Risk_Reduction_Potentials.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Introduction**\n",
    "\n",
    "This small project focuses on (and serves as an example of) leveraging the value of metabolic profiles and related calculations created in a a novel 10,000 individual synthetic dataset.  User input creates a simple gender/ age/  weight/ height status which is then compared to the average risk profile for a similar cohort in the synthetic dataset. A previously created novel health risk calculation is employed to serve as the key data point in assigning a scaled metric that provides perspective on metabolic risk. This greatly expands upon the binary Metabolic Syndrome classification. The user is then asked to provide  weight loss or gain variable input to calculate change in calculated status incorporating this change. Changes to BMI status, number of risk factors and changes to the health risk score are provided (along with a bar graph for visualization). This is only one example of how individual metrics can be leveraged with comparison to large dataset profiles and related analysis. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Importing Essential Libraries**\n",
    "I imported two fundamental libraries for data analysis and manipulation in Python: pandas and numpy. Pandas provides data structures and functions for efficiently handling structured data, including tabular data such as spreadsheets and SQL tables. Numpy offers support for large, multi-dimensional arrays and matrices, and is the foundation of most scientific computing in Python. These libraries will be used extensively throughout this project for data loading, manipulation, and analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Loading Synthetic Metabolic Data**\n",
    "\n",
    "We load the previously created synthetic metabolic dataset from a CSV file into a pandas DataFrame for analysis and manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_adult = pd.read_csv('/Users/steventuschman/Desktop/df_adult_updated.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Exploring Synthetic Metabolic Data**\n",
    "\n",
    "I explore the synthetic metabolic data by previewing the first few rows, examining dataset information, and generating summary statistics. This provides a comprehensive overview of the data's structure, content, and distribution, helping us understand the characteristics of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age  Gender   BMI  Waist_Circumference  Fasting_Blood_Glucose BMI_Category  \\\n",
      "0   30  Female  24.9                 27.0                     97       Normal   \n",
      "1   24    Male  28.0                 33.1                     82   Overweight   \n",
      "2   79    Male  30.9                 54.6                    136        Obese   \n",
      "3   75    Male  32.4                 57.2                    296        Obese   \n",
      "4   65    Male  34.2                 50.4                     73        Obese   \n",
      "\n",
      "   Triglycerides  HDL_Cholesterol  High_Blood_Pressure  Metabolic_Syndrome  \\\n",
      "0            139               36                    0               False   \n",
      "1             52               42                    0               False   \n",
      "2            268               58                    0                True   \n",
      "3            181               56                    1                True   \n",
      "4            128               58                    1               False   \n",
      "\n",
      "   Risk_Factors  BMI_Rounded  Age_Rounded  Calculated_Metabolic_Syndrome  \\\n",
      "0             1         25.0           30                          False   \n",
      "1             0         28.0           24                          False   \n",
      "2             3         31.0           79                           True   \n",
      "3             4         32.0           75                           True   \n",
      "4             2         34.0           65                          False   \n",
      "\n",
      "   Health_Risk_Score Risk_Category  Converted_Risk_Score  \n",
      "0                  2           low                    11  \n",
      "1                  3           low                    17  \n",
      "2                  9          high                    50  \n",
      "3                 11          high                    61  \n",
      "4                  7      moderate                    39  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 17 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Age                            10000 non-null  int64  \n",
      " 1   Gender                         10000 non-null  object \n",
      " 2   BMI                            10000 non-null  float64\n",
      " 3   Waist_Circumference            10000 non-null  float64\n",
      " 4   Fasting_Blood_Glucose          10000 non-null  int64  \n",
      " 5   BMI_Category                   10000 non-null  object \n",
      " 6   Triglycerides                  10000 non-null  int64  \n",
      " 7   HDL_Cholesterol                10000 non-null  int64  \n",
      " 8   High_Blood_Pressure            10000 non-null  int64  \n",
      " 9   Metabolic_Syndrome             10000 non-null  bool   \n",
      " 10  Risk_Factors                   10000 non-null  int64  \n",
      " 11  BMI_Rounded                    10000 non-null  float64\n",
      " 12  Age_Rounded                    10000 non-null  int64  \n",
      " 13  Calculated_Metabolic_Syndrome  10000 non-null  bool   \n",
      " 14  Health_Risk_Score              10000 non-null  int64  \n",
      " 15  Risk_Category                  10000 non-null  object \n",
      " 16  Converted_Risk_Score           10000 non-null  int64  \n",
      "dtypes: bool(2), float64(3), int64(9), object(3)\n",
      "memory usage: 1.2+ MB\n",
      "None\n",
      "                Age           BMI  Waist_Circumference  Fasting_Blood_Glucose  \\\n",
      "count  10000.000000  10000.000000         10000.000000           10000.000000   \n",
      "mean      55.076600     28.586410            41.013900             133.582200   \n",
      "std       22.855108      4.970175             9.258669              68.906479   \n",
      "min       18.000000     15.000000            26.000000              70.000000   \n",
      "25%       35.000000     25.200000            33.600000              83.000000   \n",
