n",
+ "time_in_hospital 0.316625 0.223167 \n",
+ "num_medications 0.257072 0.273659 \n",
+ "num_lab_procedures 1.000000 0.150825 \n",
+ "number_diagnoses 0.150825 1.000000 \n",
+ "admission_type_id -0.155730 -0.117528 \n",
+ "discharge_disposition_id 0.028112 0.050321 \n",
+ "admission_source_id 0.041391 0.074959 \n",
+ "change 0.062623 0.054779 \n",
+ "readmitted 0.040826 0.112883 \n",
+ "\n",
+ " admission_type_id discharge_disposition_id \\\n",
+ "time_in_hospital -0.011626 0.176231 \n",
+ "num_medications 0.077142 0.107639 \n",
+ "num_lab_procedures -0.155730 0.028112 \n",
+ "number_diagnoses -0.117528 0.050321 \n",
+ "admission_type_id 1.000000 0.058985 \n",
+ "discharge_disposition_id 0.058985 1.000000 \n",
+ "admission_source_id 0.111147 0.013284 \n",
+ "change 0.006577 -0.018223 \n",
+ "readmitted -0.003855 -0.020346 \n",
+ "\n",
+ " admission_source_id change readmitted \n",
+ "time_in_hospital -0.005634 0.111072 0.053492 \n",
+ "num_medications -0.048545 0.249117 0.059127 \n",
+ "num_lab_procedures 0.041391 0.062623 0.040826 \n",
+ "number_diagnoses 0.074959 0.054779 0.112883 \n",
+ "admission_type_id 0.111147 0.006577 -0.003855 \n",
+ "discharge_disposition_id 0.013284 -0.018223 -0.020346 \n",
+ "admission_source_id 1.000000 0.002413 0.039787 \n",
+ "change 0.002413 1.000000 0.046008 \n",
+ "readmitted 0.039787 0.046008 1.000000 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# EDA Task 3 (b1) - Correlation Analysis of the key variables\n",
+ "\n",
+ "# Selecting key variables\n",
+ "selected_variables = [\n",
+ " 'time_in_hospital',\n",
+ " 'num_medications',\n",
+ " 'num_lab_procedures',\n",
+ " 'number_diagnoses',\n",
+ " 'admission_type_id',\n",
+ " 'discharge_disposition_id',\n",
+ " 'admission_source_id',\n",
+ " 'change',\n",
+ " 'readmitted'\n",
+ "]\n",
+ "\n",
+ "# Create a subset of the dataframe with selected variables\n",
+ "selected_data = clean_diabetic_data[selected_variables].copy() # Explicitly create a copy\n",
+ "\n",
+ "# Convert 'No' to 0 and 'Yes' to 1 in the 'change' and 'readmitted' columns\n",
+ "selected_data['change'] = selected_data['change'].map({'No': 0, 'Ch': 1})\n",
+ "selected_data['readmitted'] = selected_data['readmitted'].map({'NO': 0, '>30': 1, '<30': 1})\n",
+ "\n",
+ "# Calculate correlation matrix\n",
+ "correlation_matrix = selected_data.corr()\n",
+ "\n",
+ "# Display the correlation matrix\n",
+ "print(\"\\nCorrelation Matrix:\")\n",
+ "print(correlation_matrix)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1200x1000 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#EDA Task 3 (b2) - Correlation Analysis visualization of the key variables \n",
+ "\n",
+ "# Visualize the correlation matrix using a heatmap\n",
+ "plt.figure(figsize=(12, 10))\n",
+ "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=.5)\n",
+ "plt.title('Correlation Matrix of Key Variables')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HlGMQOqmkHkI"
+ },
+ "source": [
+ "# Data Visualization & Insight\n",
+ "- Use at least 5 different visuals to tell a story about the data\n",
+ "- Clearly document (within the notebook) 5 different insights you gained from the data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visual 1 - Age Distribution of Patients:\n",
+ " Description:\n",
+ "- This visualization provides insights into the age groups of diabetic patients in the dataset.\n",
+ "\n",
+ "Interpretation:\n",
+ "- Each bar in the chart represents an age group, and the height of the bar indicates the count of patients in that age group.\n",
+ "- The age groups are defined using bins, making it easier to analyze the distribution.\n",
+ "\n",
+ "Relevance:\n",
+ "- Understanding Demographics: By examining the age distribution, we can identify the prevalent age groups among diabetic patients.\n",
+ "- Targeted Healthcare Planning: This information can assist in tailoring healthcare strategies based on the dominant age groups observed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "id": "9JrNcTaqkaU3"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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zvBZJ1i1fvnymaNGipmbNmqZ///4mMjIyQ/urZz68dOmSeeqpp0ytWrVM0aJFjZeXlwkMDDRjx441Fy5csB537tw58+ijj5pixYoZh8NhzcDnfL6JEyf+5baM+XP2vt27d5sOHTpY9enRo4c5efKky+OTkpLMs88+awICAoyXl5dp0qSJiYqKyjDLojHGTJ061VSsWNG4ubm5bPPqWRaNuTxT4qhRo0z58uWNh4eHKVOmjBk4cKCJiYlxaVe+fHnTrl27DK+rSZMmpkmTJhmWX+3s2bPmqaeeMmXKlDHu7u6mfPnyZvTo0ebSpUsZni+7syxeS0xMjOnbt6/x8fExBQsWNA8//LDZvHlzpv3++OOPTbVq1YyHh4fLjIdXz7LotHz5ctOsWTNTtGhR4+npacqXL28effRRs379eqtN7969TaFChTI8NrPnXL9+vXnggQeMp6enkWR69+593fskANxKDmOuY1osAMAdZfz48XrxxRd1+PDha57TBQAA7MUhiwBwh5s+fbokqVq1akpJSdGGDRv09ttvq1evXoQxAAByOQIZANzhChYsqClTpujQoUNKSkpSuXLlNGrUKL344ot2dw0AAPwFDlkEAAAAAJsw7T0AAAAA2IRABgAAAAA2IZABAAAAgE2Y1OMmSk9P17Fjx1SkSBE5HA67uwMAAADAJsYYxcfHy9/fX/nyZT0ORiC7iY4dO6aAgAC7uwEAAAAgl4iOjr7mZWgIZDdRkSJFJF0uetGiRW3uDQAAAAC7xMXFKSAgwMoIWSGQ3UTOwxSLFi1KIAMAAADwl6cyMakHAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATdzt7gAAAABujfDwcLu7YIu8+rpxZ2KEDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsYmsge+2111SvXj0VKVJEPj4+6tSpk/bv3+/Spk+fPnI4HC63Bg0auLRJSkrSkCFDVKpUKRUqVEgdO3bUkSNHXNrExMQoLCxM3t7e8vb2VlhYmM6fP+/S5vDhw+rQoYMKFSqkUqVKaejQoUpOTr4lrx0AAAAAbA1kmzZt0tNPP61t27Zp3bp1Sk1NVUhIiC5cuODSrnXr1jp+/Lh1W716tcv6YcOGadmyZVq0aJG2bNmihIQEtW/fXmlpaVabnj17KioqShEREYqIiFBUVJTCwsKs9WlpaWrXrp0uXLigLVu2aNGiRfr00081YsSIW1sEAAAAAHmWu50bj4iIcLk/Z84c+fj4aPfu3WrcuLG13NPTU35+fpk+R2xsrGbPnq358+erZcuWkqQFCxYoICBA69evV2hoqPbt26eIiAht27ZN9evXlyS9//77Cg4O1v79+xUYGKi1a9dq7969io6Olr+/vyRp0qRJ6tOnj1599VUVLVr0VpQAAAAAQB6Wq84hi42NlSSVKFHCZfnXX38tHx8fVa1aVf369dOpU6esdbt371ZKSopCQkKsZf7+/goKCtLWrVslSZGRkfL29rbCmCQ1aNBA3t7eLm2CgoKsMCZJoaGhSkpK0u7duzPtb1JSkuLi4lxuAAAAAHC9ck0gM8Zo+PDhevjhhxUUFGQtb9OmjRYuXKgNGzZo0qRJ2rlzp5o3b66kpCRJ0okTJ5Q/f34VL17c5fl8fX114sQJq42Pj0+Gbfr4+Li08fX1dVlfvHhx5c+f32pztddee806J83b21sBAQE5LwAAAACAPMfWQxavNHjwYP3www/asmWLy/Lu3btb/w4KClLdunVVvnx5rVq1Sp07d87y+Ywxcjgc1v0r/30jba40evRoDR8+3LofFxdHKAMAAABw3XLFCNmQIUO0cuVKbdy4Uffcc88125YpU0bly5fXgQMHJEl+fn5KTk5WTEyMS7tTp05ZI15+fn46efJkhuc6ffq0S5urR8JiYmKUkpKSYeTMydPTU0WLFnW5AQAAAMD1sjWQGWM0ePBgffbZZ9qwYYMqVqz4l485e/asoqOjVaZMGUlSnTp15OHhoXXr1lltjh8/rj179qhhw4aSpODgYMXGxmrHjh1Wm+3btys2NtalzZ49e3T8+HGrzdq1a+Xp6ak6derclNcLAAAAAFey9ZDFp59+Wh999JFWrFihIkWKWCNU3t7e8vLyUkJCgsLDw9WlSxeVKVNGhw4d0vPPP69SpUrpkUcesdr27dtXI0aMUMmSJVWiRAmNHDlSNWvWtGZdrF69ulq3bq1+/frpvffekyT1799f7du3V2BgoCQpJCRENWrUUFhYmCZOnKhz585p5MiR6tevHyNfAAAAAG4JW0fIZs6cqdjYWDVt2lRlypSxbosXL5Ykubm56ccff9Tf//53Va1aVb1791bVqlUVGRmpIkWKWM8zZcoUderUSd26dVOjRo1UsGBBff7553Jzc7PaLFy4UDVr1lRISIhCQkJUq1YtzZ8/31rv5uamVatWqUCBAmrUqJG6deumTp066c0337x9BQEAAACQpziMMcbuTtwt4uLi5O3trdjYWEbVAACA7cLDw+3ugi3y6utG7nK92SBXTOoBAAAAAHkRgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsQiADAAAAAJu4290BAACAv7Lv1Q12d8EW1V9obncXANxijJABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE1sDWSvvfaa6tWrpyJFisjHx0edOnXS/v37XdoYYxQeHi5/f395eXmpadOm+umnn1zaJCUlaciQISpVqpQKFSqkjh076siRIy5tYmJiFBYWJm9vb3l7eyssLEznz593aXP48GF16NBBhQoVUqlSpTR06FAlJyffktcOAAAAALYGsk2bNunpp5/Wtm3btG7dOqWmpiokJEQXLlyw2kyYMEGTJ0/W9OnTtXPnTvn5+alVq1aKj4+32gwbNkzLli3TokWLtGXLFiUkJKh9+/ZKS0uz2vTs2VNRUVGKiIhQRESEoqKiFBYWZq1PS0tTu3btdOHCBW3ZskWLFi3Sp59+qhEjRtyeYgAAAADIcxzGGGN3J5xOnz4tHx8fbdq0SY0bN5YxRv7+/ho2bJhGjRol6fJomK+vr9544w0NGDBAsbGxKl26tObPn6/u3btLko4dO6aAgACtXr1aoaGh2rdvn2rUqKFt27apfv36kqRt27YpODhYP//8swIDA7VmzRq1b99e0dHR8vf3lyQtWrRIffr00alTp1S0aNG/7H9cXJy8vb0VGxt7Xe0BAMD12ffqBru7YIvqLzS/oceHh4ffnI7cYfLq60bucr3ZIFedQxYbGytJKlGihCTp4MGDOnHihEJCQqw2np6eatKkibZu3SpJ2r17t1JSUlza+Pv7KygoyGoTGRkpb29vK4xJUoMGDeTt7e3SJigoyApjkhQaGqqkpCTt3r070/4mJSUpLi7O5QYAAAAA1yvXBDJjjIYPH66HH35YQUFBkqQTJ05Iknx9fV3a+vr6WutOnDih/Pnzq3jx4tds4+Pjk2GbPj4+Lm2u3k7x4sWVP39+q83VXnvtNeucNG9vbwUEBGT3ZQMAAADIw3JNIBs8eLB++OEHffzxxxnWORwOl/vGmAzLrnZ1m8za56TNlUaPHq3Y2FjrFh0dfc0+AQAAAMCVckUgGzJkiFauXKmNGzfqnnvusZb7+flJUoYRqlOnTlmjWX5+fkpOTlZMTMw125w8eTLDdk+fPu3S5urtxMTEKCUlJcPImZOnp6eKFi3qcgMAAACA62VrIDPGaPDgwfrss8+0YcMGVaxY0WV9xYoV5efnp3Xr1lnLkpOTtWnTJjVs2FCSVKdOHXl4eLi0OX78uPbs2WO1CQ4OVmxsrHbs2GG12b59u2JjY13a7NmzR8ePH7farF27Vp6enqpTp87Nf/EAAAAA8jx3Ozf+9NNP66OPPtKKFStUpEgRa4TK29tbXl5ecjgcGjZsmMaPH68qVaqoSpUqGj9+vAoWLKiePXtabfv27asRI0aoZMmSKlGihEaOHKmaNWuqZcuWkqTq1aurdevW6tevn9577z1JUv/+/dW+fXsFBgZKkkJCQlSjRg2FhYVp4sSJOnfunEaOHKl+/fox8gUAAADglrA1kM2cOVOS1LRpU5flc+bMUZ8+fSRJzz77rBITEzVo0CDFxMSofv36Wrt2rYoUKWK1nzJlitzd3dWtWzclJiaqRYsWmjt3rtzc3Kw2Cxcu1NChQ63ZGDt27Kjp06db693c3LRq1SoNGjRIjRo1kpeXl3r27Kk333zzFr16AAAAAHldrroO2Z2O65ABAHBrcB2ynMmr1+PKq68bucsdeR0yAAAAAMhLCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYxN3uDgAAkNe82utRu7tgixcWLLW7CwCQ6xDIAAAAgP9Z8slDdnfBFt267rC7C3kWhywCAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATWwNZN988406dOggf39/ORwOLV++3GV9nz595HA4XG4NGjRwaZOUlKQhQ4aoVKlSKlSokDp27KgjR464tImJiVFYWJi8vb3l7e2tsLAwnT9/3qXN4cOH1aFDBxUqVEilSpXS0KFDlZycfCteNgAAAABIsjmQXbhwQbVr19b06dOzbNO6dWsdP37cuq1evdpl/bBhw7Rs2TItWrRIW7ZsUUJCgtq3b6+0tDSrTc+ePRUVFaWIiAhFREQoKipKYWFh1vq0tDS1a9dOFy5c0JYtW7Ro0SJ9+umnGjFixM1/0QAAAADwP+52brxNmzZq06bNNdt4enrKz88v03WxsbGaPXu25s+fr5YtW0qSFixYoICAAK1fv16hoaHat2+fIiIitG3bNtWvX1+S9P777ys4OFj79+9XYGCg1q5dq7179yo6Olr+/v6SpEmTJqlPnz569dVXVbRo0Uy3n5SUpKSkJOt+XFxctmsAAAAAIO/K9eeQff311/Lx8VHVqlXVr18/nTp1ylq3e/dupaSkKCQkxFrm7++voKAgbd26VZIUGRkpb29vK4xJUoMGDeTt7e3SJigoyApjkhQaGqqkpCTt3r07y7699tpr1mGQ3t7eCggIuGmvGwAAAMDdL1cHsjZt2mjhwoXasGGDJk2apJ07d6p58+bWqNSJEyeUP39+FS9e3OVxvr6+OnHihNXGx8cnw3P7+Pi4tPH19XVZX7x4ceXPn99qk5nRo0crNjbWukVHR9/Q6wUAAACQt9h6yOJf6d69u/XvoKAg1a1bV+XLl9eqVavUuXPnLB9njJHD4bDuX/nvG2lzNU9PT3l6ev7l6wAAAACAzOTqEbKrlSlTRuXLl9eBAwckSX5+fkpOTlZMTIxLu1OnTlkjXn5+fjp58mSG5zp9+rRLm6tHwmJiYpSSkpJh5AwAAAAAbpY7KpCdPXtW0dHRKlOmjCSpTp068vDw0Lp166w2x48f1549e9SwYUNJUnBwsGJjY7Vjxw6rzfbt2xUbG+vSZs+ePTp+/LjVZu3atfL09FSdOnVux0sDAAAAkAflKJDde++9Onv2bIbl58+f17333nvdz5OQkKCoqChFRUVJkg4ePKioqCgdPnxYCQkJGjlypCIjI3Xo0CF9/fXX6tChg0qVKqVHHnlEkuTt7a2+fftqxIgR+uqrr/R///d/6tWrl2rWrGnNuli9enW1bt1a/fr107Zt27Rt2zb169dP7du3V2BgoCQpJCRENWrUUFhYmP7v//5PX331lUaOHKl+/fplOcMiAAAAANyoHJ1DdujQIZfrfDklJSXp6NGj1/08u3btUrNmzaz7w4cPlyT17t1bM2fO1I8//qgPP/xQ58+fV5kyZdSsWTMtXrxYRYoUsR4zZcoUubu7q1u3bkpMTFSLFi00d+5cubm5WW0WLlyooUOHWrMxduzY0eXaZ25ublq1apUGDRqkRo0aycvLSz179tSbb755/UUBAAAAgGzKViBbuXKl9e8vv/xS3t7e1v20tDR99dVXqlChwnU/X9OmTWWMyXL9l19++ZfPUaBAAU2bNk3Tpk3Lsk2JEiW0YMGCaz5PuXLl9MUXX/zl9gAAAADgZslWIOvUqZOkyzMS9u7d22Wdh4eHKlSooEmTJt20zgEAAADA3SxbgSw9PV2SVLFiRe3cuVOlSpW6JZ0CAAAAgLwgR+eQHTx48Gb3AwAAAADynBxfGPqrr77SV199pVOnTlkjZ04ffPDBDXcMAAAAAO52OQpk48aN08svv6y6deuqTJkycjgcN7tfAAAAAHDXy1Ege/fddzV37lyFhYXd7P4AAAAAQJ6RowtDJycnq2HDhje7LwAAAACQp+QokD355JP66KOPbnZfAAAAACBPydEhi5cuXdKsWbO0fv161apVSx4eHi7rJ0+efFM6BwAAAAB3sxwFsh9++EH333+/JGnPnj0u65jgAwAAAACuT44C2caNG292PwAAAAAgz8nROWQAAAAAgBuXoxGyZs2aXfPQxA0bNuS4QwAAAACQV+QokDnPH3NKSUlRVFSU9uzZo969e9+MfgEAAADAXS9HgWzKlCmZLg8PD1dCQsINdQgAAAAA8oqbeg5Zr1699MEHH9zMpwQAAACAu9ZNDWSRkZEqUKDAzXxKAAAAALhr5eiQxc6dO7vcN8bo+PHj2rVrl8aMGXNTOgYAAAAAd7scBTJvb2+X+/ny5VNgYKBefvllhYSE3JSOAQAAAMDdLkeBbM6cOTe7HwAAAACQ5+QokDnt3r1b+/btk8PhUI0aNfTAAw/crH4BAAAAwF0vR4Hs1KlTeuyxx/T111+rWLFiMsYoNjZWzZo106JFi1S6dOmb3U8AAAAAuOvkaJbFIUOGKC4uTj/99JPOnTunmJgY7dmzR3FxcRo6dOjN7iMAAAAA3JVyNEIWERGh9evXq3r16tayGjVq6J133mFSDwAAAAC4TjkaIUtPT5eHh0eG5R4eHkpPT7/hTgEAAABAXpCjQNa8eXP961//0rFjx6xlR48e1TPPPKMWLVrctM4BAAAAwN0sR4Fs+vTpio+PV4UKFVSpUiVVrlxZFStWVHx8vKZNm3az+wgAAAAAd6UcnUMWEBCg7777TuvWrdPPP/8sY4xq1Kihli1b3uz+AQAAAMBdK1sjZBs2bFCNGjUUFxcnSWrVqpWGDBmioUOHql69errvvvu0efPmW9JRAAAAALjbZCuQTZ06Vf369VPRokUzrPP29taAAQM0efLkm9Y5AAAAALibZSuQff/992rdunWW60NCQrR79+4b7hQAAAAA5AXZCmQnT57MdLp7J3d3d50+ffqGOwUAAAAAeUG2AlnZsmX1448/Zrn+hx9+UJkyZW64UwAAAACQF2QrkLVt21YvvfSSLl26lGFdYmKixo4dq/bt29+0zgEAAADA3Sxb096/+OKL+uyzz1S1alUNHjxYgYGBcjgc2rdvn9555x2lpaXphRdeuFV9BQAAAIC7SrYCma+vr7Zu3aqBAwdq9OjRMsZIkhwOh0JDQzVjxgz5+vreko4CAAAAwN0m2xeGLl++vFavXq2YmBj9+uuvMsaoSpUqKl68+K3oHwAAAADctbIdyJyKFy+uevXq3cy+AAAAAECekq1JPQAAAAAANw+BDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAm9gayL755ht16NBB/v7+cjgcWr58uct6Y4zCw8Pl7+8vLy8vNW3aVD/99JNLm6SkJA0ZMkSlSpVSoUKF1LFjRx05csSlTUxMjMLCwuTt7S1vb2+FhYXp/PnzLm0OHz6sDh06qFChQipVqpSGDh2q5OTkW/GyAQAAAECSzYHswoULql27tqZPn57p+gkTJmjy5MmaPn26du7cKT8/P7Vq1Urx8fFWm2HDhmnZsmVatGiRtmzZooSEBLVv315paWlWm549eyoqKkoRERGKiIhQVFSUwsLCrPVpaWlq166dLly4oC1btmjRokX69NNPNWLEiFv34gEAAADkee52brxNmzZq06ZNpuuMMZo6dapeeOEFde7cWZI0b948+fr66qOPPtKAAQMUGxur2bNna/78+WrZsqUkacGCBQoICND69esVGhqqffv2KSIiQtu2bVP9+vUlSe+//76Cg4O1f/9+BQYGau3atdq7d6+io6Pl7+8vSZo0aZL69OmjV199VUWLFr0N1QAAAACQ1+Tac8gOHjyoEydOKCQkxFrm6empJk2aaOvWrZKk3bt3KyUlxaWNv7+/goKCrDaRkZHy9va2wpgkNWjQQN7e3i5tgoKCrDAmSaGhoUpKStLu3buz7GNSUpLi4uJcbgAAAABwvXJtIDtx4oQkydfX12W5r6+vte7EiRPKnz+/ihcvfs02Pj4+GZ7fx8fHpc3V2ylevLjy589vtcnMa6+9Zp2X5u3trYCAgGy+SgAAAAB5Wa4NZE4Oh8PlvjEmw7KrXd0ms/Y5aXO10aNHKzY21rpFR0dfs18AAAAAcKVcG8j8/PwkKcMI1alTp6zRLD8/PyUnJysmJuaabU6ePJnh+U+fPu3S5urtxMTEKCUlJcPI2ZU8PT1VtGhRlxsAAAAAXK9cG8gqVqwoPz8/rVu3zlqWnJysTZs2qWHDhpKkOnXqyMPDw6XN8ePHtWfPHqtNcHCwYmNjtWPHDqvN9u3bFRsb69Jmz549On78uNVm7dq18vT0VJ06dW7p6wQAAACQd9k6y2JCQoJ+/fVX6/7BgwcVFRWlEiVKqFy5cho2bJjGjx+vKlWqqEqVKho/frwKFiyonj17SpK8vb3Vt29fjRgxQiVLllSJEiU0cuRI1axZ05p1sXr16mrdurX69eun9957T5LUv39/tW/fXoGBgZKkkJAQ1ahRQ2FhYZo4caLOnTunkSNHql+/fox6AQAAALhlbA1ku3btUrNmzaz7w4cPlyT17t1bc+fO1bPPPqvExEQNGjRIMTExql+/vtauXasiRYpYj5kyZYrc3d3VrVs3JSYmqkWLFpo7d67c3NysNgsXLtTQoUOt2Rg7duzocu0zNzc3rVq1SoMGDVKjRo3k5eWlnj176s0337zVJQCAO9r0EZ/b3QVbDJ7Uwe4uAADuErYGsqZNm8oYk+V6h8Oh8PBwhYeHZ9mmQIECmjZtmqZNm5ZlmxIlSmjBggXX7Eu5cuX0xRdf/GWfAQAAAOBmybXnkAEAAADA3Y5ABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgAAAAA2IZABAAAAgE0IZAAAAABgEwIZAAAAANiEQAYAAAAANiGQAQAAAIBNCGQAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2CRXB7Lw8HA5HA6Xm5+fn7XeGKPw8HD5+/vLy8tLTZs21U8//eTyHElJSRoyZIhKlSqlQoUKqWPHjjpy5IhLm5iYGIWFhcnb21ve3t4KCwvT+fPnb8dLBAAAAJCH5epAJkn33Xefjh8/bt1+/PFHa92ECRM0efJkTZ8+XTt37pSfn59atWql+Ph4q82wYcO0bNkyLVq0SFu2bFFCQoLat2+vtLQ0q03Pnj0VFRWliIgIRUREKCoqSmFhYbf1dQIAAADIe9zt7sBfcXd3dxkVczLGaOrUqXrhhRfUuXNnSdK8efPk6+urjz76SAMGDFBsbKxmz56t+fPnq2XLlpKkBQsWKCAgQOvXr1doaKj27duniIgIbdu2TfXr15ckvf/++woODtb+/fsVGBh4+14sAAAAgDwl14+QHThwQP7+/qpYsaIee+wx/f7775KkgwcP6sSJEwoJCbHaenp6qkmTJtq6daskaffu3UpJSXFp4+/vr6CgIKtNZGSkvL29rTAmSQ0aNJC3t7fVJitJSUmKi4tzuQEAAADA9crVgax+/fr68MMP9eWXX+r999/XiRMn1LBhQ509e1YnTpyQJPn6+ro8xtfX11p34sQJ5c+fX8WLF79mGx8fnwzb9vHxsdpk5bXXXrPOO/P29lZAQECOXysAAACAvCdXB7I2bdqoS5cuqlmzplq2bKlVq1ZJunxoopPD4XB5jDEmw7KrXd0ms/bX8zyjR49WbGysdYuOjv7L1wQAAAAATrk6kF2tUKFCqlmzpg4cOGCdV3b1KNapU6esUTM/Pz8lJycrJibmmm1OnjyZYVunT5/OMPp2NU9PTxUtWtTlBgAAAADX644KZElJSdq3b5/KlCmjihUrys/PT+vWrbPWJycna9OmTWrYsKEkqU6dOvLw8HBpc/z4ce3Zs8dqExwcrNjYWO3YscNqs337dsXGxlptAAAAAOBWyNWzLI4cOVIdOnRQuXLldOrUKb3yyiuKi4tT79695XA4NGzYMI0fP15VqlRRlSpVNH78eBUsWFA9e/aUJHl7e6tv374aMWKESpYsqRIlSmjkyJHWIZCSVL16dbVu3Vr9+vXTe++9J0nq37+/2rdvzwyLAAAAAG6pXB3Ijhw5oh49eujMmTMqXbq0GjRooG3btql8+fKSpGeffVaJiYkaNGiQYmJiVL9+fa1du1ZFihSxnmPKlClyd3dXt27dlJiYqBYtWmju3Llyc3Oz2ixcuFBDhw61ZmPs2LGjpk+ffntfLAAAAIA8J1cHskWLFl1zvcPhUHh4uMLDw7NsU6BAAU2bNk3Tpk3Lsk2JEiW0YMGCnHYTAAAAAHLkjjqHDAAAAADuJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALCJu90dAIDcYlPjJnZ3wRZNvtlkdxcAAMizGCEDAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALCJu90dAAAAAHBnq730S7u7YIvvHw294edghAwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAm7nZ3AMDN12haI7u7YItvh3xrdxcAAACyhREyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmBDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbuNvdAeCvHH65pt1dsEW5l360uwsAAAC4xRghAwAAAACbEMgAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIAAAAAMAmBDIAAAAAsAmB7CozZsxQxYoVVaBAAdWpU0ebN2+2u0sAAAAA7lJcGPoKixcv1rBhwzRjxgw1atRI7733ntq0aaO9e/eqXLlyN/z8df794U3o5Z1n98TH7e4CAAAAkCsxQnaFyZMnq2/fvnryySdVvXp1TZ06VQEBAZo5c6bdXQMAAABwF2KE7H+Sk5O1e/duPffccy7LQ0JCtHXr1kwfk5SUpKSkJOt+bGysJCkuLi7T9mlJiTept3eWrOpxveIvpd2kntxZbqRuqYmpN7End44b3dcupFK37EpMungTe3LnuNF97VJKyk3qyZ3lRuqWcOnCTezJneNG97Urv6fkJTdSt4sX+d6RE2kX+RvNap0x5prP4TB/1SKPOHbsmMqWLatvv/1WDRs2tJaPHz9e8+bN0/79+zM8Jjw8XOPGjbud3QQAAABwB4mOjtY999yT5XpGyK7icDhc7htjMixzGj16tIYPH27dT09P17lz51SyZMksH2OHuLg4BQQEKDo6WkWLFrW7O3cM6pZ91CxnqFv2UbOcoW7ZR81yhrplHzXLmdxcN2OM4uPj5e/vf812BLL/KVWqlNzc3HTixAmX5adOnZKvr2+mj/H09JSnp6fLsmLFit2qLt6wokWL5rod9U5A3bKPmuUMdcs+apYz1C37qFnOULfso2Y5k1vr5u3t/ZdtmNTjf/Lnz686depo3bp1LsvXrVvncggjAAAAANwsjJBdYfjw4QoLC1PdunUVHBysWbNm6fDhw3rqqafs7hoAAACAuxCB7Ardu3fX2bNn9fLLL+v48eMKCgrS6tWrVb58ebu7dkM8PT01duzYDIdX4tqoW/ZRs5yhbtlHzXKGumUfNcsZ6pZ91Cxn7oa6McsiAAAAANiEc8gAAAAAwCYEMgAAAACwCYEMAAAAAGxCIAMAAAAAmxDIkEFaWprdXQAAAADyBGZZhCRpz549kqSgoCBrmTFGDofDri7dcZYtW6aEhASVKVNGLVu2tLs7dwRqljPULfuoWc5Qt+yjZjlD3bKPmt18dn33ZYQsjzPG6Pz58xowYICeeuopNWzYUMuWLdORI0fkcDiUnp5udxfvGEeOHNG6dev0xBNPqHv37lq6dKlSU1Pt7lauRs1yhrplHzXLGeqWfdQsZ6hb9lGzG7dx40bVrVtXr7/+uiIjI20biGCEDJKk2NhY5cuXT8OGDdPBgweVnJysyZMn66GHHlJ6erry5SO7X6/ffvtN//73v3XmzBkFBATogw8+uKMvVng7ULOcoW7ZR81yhrplHzXLGeqWfdQs51JSUvTWW2/pyJEjmjlzpgYNGqS2bduqVatWt7UfBLI86uoh2bS0NLm5uUmSNmzYoNmzZ+vTTz/VF198oZYtWxLKMnFlDZ31cdbxwoULWrx4sWbOnKmCBQtq7dq1vDmKmuUUdcs+apYz1C37qFnOULfso2Y319XfhVevXq1JkyYpNTVVnTt31r/+9a/b2hnkMampqcYYY9LS0kxycnKmbY4ePWoGDBhgPD09zfbt240xxqSnp9+2PuZ2zhomJSWZw4cPmx9//DFDm+TkZLNmzRpTt25d0717d5OSknK7u5mrULOcoW7ZR81yhrplHzXLGeqWfdTs5khLS7vmsr1795p//etfJigoyEyaNOm29YtAlsc4d7q4uDjTtWtX06JFC9O4cWMzffp0c+zYMZe2J06cMD179jQNGzY0f/zxhx3dzZWcwTQ2NtbUr1/f1KxZ0+TLl8+0adPGzJ0716VtSkqKmTt3rvnb3/5mFi5caEd3cwVqljPULfuoWc5Qt+yjZjlD3bKPmt0czlB74cIFs3jxYjNr1iyzfft2k5iY6NLu8OHDZuTIkebhhx82ERERt6VvHIOWx+TLl08XL15U3bp1FR8fr06dOsnX11dz5sxRr1699Ntvv0m6PIzr6+urf/7zn/Ly8tK3334rSUzyIcnhcCg5OVmtWrXSPffco1mzZlkngr777rsaPXq01dbd3V1dunSRr6+vPv30Uxt7bS9qljPULfuoWc5Qt+yjZjlD3bKPmt04Y4zc3NwUHx+vBx98UC+//LLefPNNBQcHa9iwYdq8ebPVNiAgQE8++aQKFCigL774wnr8re4g8piPPvrI1K9f31y4cMFatnTpUtOyZUtTp04d8/vvv7u0HzhwoKlTp87t7mau9sMPP5iqVau6HDJw8uRJM3r0aFOnTh0zduxYl/Z//PGH8fHxMatWrbrNPc09qFnOULfso2Y5Q92yj5rlDHXLPmp241JTU0337t3NI488Ys6fP2+MMeazzz4z9erVM+3atTNr1qxxaR8REWE8PT3Nli1bbnnfGCHLg2JjY/Xbb78pMTHRWtalSxeNHDlSxYsX17///W+dO3fOWjdx4kR5eXlp+/btdnQ3VypQoIDi4uJ08OBBSZcnRfHx8dG///1vNW3aVGvXrtVXX30l6fIMPuXKlVP79u119uxZO7ttK2qWM9Qt+6hZznh6elK3v2Cu+pWcmuUMdcs+3tduXL58+XTkyBHVrVtX3t7ekqRHHnlEb775pi5cuKD33ntP//d//yfp8t96aGioBgwYoC+//NJadsv6dsueGblWlSpV5OPjo127drnsXKGhoXrsscf0008/6ZdffpF0+RDF9PR01alT59YP195BihcvruLFi1tD2W5ubkpPT1fx4sX13HPPKTY2Vp999pkkycPDQ5L097//XbGxsbb1+XbKbF+hZn8ts0OCS5QooRIlSlC3LKSlpWVYRs3+Gvta9qWlpVmHjjlfMzX7a5l9HhQrVozPg2vgM/TmS09P14ULF5QvXz7FxcVJkpKTkyVJjRs31pgxY7Rnzx4tW7ZM0p//DypWrKiff/5Zkm7tNcpu+RgcbHP1rIjO+2lpaaZRo0amTp065uDBgxkeV6FCBTNq1CiXZSdOnDAxMTG3qqu5Vmaz8TitWbPGuLm5mddff90Yc7m+zhqPGzfOPPTQQyYpKcnlOa4+cfRu5Hy9aWlpJj4+3qSkpFh1Wb16NTXLgrMOFy9ezHB4REREBHXLhPME7fPnz5tx48YZY/6sIzXLmrNu8fHxZty4cWbfvn3WjLvULXNX1iw0NNS8/fbb1jpqlrUrZwb86aefzK+//mp9l1i1ahV1y4SzZlfWw+mLL76gZtcpq5nBJ0yYYDw8PMz3339vjLk8M6Wz7bvvvmuKFi1qTpw44fKYJUuWuJzmcyswQnaXuvKXvEOHDkm6nOxTU1OVL18+ff7554qJidHjjz+uAwcOuDw2KChIZcuWdVnm6+urYsWK3abe5w5paWnKly+fEhISNHLkSPXo0UM9evTQvn37JEmtW7fWlClT9Pzzz+uVV17RpUuXrF9Pzp49q4CAALm5ublcv61AgQK2vJbbxVmz+Ph49erVS+3atVPDhg21YsUKpaWlqU2bNtQsC86/1wYNGuhf//qX9SuddHn0mrq5cl57Jy4uTjVr1tR3330n6XIdzf8ONZk8eTI1u4q54sT2OnXqaMeOHZJkXYcyNDRUkyZNom5XuHJfCwoK0tq1a7V06VJrPX+fmUtPT7f2tdDQUHXt2lUtWrTQCy+8oPPnz6tt27bU7SpX1uwf//iHOnTooBYtWmjmzJk6duyY2rVrp4kTJ1Kzv3Dld+Do6Gjt2bPHWvfvf/9brVu3VqtWrXTgwAF5eHhYo2HVq1eXr6+vdQSBc3mXLl1UsGDBW9vpWxr3YAtn0o+Pjzf16tUzYWFhZt++fdZ653UpfvvtN1O5cmVTs2ZN884775hNmzaZGTNmGC8vL/P111/b0vfcwlnDuLg4U61aNdO6dWvz9NNPm/vuu8/UqFHDxMfHG2Mu//L03nvvGQ8PD9OpUyczZMgQ89JLLxkPDw+zcuVKO1/CbXdlzapWrWo6dOhg5syZY0JCQkylSpXMxYsXjTHGXLp0ycycOZOaZeLgwYPG19fXBAcHm/bt25tly5ZZ6+Lj46nb/zh//Y2NjTXlypUznTp1yrRdeno6NctEcnKyad26tUvdEhISTEJCgjVS9s4771A38+dohXNf69Gjh9m5c6cpW7asmT9/vtUuKSmJz4JMJCQkmOrVq5uuXbuaXbt2mVdeecVUrVrVHDhwwBhz+fvIjBkzqNsVEhISTNWqVU379u3N3LlzTa9evUzlypVN69atTXR0tDGGv89rudYlAubNm2eMufxZ26JFC1OqVCmzefNmk5SUZIwxZvr06aZq1arm6NGjt73fBLK71KVLl0yHDh1MhQoVTLly5cygQYPMzz//bK13fqFJTEw03bp1M/fff7/x8fExNWrUMIsXL7ar27lKUlKSadu2rXnkkUdchv/LlStnJkyY4NJ2x44dpmfPnubhhx82nTp1MitWrDDG5L2LaV+6dMk0b97cdOnSxfoic+rUKdOwYUMTHR1tLl26ZH3hi4yMpGaZCAsLM/PmzTMtW7Y0rVq1MsuXLzfGGOswn+3bt1M3c/m9q0qVKqZVq1bWslmzZpnnn3/e/POf/zRr1661ZtHatm0bNbvC+fPnTZMmTcx3331njDFm5MiRJiQkxNx3333miSeesL4sb926lbqZy/UqV66c6dq1qzHGmDNnzpi6deuavn37ZmjLZ4GryZMnmxYtWrhcoLhx48bmm2++MXv37jVnzpwxxrCvGfPna/3Pf/5j2rVr57IuLCzMOBwOU79+fetUE97XspaUlGQeeugh06VLFxMZGWm2b99u2rZtax566CHzwgsvGGOMOXLkiOnRo4fJly+fadCggQkNDTVeXl5myZIltvSZQHaXioyMNK1btzbffvutWbx4sbnnnnvMwIEDXUKZ84txenq6OXbsmNm/f7/1q0Bmxy7nNZs3bzYNGjQwO3bsMMb8Wa+///3v5qWXXrLaOcPapUuXjDHGOs44L9Zw69atZvDgwWbv3r3Wsrlz55qiRYua2rVrmwceeMAMHjzY2s+o2Z/S0tJMenq6efjhh83nn39u9u/fb1q0aGH+/ve/m06dOpmyZcuahIQEY4yxfs3Ly3Vbu3atqV69uunYsaNJSkoyAwYMMEFBQaZVq1amZs2aplq1ambUqFHm7Nmzxhj2Naf09HTz/fffG29vb3Ps2DEzZswYU7NmTTN37lzz/PPPm5CQEBMYGGh++eUXY8yf55/k5boNHDjQPPLIIy7LPv74Y+Pu7u5yvqfzs4C/zz+NGzfO1KpVy/pBacmSJcbhcJj77rvPVK1a1QQFBVmfF+xrlw0dOtQ88sgjJjU11arJggULTGhoqGnevLnp27eviY2NNcawr2XlWpcIeOCBB8zLL79sLf/kk0/M2LFjzcsvv2w2bdpkjLEn1BLI7lLnzp0z69evtw4TuzKUXXn4onMUAxmlpaWZsWPHWl+CnbXq16+fGThwoNXmynXG8OvU999/b9Xjiy++MA6Hw4wZM8Zs2rTJvPrqq6Z27dpmzpw5xpg/65fXa2bMnzV49dVXzauvvmqMMebYsWOmUqVKpkCBAmbEiBFWW+evzXm5bunp6eaTTz4xTZo0MUWKFDEPPvig2b9/v7XvvfDCC+bee+8127ZtM8awr10pNTXVNG/e3Lz00kumTZs2Zv369da6bdu2mRYtWphx48aZtLQ0lwkG8qorj5Bw/nASHR1tGjVqZJ599lmTlpbm0sZZq7xcM6cPPvjA1K1b13Ts2NGMHTvWOBwOM2nSJPPHH3+YLVu2mLZt25rHHnvMXLx4kX3tf4YOHWpq1apl/ZhkjDG1atUy4eHhZuLEiaZChQouP55f+V9c9ssvvxg/Pz/rEE7nvnXu3DkzYsQIU79+fbN27dosH29HPZnU4y5VvHhxtWjRQl5eXkpPT1e3bt00efJkrVy5Um+//bb2798vSVqyZIk2bdpkc29zn/T0dOXLl0/h4eEqVKiQdaKtk3Ma2Xz58mnNmjX64osvrJNAb+m0qLmY+d/Jr7Vq1ZKbm5uMMfLw8NC8efP08ssvq3Hjxnr++eclSRs3bpQk68TjvFqzKzlrUKxYMa1bt06S9PHHH+vIkSOqWrWq9u/fb00k4O7u7vKYvMYYI4fDoS5duuipp55S27Zt9Z///EdVq1a1avLKK68oJiZGERERktjXruTm5qZq1app3rx52r17t0qWLGmtq1+/vooVK6bvvvtO+fLls9738mrdnJ8FTvny5ZPD4dA999yjRo0aae7cudZU2s73QGet8mrNrtS7d2/16NFD9957rw4fPqywsDANHz5c5cqVU6NGjazlXl5eeX5fc3r11VcVGxur4OBgde3aVQEBASpRooTGjh2rkSNHKjY2Vlu3bpXEvpaVv7ocRXx8vJYvX+7yGHPFpQbsqCeBLA9w7lhdu3bVlClT9Pnnn2v69On697//rccff1xJSUk29zD3ufID2Hnfeb2j/Pnzq0iRIpKkBQsWqF27dipQoECGx+Q1V7+BORwOhYSEKCwsTJKUmpqq5ORk1ahRQw8++KAdXczVnB8G9erVk7+/vyZMmKAXX3xRERERWrRokc6cOaP33ntPJ0+etLmn9nPOpOhwOPTYY49p5MiRevjhhyVd/ltNTU3VmTNnVKVKFd1333029zZ3ce5nb731lipXrqzTp0/ro48+0oULF6w2lStXVoUKFZSWlpbnrz+Z2fu688e3UaNGydvbW6+//rokvhRfzRlmhw8frilTpqh8+fK6ePGiS5vixYvL399fFy9ezPP7mnR5dsDChQvrhx9+UGhoqO655x4NGDDA+hFz//79KlmypCpUqGBvR3OJzK6nmJ6erpIlS2ry5MmaPXu23njjDUl/fm6UKlVK3bt3165du6zrkDnX28nd1q3jtrjyy0vXrl3l5uambt26KT09XYsWLVJISIjdXbyjpKWlqVixYlq+fLn69OmjhQsXKjQ01O5u5UrO/U66PKozb948bd68WUOGDLG5Z7mPs0733nuvVq1apcWLF+uzzz5T06ZNJUnvvvuuLl26JF9fXxt7mXtc+b5Wt25dl3Xu7u5atWqVzp49q8qVK9vUw9zJ4XAoLS1N7u7uioiIUGhoqD7++GOdO3dObdq00e+//65p06Zp2bJlLkcF4E/OkFaoUCE9/PDD2rBhg1JTU62Ra1x2dZj19vbWr7/+qq+//lp+fn6KjIzU5MmT9cknn9z6KcXvEG5ubkpLS1PRokX19ttvZ1i/ZcsW5cuXT97e3jb0LndxBv7ExERFRkYqODhYXl5e1mi183IUw4YNU0pKikaMGCEvLy9JrpcIyDVu+0GSsI3zmNiZM2cad3d388UXX1jLOf74+g0fPtwULFjQuLm5mQULFhhjqOFf2bFjh5k2bZqtMxjdCZz70PLly83mzZszLMdf27Ztm3nrrbeMp6en+eSTT+zuTq7lPA8xNTXVPP/886Zp06bmnnvuMQ0bNjRLly41xrDfXYuzNjt27DAOh8NlwixkLiUlxTRo0MCUKVPGVKhQwVSpUsX6PGBfu7ZffvnFvPrqq8bT05PPUPPn/pKQkGCqVKliChQoYD777DNrkhOnO+lyFA5jGCPOSw4cOKCaNWtq1qxZevzxxzMc846smf/9Gj906FBNnz5dX3zxhdq2bUsNr8O4ceO0evVqvfDCC+rYsaPLyBkyuvq8FVy/559/Xp9//rn+85//qFOnTuxr1+C84LHz32fOnFGBAgXk7e3N+9p1MMYoKSlJx44d07333mt3d3I15whiWlqaVq1apUKFCsnf31/Vq1dnX7sOmzdv1kcffaQ2bdrwGfo/qampGjBggI4fPy4PDw9t2rRJs2fPVocOHZQ/f36Xtjt37tTUqVN1+PBhlSpVSk888USuqyOBLA86evSoypYty5tgDqWmpmrr1q1q3LgxNbxO6enpio6OVvny5akZbqn09HQdOnRI9957L/vadchNX0judNTy2q78AQDZFxcXp6JFi/K+9j9Hjx7V9OnTdd9996lXr17q3bu3li9frg8++MAllDn3u0uXLqlAgQK6ePGiChYsmOvqSCC7g2T2Zn8jv6TnxQ+PrOp1vR8UV58nkBdqeKM1y6ujPTdat7yIfS1n2Neyj5rlDHXLPmp26+zbt0/lypVToUKFJElhYWFauXJlhlDmDGFS7v3eRiC7Qzj/cJOTk/XHH3/I4XCoXLlyGYZlkTVnDRMTE7Vy5Uqlp6erdOnSatmypd1dy7WoWc5Qt+yjZjlD3bKPmuUMdcs+anZ7pKSkyMPDQ9KfoWzOnDlq3769PvzwQ+3du1evv/56rv7OTCC7AzjTfFxcnNq0aaNTp04pOTlZJUqU0MyZM/XAAw/I09PT7m7mas4axsfHq27duipatKiio6Pl7u6uevXqadasWSpdunSmj8mrqFnOULfso2Y5Q92yj5rlDHXLPmp2e1054vj4449r9erVat26tT766CN99tln6tSpk70d/Cs3fZoQ3BKXLl0yDz30kOnatavZtm2bWbNmjenUqZMpUqSImTVrlomLi7PapqWlGWOMiYmJsf6Ny3V55JFHTNu2bU1iYqI5fPiw+eqrr8y9995rHnroIXPgwIEMj/n999/N+fPnbeht7kDNcoa6ZR81yxnqln3ULGeoW/ZRs9srNTXV+ndQUJBxOBx3zKyxBLI7xC+//GICAwPNjh07XJb/61//MoULFzbz5s0zxvy5w0VFRZmaNWua33//3WV5XpaammqaNWtmZsyY4bL86NGjplq1aqZRo0bm0qVLVtszZ84YLy8vEx4ebkd3cwVqljPULfuoWc5Qt+yjZjlD3bKPmt1+qampZvLkycbhcJgVK1YYY+6MSxPlvbOf7yDmf0eTGmN0/vx5RUdHq0iRIpKkpKQkSdLUqVP1+OOPa+jQoTp06JA11F2lShUdOnRIM2fOlJR7ZpGxk8Ph0IkTJxQVFWUtS0tLk7+/v1avXq1Dhw5ZFyx2c3NTyZIl9frrr+vLL7/U0aNHbeq1vahZzlC37KNm2Wf+d3gTdbs2c8WZGenp6dTsOqWnp1v/Zl/LGWp2+yUmJurnn3/WvHnzrKntpdz/PZhAlku9//77+vDDD60Pj3r16qlGjRp69tlnZYyRp6enkpOTJUnvvPOOatasqTFjxsgYo5SUFBUsWFDz589XcnKyEhMTbX419nPOcjR48GB98803+vTTTyVdfgNMTU1VxYoVNXbsWO3YscPlTbBly5YKDg62gvDdzFx1Oqkxhppdh6vrJom6/YW0tDSX+6mpqdTsOjjrlpaWZn1Bpm7XlpaWJofDoZSUFKWlpSlfvnzU7Do4a3Xx4kUdPXqUfe06XBlgnajZ7Ve4cGFNnTpVYWFhd0wYkwhkudInn3yiAQMGqGTJksqXL59SU1MlSSNGjNCRI0c0atQoGWOUP39+paSkSJJq166tM2fOyOFwWDPN1K1bVy+++KK8vLxsey12uPrLnvODRZJatGihKlWqaPbs2YqIiJAkaxr7MmXK6OTJky5vqjVq1FB4eLiKFi16m3pvD+eXltTUVJ05c0Znz5616tCkSRNqlgVn3ZKTk/XTTz9p165d1ug1dcucMUZubm6Kj4/Xe++9J+nPuvD3mTXnCeuxsbHq3bu39uzZY62jbplLT0+39rUePXpo1qxZVh2oWdac+1pCQoKqVKmiNm3aWJ+r1C1zzu8ZztkU33vvPZ06dUqS1LRpU2p2nTILtVLG73V/xfm91+Fw3BFhTCKQ5TqJiYn64IMPFBgYaC1z/uG2a9dObdq00caNGzV8+HBJssJX4cKF5eXlpaSkJGuHLlu2rEqVKnWbX4G9nB/ACQkJGjhwoA4dOiQ3NzfrjzkwMFCjRo3ShQsXNHXqVH344YeSLn9JPHr0qEqXLm3N0uP8ZeVu/5Xqyi8t3bt3V0hIiJo3b66hQ4fq0qVLuu+++/Tvf/+bml3FWbe4uDi1bt1ajz32mDp37qw2bdooMTFR9913n5599lnqlon09HS1aNFCAwcO1KhRo6zlgYGB1CwTzi/IcXFxuu+++3T+/HnVrFnTWh8YGKiRI0dSt6vky5dPCQkJevDBB+VwONS0aVPr8zEwMFAjRozQhQsXNGXKFGr2P1fva/nz55cxRt98840k/kYz4/yRKS4uTo0aNdKYMWP04osvqmbNmvrjjz+so5uo2bVdGWoXL16sjz/+WOvXr5ekvHG9tlt+lhqy7bfffjMdO3Y0zZo1MwsXLnRZFxMTY15++WVTs2ZNU7duXTNhwgQzYsQI4+HhYT7//HObepy7XLhwwTz00EPG4XCY5s2bm0OHDhljjElJSbHaREZGml69epnSpUub2rVrm7Zt2xovLy+zePFiu7ptC+dJrvHx8SYwMNB07tzZfPrpp+all14ywcHBZvr06VZbavanq+vWrVs3s2PHDrN06VJTuXJls2rVKqvt5s2bzT/+8Q/qdpWBAweaXr16mWLFiplBgwa5rNu1axc1+x/nvhYXF2cqVqxoHn30UWvdxYsXTXx8vPXexr7mKj093QwePNh06NDBWnbgwAETGRlpYmJijDHG7N692/zjH/8wpUqVyvM1c87KHBsba8qXL2969uxpEhISTNWqVc0TTzzh0pbPA1eJiYmmXr165vHHHzfHjh0zp0+fNg888ICZOnWq1SYyMpJ9LQtXvs9VrVrV1K1b1/j6+pqyZcuaTp06mVOnTmX5mLsF1yHLpQ4ePKghQ4bo4sWLevLJJ9WzZ09r3cWLF/Xdd99p6tSpOnr0qEqUKKEBAwZYJy/eKcOzt0JaWpqef/55fffdd2rfvr2++OILJScna968eapQoYJSU1OtEcfjx4/r119/1aJFi1ShQgXVq1dPTZs2zXM1TE1N1YABA3T27Fl98skn1qhr165dlZKSouXLl1ttqdmfUlJS9Oijjyp//vxauHChdcHJkJAQDRo0SN7e3qpVq5ZKliypY8eO6bfffqNu+nNygIEDB8rLy0stWrRQly5d1L9/f7399tv66quvVKdOHSUnJ2v//v3UTJcncbr33ntVunRpa3KAl19+Wbt27dLRo0dVtmxZzZo1S35+foqOjtahQ4eomy7va23bttXf//53PfXUU3ryySe1fft2HT9+XJL0yiuvqH///jp37pz27dtHzSQlJCQoICBArVq10pIlSyRJc+fO1fPPP6+lS5eqYcOGVls+D/60bds2DRw4UEuXLlWlSpUkSb169dL999+vtLQ0NWvWTA899JDOnTunPXv2aPHixXm+ZldLT0/Xo48+qqSkJH366ac6ffq0Dhw4oH79+qlUqVJauHChKleu7PKYgwcPqkSJEvL29rap1zeRLTEQ1+X333837dq1y3SkzCklJcUkJSUZY+6MaT1vh/fff9+88cYbJjU11Xz++eemefPmpnHjxubgwYPGGNeRsqvlpRo6X+epU6dM//79zfvvv2+M+bM+n332mfnb3/5mkpOTqVkmUlJSzJQpU8yaNWusZUuXLjXu7u6mevXqJjAw0JQsWdJERUVl+vi8Wjfnr/CLFy82w4YNM8YYs2jRIuPl5WXq1q1r/P39zd69ezN9bF6tmTHGdO3a1fj4+Ji1a9eaHj16mKCgIBMeHm7GjBlj6tevb8qWLWtOnjyZ6WPzUt2ufJ2JiYmmdevW5tNPPzUffPCBqVmzpvnmm2/Mb7/9Zp599llTunRps2DBgiyfJ6/UzJg/6/bVV1+ZF154wWXdDz/8YCpUqGAmTZpkjOEzNDMrV640bm5u5rfffjPGGLN69Wrj5uZmmjVrZh588EFTuHBhM23atEwfm1drdrW8fokAAlkul1UoS05Otv6d1/+QZ82aZebOnevyIZGYmGj9e/ny5aZFixamcePG1uGLqamp5uzZs7e9r7mFs2bp6ekmKSnJrF692ly8eNGlzZIlS0xQUJDLhRadX6bz6j7nrJuzJklJSda/t23bZvz8/MykSZPMwYMHzfnz503r1q1NgwYN8my9jPmzZldfpH7Tpk0mKCjI2u+6d+9u3N3dTevWra02efnC9s66OX9wM8aYnj17GofDYYKDg10uKBsdHW1q165tevfunae/3GW2r/Xp08dUrVrVDBs2zOUQbGOMGTRokKlUqZLLe1xe5KybMa4X1r1yP3ruueeMr6+vOXbs2G3vX2509WdBenq6adiwoSlcuLDp3r27yZcvn5k5c6a5cOGCMcaYF154wZQuXdqcPn3azm7namlpaaZ69eqmf//+1jJnfX///XdTtmxZ069fP5fHvPXWWyY4ONgcOXLktvb1VmBSj1yuYsWKmjZtmgoWLKj//ve/+uijjyRdnszD3EHTed4qV85I6e7ubs1IWaBAAWsij7///e8aMmSIPDw81Lt3b/3xxx+aMWOGGjdurPj4eDu7b4sra+ZwOJQ/f361adNGXl5eLlO4XznDpyTNnz9fnTt3ti7FkNdcWTc3NzdrplPnycYlS5bU/PnzNXz4cFWoUEHe3t5q1KiRUlJSXOqYl1w9Y6xz/3Jeh8fT01NeXl6aMmWKVq5cqWHDhmnr1q3q37+/JFmzo+Y1V9Ytf/781iVOFi5cqOeee06dO3dWpUqVrHrec889qlSpks6ePXtHzSp2M2U1O/GLL76oQoUK6a233tLFixclyZqduE2bNipYsKC1PC+6sm6S6/cJh8Nh7WNdu3ZViRIltGLFCklZz4aXF1z9WZCamiqHw6F169bprbfeUqdOndSoUSM99thjVj2d+9r58+ft7XwuYTI5WyqvXyIgb37a3WGyCmUHDx7M9lSgd5OsZqR0/qE7vzRLf4YyT09PBQcHa/jw4Ro5cuRd8UecHZnVTPrzw/XKD+MSJUrIy8tLbm5umjt3rp544gk9+uijefJLcmZ1u/LLijFGlStXVsuWLa370uXzPWvWrClz+WiE299xG12rZm5ubqpcubIqVqyokJAQPf/881q8eLEmTpyoGTNmaM6cOdq7d6+NvbdPZnW7MpSNHz9e/fv3t4KX82+3WLFiqlGjhqTMv+zcza41O3FAQIAGDx6sChUq6MMPP9SRI0es82R//fVXFS1a1LquW16TWd2u/OFE+vMz4cEHH1SlSpU0Z84cq11elNW+lpaWpoIFC+qf//ynChYsqGPHjqlYsWLW9Ou7du1S8eLFVahQIbu6nmtcebmYgwcP6uDBg9b7W4sWLVS5cmX997//zXuXCLjdQ3LIOefhi+3atTNdu3Y1JUuWNH/88Yfd3bJVVjNSXnmoxZX/DgsLMw6Hw6xcuTLDurziempmjDFffPGFefjhh837779v3NzczEcffZRpu7zieuvmNHfuXFOsWDGzdu3a29XFXCermqWkpJjExETTqVMn4+Pj4zJDbGpqap4/LOpadcvMvHnzTIkSJczXX399u7qY61xrduKLFy+ahQsXmqpVq5qyZcuavn37mgEDBpiCBQua5cuX29Tj3OF63tech4D++OOPpmjRotbhjXlVVjVz1uno0aOmevXq5pFHHjHLli0zr776qilSpIhZtmyZTT3OPa6cybNx48bm/vvvNzVq1DC1a9c2kZGRxhhjtm/fbho3bmxCQ0PNvHnzjDGX98d3333X1KxZ0xw9etRadjchkN1hfv/9d9O0aVPj7+9vlixZYnd3coWszrO7OpTNmTPHOBwO88knn1jL7rY/6Ot1PTVbunSpcTgcxuFwuISxvFozY66vblFRUea5554zxYoVs6YzpmaZT0508uRJs2vXriwfS92uva999913ZsSIEaZIkSJMnW2uva+lpqaa48ePm2eeecZ06dLF9OnTx5qQJy/vZ8Zc375mjDHHjh0zDRs2tL4452XXqtmlS5fMggULTN26dU3ZsmVNw4YNreCf1/c1Yy7/QHL//febnj17mt27d5tNmzaZ1q1bGx8fH2tysZ07d+a5S3gQyO5A0dHR1/wSkxddzwfKjBkzzIoVK6zlef2NMauaOX/B2rVrlwkMDHQZTczrNTPm2nVLSUkxa9euNUOHDnX5spfX63Y9M8Yio7/a11avXm2efPJJ67p37GvXHy6c96nZZddbt/Pnz9/uruVa13pfcwazP/74w5w5c8Zaxr5mzLp168zf/vY3lwlOli9fbhwOhylRooT5+OOPjTHGHD582HzzzTdm0KBBZsKECWbjxo3GmLs31HIdMtw1srp2W1pamstV3g2ToViudb27Cxcu6MSJEy6TB1Czy65Vt7S0NCUmJqpw4cLU7QpZ1Sw9PT3Pno9yPa61r6WmpurixYsqWrQo+9oVsqrZldehZL/L6Hr+Rg3Xy3Jxvd878KfZs2dr5MiROn78uAoUKCBJ+umnnzRt2jSdOnVKmzdv1s8//2xNNHOlu/l9jncj3DWymvzEzc3N5STQvDoLWWayqpkkFSpUyLrApXR3vgHm1LXq5ubmpsKFC0tiX7tSVjW7ehIBuLrWvubu7m6dzM6+9qesanblpE+EsYyu52+UfczV9X7vyMvef/99zZs3z5qErlGjRqpQoYLGjx+v2NhYxcTEqHPnzkpLS9P8+fNVpkwZq45Xfzbcze9zvCPhrpLVm+OhQ4fy9IyU13I9s3jerW+AN4LZT7OPmuUMdcs+apYz1C37+N6RtasvESBdnvm0Y8eOWr58uapWrarq1aurcuXKev/991WoUCHlz59fhw8flpS3vntwyCLuSs7DCCSpYMGC2rBhg7777juVK1fO5p7lXtQsZ6hb9lGznKFu2UfNcoa6ZR81c5WYmKjOnTvr0KFDmjhxotq3b2+tu3jxon799Vf98MMPKly4sDp16iRJunTpknr06KF27drpySeftKnnNrHjxDXgdmBGyuyjZjlD3bKPmuUMdcs+apYz1C37qJmra12O4mopKSlmzpw5pnjx4nlyJk9GyHBXO3LkiE6ePKk6derY3ZU7BjXLGeqWfdQsZ6hb9lGznKFu2UfNXF3PxCcHDhzQa6+9pmXLlundd99V9+7d7eyyLQhkAAAAAG6JrEKZuWLWznfeeUdVqlRRSEhInpxEhkAGAAAA4Ja5nstRSHf31PbXQiADAAAAcEtxPcqs5e1XDwAAAOCW4xIBWWOEDAAAAMBtwSUCMmKEDAAAAMBt4Rwpu3Dhgr799lvNnDkzT4cxiREyAAAAALcZlwj4E4EMAAAAAGzCIYsAAAAAYBMCGQAAAADYhEAGAAAAADYhkAEAAACATQhkAAAAAGATAhkAAAAA2IRABgC4a2zdulVubm5q3br1bd1ucnKyJk6cqAcffFCFChWSt7e3ateurRdffFHHjh27rX0BANxZuA4ZAOCu8eSTT6pw4cL673//q71796pcuXK3fJtJSUkKCQnRDz/8oHHjxqlRo0by9vbWb7/9puXLl6tYsWJ67bXXMn1scnKy8ufPf8v7CADIvRghAwDcFS5cuKAlS5Zo4MCBat++vebOnZuhzcqVK1WlShV5eXmpWbNmmjdvnhwOh86fP2+12bp1qxo3biwvLy8FBARo6NChunDhQpbbnTJlirZs2aINGzZo6NChqlOnjipXrqzQ0FDNnDlT48ePt9o2bdpUgwcP1vDhw1WqVCm1atVKkrRp0yY99NBD8vT0VJkyZfTcc88pNTXVelyFChU0depUl+3ef//9Cg8Pt+47HA7NnDlTbdq0kZeXlypWrKhPPvkke0UEANx2BDIAwF1h8eLFCgwMVGBgoHr16qU5c+boyoNADh06pEcffVSdOnVSVFSUBgwYoBdeeMHlOX788UeFhoaqc+fO+uGHH7R48WJt2bJFgwcPznK7H3/8sVq1aqUHHngg0/UOh8Pl/rx58+Tu7q5vv/1W7733no4ePaq2bduqXr16+v777zVz5kzNnj1br7zySrZrMGbMGHXp0kXff/+9evXqpR49emjfvn3Zfh4AwO1DIAMA3BVmz56tXr16SZJat26thIQEffXVV9b6d999V4GBgZo4caICAwP12GOPqU+fPi7PMXHiRPXs2VPDhg1TlSpV1LBhQ7399tv68MMPdenSpUy3+8svvygwMNBl2SOPPKLChQurcOHCatiwocu6ypUra8KECQoMDFS1atU0Y8YMBQQEaPr06apWrZo6deqkcePGadKkSUpPT89WDbp27aonn3xSVatW1X/+8x/VrVtX06ZNy9ZzAABuLwIZAOCOt3//fu3YsUOPPfaYJMnd3V3du3fXBx984NKmXr16Lo976KGHXO7v3r1bc+fOtcJU4cKFFRoaqvT0dB08eDDL7V89CjZjxgxFRUXpn//8py5evOiyrm7dui739+3bp+DgYJfnaNSokRISEnTkyJHrePV/Cg4OznCfETIAyN3c7e4AAAA3avbs2UpNTVXZsmWtZcYYeXh4KCYmRsWLF5cxJkNwunpeq/T0dA0YMEBDhw7NsI2sJgipUqWKfv75Z5dlZcqUkSSVKFEiQ/tChQpl6ENW/XIuz5cvX4a+pqSkZNqfq1393ACA3IURMgDAHS01NVUffvihJk2apKioKOv2/fffq3z58lq4cKEkqVq1atq5c6fLY3ft2uVy/8EHH9RPP/2kypUrZ7hlNRtijx49tG7dOv3f//1fjvpfo0YNbd261SVwbd26VUWKFLECZunSpXX8+HFrfVxcXKYjdtu2bctwv1q1ajnqFwDg9iCQAQDuaF988YViYmLUt29fBQUFudweffRRzZ49W5I0YMAA/fzzzxo1apR++eUXLVmyxJqJ0TmKNGrUKEVGRurpp59WVFSUDhw4oJUrV2rIkCFZbv+ZZ55RcHCwmjdvrrfeekvfffedDh48qC+//FJr1qyRm5vbNfs/aNAgRUdHa8iQIfr555+1YsUKjR07VsOHD1e+fJc/pps3b6758+dr8+bN2rNnj3r37p3p837yySf64IMP9Msvv2js2LHasWPHNSckAQDYj0AGALijzZ49Wy1btpS3t3eGdV26dFFUVJS+++47VaxYUUuXLtVnn32mWrVqaebMmdYsi56enpKkWrVqadOmTTpw4ID+9re/6YEHHtCYMWOsQxAzU6BAAX311Vd67rnnNGfOHD388MOqXr26hg0bpkaNGmn58uXX7H/ZsmW1evVq7dixQ7Vr19ZTTz2lvn376sUXX7TajB49Wo0bN1b79u3Vtm1bderUSZUqVcrwXOPGjdOiRYtUq1YtzZs3TwsXLlSNGjWup4wAAJtwYWgAQJ716quv6t1331V0dLTdXblhDodDy5YtU6dOnezuCgAgG5jUAwCQZ8yYMUP16tVTyZIl9e2332rixIkc0gcAsBWBDACQZxw4cECvvPKKzp07p3LlymnEiBEaPXq03d0CAORhHLIIAAAAADZhUg8AAAAAsAmBDAAAAABsQiADAAAAAJsQyAAAAADAJgQyAAAAALAJgQwAAAAAbEIgAwAAAACbEMgAAAAAwCb/D1fVFiJMdd1OAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Extract numerical values from the 'age' column\n",
+ "clean_diabetic_data['age_numeric'] = clean_diabetic_data['age'].apply(lambda x: int(x.split('-')[0][1:]))\n",
+ "\n",
+ "# Define age bins\n",
+ "age_bins = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
+ "\n",
+ "# Create a new column 'age_group' with the specified bins\n",
+ "clean_diabetic_data['age_group'] = pd.cut(clean_diabetic_data['age_numeric'], bins=age_bins, right=False)\n",
+ "\n",
+ "# Plotting the Age Distribution with bins\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "sns.countplot(x='age_group', data=clean_diabetic_data, order=clean_diabetic_data['age_group'].unique())\n",
+ "plt.title('Age Distribution of Patients')\n",
+ "plt.xlabel('Age Group')\n",
+ "plt.ylabel('Count')\n",
+ "plt.xticks(rotation=45) # Rotate x-axis labels for better visibility\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visual 2. Gender Distribution:\n",
+ "\n",
+ "Description:\n",
+ "- This visualization illustrates the gender distribution among diabetic patients in the dataset.\n",
+ "\n",
+ "Interpretation:\n",
+ "- The bar chart displays the count of patients for each gender category.\n",
+ "-It helps in assessing the balance between male and female patients.\n",
+ "\n",
+ "Relevance:\n",
+ "* Gender-specific Health Insights: Understanding the distribution helps in gaining insights into gender-specific health patterns.\n",
+ "* Support for Targeted Interventions: Healthcare interventions can be tailored based on the observed gender distribution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting the Gender Distribution\n",
+ "plt.figure(figsize=(8, 5))\n",
+ "ax = sns.countplot(x='gender', data=clean_diabetic_data)\n",
+ "\n",
+ "# Set the title and labels\n",
+ "plt.title('Gender Distribution of Diabetic Patients')\n",
+ "plt.xlabel('Gender')\n",
+ "plt.ylabel('Count')\n",
+ "\n",
+ "# Add percentage annotations on top of each bar\n",
+ "total = len(clean_diabetic_data['gender'])\n",
+ "for p in ax.patches:\n",
+ " height = p.get_height()\n",
+ " ax.text(p.get_x() + p.get_width() / 2., height + 0.2,\n",
+ " f'{height/total:.1%}', ha=\"center\")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visual 3. Number of Diagnoses visualization\n",
+ "Description:\n",
+ "- This visualization displays the distribution of the number of diagnoses for diabetic patients.\n",
+ "\n",
+ "Interpretation:\n",
+ "- The count plot shows the frequency of different numbers of diagnoses among diabetic patients.\n",
+ "- It helps identify common scenarios regarding the number of diagnoses.\n",
+ "\n",
+ "Relevance:\n",
+ "- Diagnostic Patterns: Understanding the distribution aids in identifying common patterns of diagnoses.\n",
+ "- Healthcare Planning: Healthcare resources and planning can be optimized based on prevalent diagnosis scenarios."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting the Number of Diagnoses Distribution\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "sns.countplot(x='number_diagnoses', data=clean_diabetic_data)\n",
+ "plt.title('Distribution of Number of Diagnoses for Diabetic Patients')\n",
+ "plt.xlabel('Number of Diagnoses')\n",
+ "plt.ylabel('Count')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## visual 4 - Medication Prescription Patterns for Diabetic Patients\n",
+ "\n",
+ "Description:\n",
+ "- This visualization is a stacked bar chart that shows the distribution of medication prescriptions for different types of diabetes medications among diabetic patients.\n",
+ "\n",
+ "Interpretation:\n",
+ "- Each bar in the chart represents a specific medication, and the height of the bar represents the count of prescriptions for that medication.\n",
+ "- The bars are stacked to show the breakdown of prescription status (e.g., 'No', 'Steady', 'Up', 'Down').\n",
+ "- Different colors within each bar represent different prescription statuses.\n",
+ "\n",
+ "Relevance:\n",
+ "- Identifying Commonly Prescribed Medications: By looking at the chart, you can identify which medications are frequently prescribed to diabetic patients.\n",
+ "- Understanding Prescription Trends: The breakdown of prescription status helps understand whether patients are mostly on a steady dose, have had a change in dosage (up or down), or if the medication is not prescribed ('No')."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 1400x800 with 0 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Selecting relevant medication columns\n",
+ "medication_columns = ['metformin', 'repaglinide', 'nateglinide', 'chlorpropamide', 'glimepiride', 'acetohexamide',\n",
+ " 'glipizide', 'glyburide', 'tolbutamide', 'pioglitazone', 'rosiglitazone', 'acarbose',\n",
+ " 'miglitol', 'troglitazone', 'tolazamide', 'examide', 'citoglipton', 'insulin',\n",
+ " 'glyburide-metformin', 'glipizide-metformin', 'glimepiride-pioglitazone', 'metformin-rosiglitazone',\n",
+ " 'metformin-pioglitazone']\n",
+ "\n",
+ "# Plotting Medication Prescription Patterns as percentages\n",
+ "plt.figure(figsize=(14, 8))\n",
+ "medication_data = clean_diabetic_data[medication_columns].apply(lambda x: x.value_counts(normalize=True) * 100)\n",
+ "medication_data.transpose().plot(kind='bar', stacked=True)\n",
+ "plt.title('Medication Prescription Patterns for Diabetic Patients')\n",
+ "plt.xlabel('Medication Type')\n",
+ "plt.ylabel('Percentage')\n",
+ "plt.legend(title='Prescription Status', bbox_to_anchor=(1, 1))\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## visual 5 (a)- Race distribution of diabetic patients \n",
+ "\n",
+ "Description:\n",
+ "- This visualization is a count plot that illustrates the distribution of diabetic patients across different racial categories.\n",
+ "\n",
+ "Interpretation:\n",
+ "- Each bar represents a racial category, and the height of the bar indicates the count of diabetic patients in that category.\n",
+ "- The plot uses a color palette ('viridis') to distinguish between different racial categories.\n",
+ "\n",
+ "Relevance:\n",
+ "- Understanding Racial Distribution: The plot provides insights into the racial distribution of diabetic patients in the dataset.\n",
+ "- Recognizing Dominant and Underrepresented Groups: By observing the heights of the bars, it's possible to identify which racial categories have a higher or lower representation among diabetic patients."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Create a count plot for race distribution\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "sns.countplot(x='race', data=clean_diabetic_data, palette='viridis')\n",
+ "\n",
+ "# Set the title and labels\n",
+ "plt.title('Race Distribution of Diabetic Patients')\n",
+ "plt.xlabel('Race')\n",
+ "plt.ylabel('Count')\n",
+ "\n",
+ "# Show the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visual 5 (b)- Race Distribution of Diabetic Patients (Styled Pie Chart with Legend)\n",
+ "\n",
+ "Description:\n",
+ "- This visualization is a styled pie chart that depicts the percentage distribution of diabetic patients across different racial categories.\n",