      "50%       53.000000     28.600000            40.200000              95.000000   \n",
      "75%       73.000000     32.000000            47.500000             182.000000   \n",
      "max      100.000000     47.100000            65.000000             300.000000   \n",
      "\n",
      "       Triglycerides  HDL_Cholesterol  High_Blood_Pressure  Risk_Factors  \\\n",
      "count   10000.000000     10000.000000          10000.00000  10000.000000   \n",
      "mean      141.168800        51.628700              0.42410      2.000600   \n",
      "std        72.767023        13.274451              0.49423      1.236104   \n",
      "min        30.000000        20.000000              0.00000      0.000000   \n",
      "25%        86.000000        42.000000              0.00000      1.000000   \n",
      "50%       123.000000        54.000000              0.00000      2.000000   \n",
      "75%       180.000000        60.000000              1.00000      3.000000   \n",
      "max       350.000000        80.000000              1.00000      5.000000   \n",
      "\n",
      "        BMI_Rounded   Age_Rounded  Health_Risk_Score  Converted_Risk_Score  \n",
      "count  10000.000000  10000.000000       10000.000000          10000.000000  \n",
      "mean      28.582400     55.076600           5.866000             32.612000  \n",
      "std        4.976674     22.855108           3.246398             18.022035  \n",
      "min       15.000000     18.000000           0.000000              0.000000  \n",
      "25%       25.000000     35.000000           3.000000             17.000000  \n",
      "50%       29.000000     53.000000           6.000000             33.000000  \n",
      "75%       32.000000     73.000000           8.000000             44.000000  \n",
      "max       47.000000    100.000000          13.000000             72.000000  \n"
     ]
    }
   ],
   "source": [
    "print(df_adult.head())  # Display the first few rows\n",
    "print(df_adult.info())  # Display dataset information\n",
    "print(df_adult.describe())  # Display summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Collecting User Information**\n",
    "\n",
    "\n",
    "We prompt the user to enter their personal details, including gender, age, height, and weight. This information will be used to calculate their BMI and determine their average risk score. The user is asked to input their gender as either Male or Female, age in years, height in inches, and weight in pounds. This data is stored in the gender, age, height, and weight variables for further processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You entered: Gender - M, Age - 61, Height - 71.0 inches, Weight - 267.0 pounds\n",
      "Your calculated BMI is: 37.24\n"
     ]
    }
   ],
   "source": [
    "# Get user input for gender, age, height, and weight\n",
    "gender = input(\"Enter your gender (M/F): \")\n",
    "age = int(input(\"Enter your age: \"))\n",
    "height = float(input(\"Enter your height in inches: \"))\n",
    "weight = float(input(\"Enter your weight in pounds: \"))\n",
    "\n",
    "# Convert user's height and weight to metric units\n",
    "height_meters = height * 0.0254\n",
    "weight_kg = weight * 0.453592\n",
    "\n",
    "# Calculate user's Body Mass Index (BMI)\n",
    "bmi = weight_kg / (height_meters ** 2)\n",
    "\n",
    "# Display summary of user's entered information\n",
    "print(f\"You entered: Gender - {gender}, Age - {age}, Height - {height} inches, Weight - {weight} pounds\")\n",
    "print(f\"Your calculated BMI is: {bmi:.2f}\")\n",
    "\n",
    "# Store user data for further analysis\n",
    "user_gender = 'Male' if gender == 'M' else 'Female'\n",
    "user_age = age\n",
    "user_bmi = bmi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Cohort Comparison** \n",
    "\n",
    "We now identify a cohort of individuals from the dataset who are comparable to the user based on gender, age, and Body Mass Index (BMI). Cophort definition includes  BMI within ±1 of the user's rounded BMI, and an age within ±1 year of the user's age. For the identified cohort, we calculate the average Health Risk Score, Converted Risk Score, and the average number of Risk Factors. The average Converted Risk Score is categorized into risk levels: No Risk, Low Risk, Moderate Risk, High Risk, and Very High Risk. The accompanying code block implements this analysis, providing the cohort results along with debug information to verify the filtering process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User Gender: Male\n",
      "User Age: 61\n",
      "User BMI: 37.24 (Rounded: 37)\n",
      "Cohort Analysis Results:\n",
      "Number of comparables in cohort: 18\n",
      "Average Health Risk Score: 8.56\n",
      "Average Converted Risk Score: 47.61\n",
      "Risk Category: High Risk\n",
      "Average Number of Risk Factors: 3.06\n",
      "\n",
      "Debug Information:\n",
      "User Gender: Male\n",
      "User Age: 61\n",
      "User BMI: 37.24 (Rounded: 37)\n",
      "Gender match count: 4989\n",
      "BMI range match count: 599\n",
      "Age range match count: 410\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def get_risk_category(score):\n",
    "    if pd.isna(score):\n",
    "        return \"Unable to determine (No matching cohort)\"\n",
    "    score = int(score)\n",
    "    if score == 0:\n",
    "        return \"No Risk\"\n",
    "    elif 1 <= score <= 22:\n",
    "        return \"Low Risk\"\n",
    "    elif 23 <= score <= 44:\n",
    "        return \"Moderate Risk\"\n",
    "    elif 45 <= score <= 67:\n",
    "        return \"High Risk\"\n",
    "    else:\n",
    "        return \"Very High Risk\"\n",
    "\n",
    "# Use the stored user data\n",
    "user_bmi_rounded = round(user_bmi)\n",
    "\n",
    "# Debug: Print user input details\n",
    "print(f\"User Gender: {user_gender}\")\n",
    "print(f\"User Age: {user_age}\")\n",
    "print(f\"User BMI: {user_bmi:.2f} (Rounded: {user_bmi_rounded})\")\n",
    "\n",
    "# Filter the dataset for the cohort\n",
    "cohort = df_adult[\n",
    "    (df_adult['Gender'] == user_gender) &\n",
    "    (df_adult['BMI_Rounded'].between(user_bmi_rounded - 1, user_bmi_rounded + 1)) &\n",
    "    (df_adult['Age'].between(user_age - 1, user_age + 1))\n",
    "]\n",
    "\n",
    "# Calculate the required metrics\n",
    "cohort_size = len(cohort)\n",
    "\n",
    "if cohort_size > 0:\n",
    "    avg_health_risk_score = cohort['Health_Risk_Score'].mean()\n",
    "    avg_converted_risk_score = cohort['Converted_Risk_Score'].mean()\n",
    "    avg_risk_factors = cohort['Risk_Factors'].mean()\n",
    "    risk_category = get_risk_category(avg_converted_risk_score)\n",
    "else:\n",
    "    avg_health_risk_score = avg_converted_risk_score = avg_risk_factors = np.nan\n",
    "    risk_category = \"Unable to determine (No matching cohort)\"\n",
    "\n",
    "# Print the results\n",
    "print(f\"Cohort Analysis Results:\")\n",
    "print(f\"Number of comparables in cohort: {cohort_size}\")\n",
    "\n",
    "if cohort_size > 0:\n",
    "    print(f\"Average Health Risk Score: {avg_health_risk_score:.2f}\")\n",
    "    print(f\"Average Converted Risk Score: {avg_converted_risk_score:.2f}\")\n",
    "    print(f\"Risk Category: {risk_category}\")\n",
    "    print(f\"Average Number of Risk Factors: {avg_risk_factors:.2f}\")\n",
    "else:\n",
    "    print(\"No matching cohort found. Unable to provide comparative analysis.\")\n",
    "\n",
    "# Debug information\n",
    "print(\"\\nDebug Information:\")\n",
    "print(f\"User Gender: {user_gender}\")\n",
    "print(f\"User Age: {user_age}\")\n",
    "print(f\"User BMI: {user_bmi:.2f} (Rounded: {user_bmi_rounded})\")\n",
    "print(f\"Gender match count: {len(df_adult[df_adult['Gender'] == user_gender])}\")\n",
    "print(f\"BMI range match count: {len(df_adult[df_adult['BMI_Rounded'].between(user_bmi_rounded - 1, user_bmi_rounded + 1)])}\")\n",
    "print(f\"Age range match count: {len(df_adult[df_adult['Age'].between(user_age - 1, user_age + 1)])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Cohort Analysis**\n",
    "\n",
    "This code segment performs a cohort analysis to compare health metrics before and after a weight change. It starts by importing necessary libraries: pandas, numpy, and matplotlib. The get_risk_category function categorizes the risk score into various risk levels. The perform_cohort_analysis function filters the dataset based on gender, age, and BMI, and calculates the average health risk score, converted risk score, and number of risk factors for the cohort.\n",
    "The original user data is printed, including gender, age, height, weight, and BMI. The cohort analysis is performed for the original profile, and the results are printed, including the number of comparables in the cohort, average health risk score, average converted risk score, risk category, and average number of risk factors.\n",
    "Next, the user is prompted to enter a weight change (negative for loss, positive for gain) in pounds. The new weight and BMI are calculated, and the new profile is printed, including gender, age, height, new weight, and new BMI. The cohort analysis is performed for the new profile, and the results are printed.\n",
    "The original and new profiles are compared, showing the changes in weight, BMI, health risk score, converted risk score, and number of risk factors. A bar graph is created to visualize the comparison of health metrics before and after the weight change. The percentage changes for BMI, number of risk factors, and converted risk score are calculated and printed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Profile:\n",
      "Gender: Male\n",
      "Age: 61\n",
      "Height: 71.0 inches\n",