+ "- A legend is added to the side to show the percentages for each category."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Calculate percentage distribution\n",
+ "race_percentage = clean_diabetic_data['race'].value_counts(normalize=True) * 100\n",
+ "\n",
+ "# Define colors for each category\n",
+ "colors = plt.cm.tab10.colors[:len(race_percentage)]\n",
+ "\n",
+ "# Create a pie chart with styling\n",
+ "plt.figure(figsize=(10, 8))\n",
+ "wedges, texts, _ = plt.pie(race_percentage, labels=None, autopct='', startangle=90, colors=colors, wedgeprops=dict(width=0.3, edgecolor='w'))\n",
+ "\n",
+ "# Create a legend to the side\n",
+ "plt.legend(wedges, race_percentage.index, title=\"Race\", loc=\"center left\", bbox_to_anchor=(1, 0, 0.5, 1))\n",
+ "\n",
+ "# Add percentages as text to the legend\n",
+ "legend_labels = [f\"{label}: {race_percentage[label]:.1f}%\" for label in race_percentage.index]\n",
+ "plt.legend(legend_labels, title=\"Race\", loc=\"center left\", bbox_to_anchor=(1, 0, 0.5, 1))\n",
+ "\n",
+ "# Set the title\n",
+ "plt.title('Race Distribution of Diabetic Patients')\n",
+ "\n",
+ "# Show the plot\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visual 6 - Relationship between Time in Hospital, Number of Procedures, and Readmission\n",
+ "\n",
+ "Description:\n",
+ "- This visualization is a pair plot that explores the relationship between the time spent in the hospital, the number of procedures performed, and the likelihood of readmission among diabetic patients.\n",
+ "\n",
+ "Interpretation:\n",
+ "- The pair plot displays scatter plots for each combination of variables: time in the hospital, number of procedures, and readmission status.\n",
+ "- The hue ('readmitted') is used to color the points based on different readmission categories ('NO', '>30', '<30').\n",
+ "- Points are scattered along the plots, and patterns or trends can be observed based on the colors.\n",
+ "\n",
+ "Relevance:\n",
+ "- Identifying Relationships: By examining the scatter plots, we can identify potential relationships or patterns between time in the hospital, number of procedures, and readmission.\n",
+ "- Readmission Patterns: The color-coded points help visualize how different readmission categories are distributed across the variables, offering insights into factors associated with readmission.\n",
+ "- Supporting Predictive Analysis: Understanding these relationships is crucial for building models to predict readmission, as certain patterns may indicate increased risk or protective factors."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\giddy\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
+ " self._figure.tight_layout(*args, **kwargs)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 588.361x500 with 6 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Select relevant columns\n",
+ "readmission_data = clean_diabetic_data[['time_in_hospital', 'num_procedures', 'readmitted']].copy()\n",
+ "\n",
+ "# Map 'readmitted' values to numerical for better visualization\n",
+ "readmission_data['readmitted'] = readmission_data['readmitted'].map({'NO': 0, '>30': 1, '<30': 2})\n",
+ "\n",
+ "# Create a pair plot\n",
+ "sns.pairplot(readmission_data, hue='readmitted', palette='viridis')\n",
+ "plt.suptitle('Relationship between Time in Hospital, Number of Procedures, and Readmission', y=1.02)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aQoXyFnHka49"
+ },
+ "source": [
+ "# Feature Engineering\n",
+ "- Convert categorical or non-numeric features into a numerical representation\n",
+ "- Transform neccessary features using feature transformation techniques of your choice."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>admission_type_id</th>\n",
+ " <th>discharge_disposition_id</th>\n",
+ " <th>admission_source_id</th>\n",
+ " <th>time_in_hospital</th>\n",
+ " <th>num_lab_procedures</th>\n",
+ " <th>num_procedures</th>\n",
+ " <th>num_medications</th>\n",
+ " <th>number_outpatient</th>\n",
+ " <th>number_emergency</th>\n",
+ " <th>number_inpatient</th>\n",
+ " <th>...</th>\n",
+ " <th>metformin-rosiglitazone_Steady</th>\n",
+ " <th>metformin-pioglitazone_No</th>\n",
+ " <th>metformin-pioglitazone_Steady</th>\n",
+ " <th>change_Ch</th>\n",
+ " <th>change_No</th>\n",
+ " <th>diabetesMed_No</th>\n",
+ " <th>diabetesMed_Yes</th>\n",
+ " <th>readmitted_<30</th>\n",
+ " <th>readmitted_>30</th>\n",
+ " <th>readmitted_NO</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>6.0</td>\n",
+ " <td>3.116569</td>\n",
+ " <td>-1.173578</td>\n",
+ " <td>-1.187976</td>\n",
+ " <td>-0.098888</td>\n",
+ " <td>0.0</td>\n",
+ " <td>-1.418833</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1.0</td>\n",
+ " <td>-0.545417</td>\n",
+ " <td>0.309166</td>\n",
+ " <td>-0.473889</td>\n",
+ " <td>0.862910</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.324041</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1.0</td>\n",
+ " <td>-0.545417</td>\n",
+ " <td>0.309166</td>\n",
+ " <td>-0.830933</td>\n",
+ " <td>-1.701884</td>\n",
+ " <td>5.0</td>\n",
+ " <td>-0.402157</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1.0</td>\n",
+ " <td>-0.545417</td>\n",
+ " <td>0.309166</td>\n",
+ " <td>-0.830933</td>\n",
+ " <td>0.061412</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.033562</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1.0</td>\n",
+ " <td>-0.545417</td>\n",
+ " <td>0.309166</td>\n",
+ " <td>-1.187976</td>\n",
+ " <td>0.435444</td>\n",
+ " <td>0.0</td>\n",
+ " <td>-1.128354</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 2368 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " admission_type_id discharge_disposition_id admission_source_id \\\n",
+ "0 6.0 3.116569 -1.173578 \n",
+ "1 1.0 -0.545417 0.309166 \n",
+ "2 1.0 -0.545417 0.309166 \n",
+ "3 1.0 -0.545417 0.309166 \n",
+ "4 1.0 -0.545417 0.309166 \n",
+ "\n",
+ " time_in_hospital num_lab_procedures num_procedures num_medications \\\n",
+ "0 -1.187976 -0.098888 0.0 -1.418833 \n",
+ "1 -0.473889 0.862910 0.0 0.324041 \n",
+ "2 -0.830933 -1.701884 5.0 -0.402157 \n",
+ "3 -0.830933 0.061412 1.0 0.033562 \n",
+ "4 -1.187976 0.435444 0.0 -1.128354 \n",
+ "\n",
+ " number_outpatient number_emergency number_inpatient ... \\\n",
+ "0 0.0 0.0 0.0 ... \n",
+ "1 0.0 0.0 0.0 ... \n",
+ "2 2.0 0.0 1.0 ... \n",
+ "3 0.0 0.0 0.0 ... \n",
+ "4 0.0 0.0 0.0 ... \n",
+ "\n",
+ " metformin-rosiglitazone_Steady metformin-pioglitazone_No \\\n",
+ "0 False True \n",
+ "1 False True \n",
+ "2 False True \n",
+ "3 False True \n",
+ "4 False True \n",
+ "\n",
+ " metformin-pioglitazone_Steady change_Ch change_No diabetesMed_No \\\n",
+ "0 False False True True \n",
+ "1 False True False False \n",
+ "2 False False True False \n",
+ "3 False True False False \n",
+ "4 False True False False \n",
+ "\n",
+ " diabetesMed_Yes readmitted_<30 readmitted_>30 readmitted_NO \n",
+ "0 False False False True \n",
+ "1 True False True False \n",
+ "2 True False False True \n",
+ "3 True False False True \n",
+ "4 True False False True \n",
+ "\n",
+ "[5 rows x 2368 columns]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Applying Label Encoding to the 'age_group' column\n",
+ "le = LabelEncoder()\n",
+ "clean_diabetic_data['age_group'] = le.fit_transform(\n",
+ " clean_diabetic_data['age_group'])\n",
+ "\n",
+ "# Applying One-Hot Encoding to other categorical columns\n",
+ "categorical_cols = clean_diabetic_data.select_dtypes(\n",
+ " include=['object']).columns\n",
+ "clean_diabetic_data = pd.get_dummies(\n",
+ " clean_diabetic_data, columns=categorical_cols)\n",
+ "\n",
+ "# Identifying continuous columns for standardization\n",
+ "unique_threshold = 10\n",
+ "continuous_cols = [col for col in clean_diabetic_data.columns\n",
+ " if clean_diabetic_data[col].nunique() > unique_threshold]\n",
+ "\n",
+ "# Standardizing continuous columns\n",
+ "scaler = StandardScaler()\n",
+ "clean_diabetic_data[continuous_cols] = scaler.fit_transform(\n",
+ " clean_diabetic_data[continuous_cols])\n",
+ "\n",
+ "# Dropping 'encounter_id' and 'patient_nbr' columns\n",
+ "clean_diabetic_data.drop(['encounter_id', 'patient_nbr'], axis=1, inplace=True)\n",
+ "\n",
+ "clean_diabetic_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oO41dtK2koFf"
+ },
+ "source": [
+ "# Machine Learning\n",
+ "- Use 2 different ML algorithms to build a model using your preprocessed data.\n",
+ "- Compare the 2 models based on their accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "readmitted_binary 1.000000\n",
+ "number_inpatient 0.231765\n",
+ "number_emergency 0.124014\n",
+ "number_diagnoses 0.112883\n",
+ "number_outpatient 0.106572\n",
+ "diag_1_428 0.070162\n",
+ "diabetesMed_Yes 0.061508\n",
+ "num_medications 0.059127\n",
+ "time_in_hospital 0.053492\n",
+ "diag_2_403 0.049687\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Creating a binary target variable: 1 if readmitted (<30 or >30), 0 if not readmitted (NO)\n",
+ "clean_diabetic_data['readmitted_binary'] = (\n",
+ " clean_diabetic_data['readmitted_<30'] | clean_diabetic_data['readmitted_>30']).astype(int)\n",
+ "\n",
+ "# Dropping original readmission columns\n",
+ "clean_diabetic_data.drop(['readmitted_<30', 'readmitted_>30',\n",
+ " 'readmitted_NO'], axis=1, inplace=True)\n",
+ "\n",
+ "# Calculating the correlation of each feature with the target variable 'readmitted_binary'\n",
+ "correlations = clean_diabetic_data.corrwith(\n",
+ " clean_diabetic_data['readmitted_binary']).sort_values(ascending=False)\n",
+ "\n",
+ "# Displaying the top correlated features\n",
+ "correlations.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Finished Training Model\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+ "from sklearn.svm import SVC\n",
+ "\n",
+ "\n",
+ "# Selecting only the top correlated features\n",
+ "top_features = [\n",
+ " 'number_inpatient', 'number_emergency', 'number_diagnoses',\n",
+ " 'number_outpatient', 'diag_1_428', 'diabetesMed_Yes',\n",
+ " 'num_medications', 'time_in_hospital'\n",
+ "]\n",
+ "\n",
+ "# Updating the dataset for model training with top correlated features\n",
+ "X_top = clean_diabetic_data[top_features]\n",
+ "y = clean_diabetic_data['readmitted_binary']\n",
+ "\n",
+ "# Splitting the dataset into training and testing sets\n",
+ "X_train_top, X_test_top, y_train, y_test = train_test_split(\n",
+ " X_top, y, test_size=0.3, random_state=42)\n",
+ "\n",
+ "# Initializing the models\n",
+ "logistic_model = LogisticRegression(max_iter=3000, random_state=1)\n",
+ "tree_model = DecisionTreeClassifier(random_state=1)\n",
+ "\n",
+ "# Training the models on the top features\n",
+ "logistic_model.fit(X_train_top, y_train)\n",
+ "tree_model.fit(X_train_top, y_train)\n",
+ "\n",
+ "# Predicting on the test set with top features\n",
+ "logistic_predictions_top = logistic_model.predict(X_test_top)\n",
+ "tree_predictions_top = tree_model.predict(X_test_top)\n",
+ "\n",
+ "print('Finished Training Model')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xQAC-paZk3mA"
+ },
+ "source": [
+ "# Model Evaluation\n",
+ "- Evaluate the 2 models using a minimum of 4 evaluation metrics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "id": "1Uq1CBcelAKR"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Logistic Regression': {'Accuracy': 0.6216508352440223,\n",
+ " 'Precision': 0.6224611805257899,\n",
+ " 'Recall': 0.6216508352440223,\n",
+ " 'F1 Score': 0.6105345294814823},\n",
+ " 'Decision Tree Classifier': {'Accuracy': 0.5837864395676384,\n",
+ " 'Precision': 0.5814989268741881,\n",
+ " 'Recall': 0.5837864395676384,\n",
+ " 'F1 Score': 0.5672927174329188}}"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Calculating evaluation metrics for both models using top features\n",
+ "metrics_top = {\n",
+ " 'Logistic Regression': {\n",
+ " 'Accuracy': accuracy_score(y_test, logistic_predictions_top),\n",
+ " 'Precision': precision_score(y_test, logistic_predictions_top, average='weighted'),\n",
+ " 'Recall': recall_score(y_test, logistic_predictions_top, average='weighted'),\n",
+ " 'F1 Score': f1_score(y_test, logistic_predictions_top, average='weighted')\n",
+ " },\n",
+ " 'Decision Tree Classifier': {\n",
+ " 'Accuracy': accuracy_score(y_test, tree_predictions_top),\n",
+ " 'Precision': precision_score(y_test, tree_predictions_top, average='weighted'),\n",
+ " 'Recall': recall_score(y_test, tree_predictions_top, average='weighted'),\n",
+ " 'F1 Score': f1_score(y_test, tree_predictions_top, average='weighted')\n",
+ " },\n",
+ "}\n",
+ "\n",
+ "metrics_top"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FEazQftYlAio"
+ },
+ "source": [
+ "# Deployment\n",
+ "- Able to deploy the ML model to cloud.\n",
+ "- Provides a live working URL to the deployed app."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "1lFeLUyNlQE_"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['diabetic_patients_readmission_model.pkl']"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import joblib\n",
+ "\n",
+ "# Selecting 'logistic_model' since it has a higher accuracy\n",
+ "joblib.dump(logistic_model, 'diabetic_patients_readmission_model.pkl')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Deployed Model Live Link\n",
+ "https://diabetes-patients-readmission-prediction.onrender.com"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}