      "Weight: 267.0 pounds\n",
      "BMI: 37.24\n",
      "Number of comparables in cohort: 18\n",
      "Average Health Risk Score: 8.56\n",
      "Average Converted Risk Score: 47.61\n",
      "Risk Category: High Risk\n",
      "Average Number of Risk Factors: 3.06\n",
      "\n",
      "New Profile After Weight Change:\n",
      "Gender: Male\n",
      "Age: 61\n",
      "Height: 71.0 inches\n",
      "New Weight: 192.0 pounds\n",
      "New BMI: 26.78\n",
      "Number of comparables in new cohort: 44\n",
      "New Average Health Risk Score: 6.16\n",
      "New Average Converted Risk Score: 34.25\n",
      "New Risk Category: Moderate Risk\n",
      "New Average Number of Risk Factors: 2.50\n",
      "\n",
      "Comparison:\n",
      "Weight Change: -75.00 pounds\n",
      "BMI Change: -10.46\n",
      "Health Risk Score Change: -2.40\n",
      "Converted Risk Score Change: -13.36\n",
      "Risk Factor Change: -0.56\n"
     ]
    },
    {
     "data": {
      "image/png": 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WKFu2rFGvXj2bdkmGg4ODcfDgwST7JskYPny49bmXl5fRr1+/FI9FbGys4ePjY5QtW9a4c+eOtX3ZsmWGJOODDz5Isi+jRo2yWUelSpWMwMDAFLdhGIZx4cIFw8nJyWjUqJERHx9vbZ80aZIhyZg5c6a1bfjw4YYkm2OTksS+j3o0a9bM2n/Tpk2GJOO7776zWc+KFSuStD/8fhiGYfTp08fIkSOHcffuXWtbaGioUbhwYevzixcvJnkfHuz7pMfQMAyjdu3aRpkyZQzDMIwqVaoYPXr0MAzDMK5evWo4OTkZc+bMMdavX29IMubPn299XY8ePYz8+fMbly5dsllfhw4dDC8vL+u+7ty505BkzJo1K9ltSzKmTp2a7LLatWtbn8+cOdOQZHzxxRdJ+iYkJBiGYRgDBgwwPD09jXv37j12vx+W0nvdqlUrm/cmISHBKFGihBEcHGzdrmHcf2+LFCliNGzY0NqW3HEzDMOoWLGi4ePjY1y+fNnatm/fPsPBwcHo0qWLtS3xs9ixY0eb1584ccLIli2bMXr0aJv2/fv3G9mzZ0/S/rDkPofbtm0zJBnffPONtW3WrFmGJKNBgwY2+zpo0CAjW7ZsxrVr1wzD+P/vYrNmzWz6/ec//zEkGaGhoY+sJ1G5cuWMzp0727w+T548RlxcnLXt+PHjhiTj008/tXlts2bNbL4zib799lvDwcHB2LRpk0371KlTDUnGli1brG2POg8mZ8iQIYYk4++//zYMwzC+//57w8XFxYiJiTF+/fVXI1u2bMb169cNw/j/81Li9j788EPDzc3NOHLkiM063333XSNbtmzGqVOnbOp68LsfEhJi5MiRwzhz5oy1LTIy0siePbvx8K8WkgwnJyfj6NGj1rZ9+/YZkoyJEyda2z799FNDknH8+PHH7vdPP/1kSDLGjRv32L6G8f/fg1KlShkxMTHW9gkTJhiSjP3791vbkvtshoWFGRaLxeZnY2rPewsXLjQkGePHj7e2xcfHG/Xq1UtyXqpfv75Rrly5JN/3atWqGSVKlEjVvgLAPxGX7wFAJnT9+nVJSvVfgX/99VdJ0uDBg23a33zzTUlKMvdU6dKlFRQUZH3+/PPPS5Lq1aunQoUKJWn/66+/kmzzwUl+E0erxMbGas2aNdZ2V1dX67+vXr2q6Oho1axZM8mldpJUu3ZtlS5d+jF7en8ul+3bt+vs2bPJLt+1a5cuXLigvn372syF0qxZMwUEBCQ7D9err75q87xmzZrJ7vOD1qxZo9jYWA0cONBmFFmvXr3k6en51PN9LVy4UKtXr07yeHgkz/z58+Xl5aWGDRvq0qVL1kdgYKDc3d21fv16a98H348bN27o0qVLqlmzpm7fvq3Dhw8/Vb1Pcgwf1qlTJy1atEixsbFasGCBsmXLluzoAMMwtHDhQoWEhMgwDJv9Dg4OVnR0dLKfseQ4OzurW7duj+23cOFC5cmTR6+//nqSZYmXLOXMmVO3bt3S6tWrU7Xth7Vs2dL6Pv/0008aOnSoVqxYoU6dOllHKIWHhysyMlKdOnXS5cuXrft969Yt1a9fX7/99luyo+kSRUVFKTw8XF27dlXu3Lmt7eXLl1fDhg2t55IHPfzeLlq0SAkJCWrfvr3Nsff19VWJEiVsPnPJefBzGBcXp8uXL6t48eLKmTNnsu9b7969bS4Lq1mzpuLj43Xy5ElJ//9dfP311236pWXS8T/++EP79+9Xx44drW0dO3bUpUuXtHLlylSv52Hz589XqVKlFBAQYHOsEkcDPnysUnselP5/XqlNmzZJun/pXmBgoJycnBQUFGS9ZC9xWeLlYIl11axZU7ly5bKpq0GDBoqPj9dvv/2W7Dbj4+O1Zs0atWrVSgUKFLC2Fy9eXE2aNEn2NQ0aNFCxYsWsz8uXLy9PT880nx8SpfXnY6Ju3brZzBWXOHLywToe/GzeunVLly5dUrVq1WQYRpJL2aXHn/dWrFghR0dH9erVy9rm4OCgfv362bzuypUrWrdundq3b289N1+6dEmXL19WcHCwIiMjk1x+DgBZBZfvAUAm5OnpKel+cJAaJ0+elIODQ5I7u/n6+ipnzpzWX94SPRg8SZKXl5ckqWDBgsm2Pzw3kYODg4oWLWrT9uyzz0qSzTwgy5Yt00cffaTw8HCb+VOSm3ekSJEiKe7fgz755BOFhoaqYMGCCgwMVNOmTdWlSxdrPYn7WrJkySSvDQgIsLncRbp/GczDl4nkypUryT4/LKXtODk5qWjRokmOeVrVqlXLehnLw/U+KDIyUtHR0cle4ib9/2TX0v3LQ95//32tW7fO+otdoujo6Ceu9UmP4cM6dOigt956S8uXL9d3332n5s2bJ/uL58WLF3Xt2jVNnz49xbtPPrjfj/LMM8+kalLzY8eOqWTJko+8C1nfvn31448/qkmTJnrmmWfUqFEjtW/fXo0bN05VLX5+fmrQoIH1eYsWLeTt7a233npLy5YtU0hIiCIjIyUp2ct/EkVHRytXrlzJLnvU96NUqVJauXJlksnMH/5uRkZGyjAMlShRItltJDfHz4Pu3LmjsLAwzZo1S2fOnLGZiy65z+HD56vEfUv8fCXu08P15M2bN8Xj8LD//e9/cnNzU9GiRa3zDLm4uMjf31/fffddksuNUysyMlKHDh1K8VK0hz+nqT0PSlL16tWtc2516NBBW7ZsUcOGDSXdD0hLly5tbduyZYuqVq1q/axHRkbqjz/+SHVdD7bfuXMn2buIpnRn0YffP+nJzg+J0vrzMaU6Hv4cSffnFfvggw/0888/J6nv4c9mas57J0+eVP78+ZNckvrwsTp69KgMw9CwYcM0bNiwZOu/cOGCnnnmmUftIgD8IxFKAUAm5OnpqQIFCujAgQNpel1Kk8w+LFu2bGlqNx6awDw1Nm3apBYtWqhWrVqaPHmy8ufPL0dHR82aNUtz585N0v/Bv1A/Svv27VWzZk0tXrxYq1at0qeffqqxY8dq0aJFKf6l/lFS2ud/ioSEBPn4+Oi7775LdnniL03Xrl1T7dq15enpqVGjRqlYsWJycXHRnj179M477zxydM3jpNcxzJ8/v+rUqaPPP/9cW7ZsSfGOe4m1vvzyyymGM6mZt0tK/ecuNXx8fBQeHq6VK1dq+fLlWr58uWbNmqUuXbpozpw5T7TO+vXrS5J+++03hYSEWPf9008/VcWKFZN9TeJ8Runl4WOUkJAgi8Wi5cuXJ/veP277r7/+umbNmqWBAwcqKChIXl5eslgs6tChQ7Kfw/Q8L6W0nu+//163bt1KdpTShQsXdPPmzSc6rgkJCSpXrpy++OKLZJc//IeAtHwevb29rUH7zZs39ccff1hvqiDdnztt8+bN+vvvv3Xq1Cl17tzZpq6GDRvq7bffTnbdiX9kSA/p/f4FBARIuj9PVnrWER8fr4YNG+rKlSt65513FBAQIDc3N505c0Zdu3ZN8tlMz58diet+6623FBwcnGyflEI/APinI5QCgEyqefPmmj59urZt22ZzqV1yChcurISEBEVGRtrc7vv8+fO6du2a9Y5R6SUhIUF//fWXzS8uR44ckSTrxLwLFy6Ui4uLVq5cKWdnZ2u/WbNmPfX28+fPr759+6pv3766cOGCKleurNGjR6tJkybWfY2IiLBeIpMoIiIi3Y7Fg9t5cNRYbGysjh8/bjPiJSMVK1ZMa9asUfXq1R/5C+2GDRt0+fJlLVq0SLVq1bK2Hz9+/LHbSG3YmR46deqknj17KmfOnGratGmyffLmzSsPDw/Fx8c/9jinV+3FihXT9u3bFRcX98iRQE5OTgoJCbEGSH379tW0adM0bNiwJ/ql8t69e5KkmzdvWuuQ7gfXT/IZe/Bz+7DDhw8rT548NqOkklOsWDEZhqEiRYo8UXixYMEChYaG6vPPP7e23b171+aulWmRuE+RkZE238WLFy+majTOxo0b9ffff2vUqFE250/p/iia3r17a8mSJXr55ZdTXEdKn7NixYpp3759ql+/foZ8j2rUqKGZM2dq1apVio+Pt5nEv1q1avr++++td1Z98M6JxYoV082bN9P8GfLx8ZGLi0uSu9ZJSrYttdJybJ599lmVLFlSP/30kyZMmJBuIez+/ft15MgRzZkzR126dLG2P+nluNL9z+b69et1+/Ztm9FSDx+rxM+to6OjaT87ACCzYE4pAMik3n77bbm5ualnz546f/58kuXHjh2z3mo+8Zf3h++YlPjX+Se99ORRJk2aZP23YRiaNGmSHB0drSM7smXLJovFYnN77BMnTiS521RaxMfHJ7mEwsfHRwUKFLBeHlilShX5+Pho6tSpNpcMLl++XIcOHUq3Y9GgQQM5OTnpyy+/tPmL/4wZMxQdHZ0hxzw57du3V3x8vD788MMky+7du2f9RT/xr/oP1hobG6vJkyc/dhuJv0w9aWiQFi+++KKGDx+uyZMnp3hZXbZs2dS2bVstXLgw2dGEFy9etP47MWB52trbtm2rS5cu2XzuEyUe08uXL9u0Ozg4WEdsPfhZTIulS5dKkipUqCBJCgwMVLFixfTZZ59Zg6oHPbjvycmfP78qVqyoOXPm2ByTAwcOaNWqVSkGgQ9q06aNsmXLppEjRyYZ7WIYRpLj8LBs2bIled3EiRNtzhVp0aBBAzk6OmrixIk2633UHeQelHjp3pAhQ/Tiiy/aPHr16qUSJUqkOBIxUeId+B7Wvn17nTlzRl9//XWSZXfu3NGtW7dSVWNKatSoofj4eH322WcqUaKEzeVk1apV082bNzV58mQ5ODjYBFbt27fXtm3bkp0v69q1a9Yw9GHZsmVTgwYNtGTJEpt5/Y4eParly5c/8X6k9Xs6cuRIXb58WT179ky21lWrVmnZsmVpqiG5c6RhGNafs08iODhYcXFxNu9/QkKCvvrqK5t+Pj4+qlOnjqZNm6aoqKgk63nc9xoA/skYKQUAmVSxYsU0d+5cvfTSSypVqpS6dOmismXLKjY2Vlu3btX8+fPVtWtXSfd/YQ0NDdX06dOtl2nt2LFDc+bMUatWrVS3bt10rc3FxUUrVqxQaGionn/+eS1fvly//PKL/vOf/1h/KWrWrJm++OILNW7cWJ06ddKFCxf01VdfqXjx4vrjjz+eaLs3btyQn5+fXnzxRVWoUEHu7u5as2aNdu7caR114ejoqLFjx6pbt26qXbu2OnbsqPPnz2vChAny9/fXoEGD0uUY5M2bV0OHDtXIkSPVuHFjtWjRQhEREZo8ebKqVq36yFEV6al27drq06ePwsLCFB4erkaNGsnR0VGRkZGaP3++JkyYoBdffFHVqlVTrly5FBoaqjfeeEMWi0Xffvttqi6hcXV1VenSpfXDDz/o2WefVe7cuVW2bFmVLVs23ffHy8tLI0aMeGy/MWPGaP369Xr++efVq1cvlS5dWleuXNGePXu0Zs0aXblyRdL971HOnDk1depUeXh4yM3NTc8//3ya5u6RpC5duuibb77R4MGDtWPHDtWsWVO3bt3SmjVr1LdvX7Vs2VI9e/bUlStXVK9ePfn5+enkyZOaOHGiKlasmGQETnKOHDmi//3vf5Kk27dv6/fff9ecOXNUvHhxvfLKK5LuB13//e9/1aRJE5UpU0bdunXTM888ozNnzmj9+vXy9PS0Blkp+fTTT9WkSRMFBQWpR48eunPnjiZOnJjqY1+sWDF99NFHGjp0qE6cOKFWrVrJw8NDx48f1+LFi9W7d2+99dZbKb6+efPm+vbbb+Xl5aXSpUtr27ZtWrNmjby9vR+77eTkzZtXb731lsLCwtS8eXM1bdpUe/fu1fLly5Odl+1BMTExWrhwoRo2bJhkvrZELVq00IQJEx45T1lgYKB++OEHDR48WFWrVpW7u7tCQkL0yiuv6Mcff9Srr76q9evXq3r16oqPj9fhw4f1448/auXKldbJx59E4uinbdu2WX8eJHr22WeVJ08ebdu2TeXKlVPOnDmty4YMGaKff/5ZzZs3V9euXRUYGKhbt25p//79WrBggU6cOJHisRsxYoRWrVql6tWr67XXXlN8fLwmTZqksmXLKjw8/In2IzAwUJL03nvvqUOHDnJ0dFRISEiKo/Zeeukl7d+/X6NHj9bevXvVsWNHFS5cWJcvX9aKFSu0du3aZC8Tf5SAgAAVK1ZMb731ls6cOSNPT08tXLjwiee+kqRWrVrpueee05tvvqmjR48qICBAP//8s/X89OAIsa+++ko1atRQuXLl1KtXLxUtWlTnz5/Xtm3b9Pfff2vfvn1PXAcAZGrm3egPAPAkjhw5YvTq1cvw9/c3nJycDA8PD6N69erGxIkTbW4dHRcXZ4wcOdIoUqSI4ejoaBQsWNAYOnSoTR/DMIzChQsbzZo1S7IdSUa/fv1s2pK7/XloaKjh5uZmHDt2zGjUqJGRI0cOI1++fMbw4cON+Ph4m9fPmDHDKFGihOHs7GwEBAQYs2bNst5m/nHbfnBZ4u3IY2JijCFDhhgVKlQwPDw8DDc3N6NChQrG5MmTk7zuhx9+MCpVqmQ4OzsbuXPnNjp37my9dfrD+/Kw5GpMyaRJk4yAgADD0dHRyJcvn/Haa68ZV69eTXZ9Fy9efOz6Htc3pfdv+vTpRmBgoOHq6mp4eHgY5cqVM95++23j7Nmz1j5btmwxXnjhBcPV1dUoUKCA8fbbbxsrV640JBnr16+39gsNDU1ye/utW7cagYGBhpOTk8178rTHsHbt2kaZMmUe2Sfxlu7z58+3aT9//rzRr18/o2DBgoajo6Ph6+tr1K9f35g+fbpNv59++skoXbq09Zb1ibdhf9S2a9eubdSuXdum7fbt28Z7771n/Y75+voaL774onHs2DHDMAxjwYIFRqNGjQwfHx/DycnJKFSokNGnTx8jKirqscdBks0jW7Zshp+fn9G7d2/j/PnzSfrv3bvXaNOmjeHt7W04OzsbhQsXNtq3b2+sXbv2scfNMAxjzZo1RvXq1Q1XV1fD09PTCAkJMf7880+bPo/7LC5cuNCoUaOG4ebmZri5uRkBAQFGv379jIiIiEfu69WrV41u3boZefLkMdzd3Y3g4GDj8OHDRuHChY3Q0FBrv1mzZhmSjJ07d9q8PnG/HvzMxsfHGyNHjjTy589vuLq6GnXq1DEOHDiQZJ3J7YMkY8aMGSn22bBhgyHJmDBhQrLnRMMwjJs3bxqdOnUycubMaUiy+f7ExsYaY8eONcqUKWM4OzsbuXLlMgIDA42RI0ca0dHR1n6POg8+SoECBQxJST73hmEYLVq0MCQZr732WpJlN27cMIYOHWoUL17ccHJyMvLkyWNUq1bN+Oyzz4zY2FibuhK/74nWrl1rVKpUyXBycjKKFStm/Pe//zXefPNNw8XFxaZfSvuU3Pvy4YcfGs8884zh4OBgSDKOHz/+2H1fu3at0bJlS8PHx8fInj27kTdvXiMkJMT46aefrH1S+h4kvpeJ5wPDMIw///zTaNCggeHu7m7kyZPH6NWrl7Fv374k/dJy3rt48aLRqVMnw8PDw/Dy8jK6du1qbNmyxZBkzJs3z6bvsWPHjC5duhi+vr6Go6Oj8cwzzxjNmzc3FixY8NhjAQD/VBbDSKdZIgEA/wpdu3bVggULkr10CADw79SqVSsdPHjQeodIpGzJkiVq3bq1Nm/erOrVq9u7HACwK+aUAgAAAJBqd+7csXkeGRmpX3/9VXXq1LFPQZnYw8cqPj5eEydOlKenpypXrmynqgAg82BOKQAAAACpVrRoUXXt2lVFixbVyZMnNWXKFDk5Oentt9+2d2mZzuuvv647d+4oKChIMTExWrRokbZu3aqPP/74kXdMBYB/C0IpAAAAAKnWuHFjff/99zp37pycnZ0VFBSkjz/+WCVKlLB3aZlOvXr19Pnnn2vZsmW6e/euihcvrokTJ6p///72Lg0AMgXmlAIAAAAAAIDpmFMKAAAAAAAApiOUAgAAAAAAgOmy/JxSCQkJOnv2rDw8PGSxWOxdDgAAAAAAQJZmGIZu3LihAgUKyMEh5fFQWT6UOnv2rAoWLGjvMgAAAAAAAP5VTp8+LT8/vxSXZ/lQysPDQ9L9A+Hp6WnnagAAAAAAALK269evq2DBgtZMJiVZPpRKvGTP09OTUAoAAAAAAMAkj5tGiYnOAQAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmy/JzSgEAAAAA8G8XHx+vuLg4e5eBLMLR0VHZsmV76vUQSgEAAAAAkEUZhqFz587p2rVr9i4FWUzOnDnl6+v72MnMH4VQCgAAAACQZmPGjNHQoUM1YMAAjR8/XidOnFCRIkWS7fvjjz+qXbt2Ka7r0KFDeuedd7Rx40bdu3dPpUuX1sKFC1WoUCFJ0vTp0zV37lzt2bNHN27c0NWrV5UzZ86M2K0sJzGQ8vHxUY4cOZ4qQACk+0Hn7du3deHCBUlS/vz5n3hdhFIAAAAAgDTZuXOnpk2bpvLly1vbChYsqKioKJt+06dP16effqomTZqkuK5jx46pRo0a6tGjh0aOHClPT08dPHhQLi4u1j63b99W48aN1bhxYw0dOjT9dyiLio+PtwZS3t7e9i4HWYirq6sk6cKFC/Lx8XniS/kIpQAAAAAAqXbz5k117txZX3/9tT766CNre7Zs2eTr62vTd/HixWrfvr3c3d1TXN97772npk2b6pNPPrG2FStWzKbPwIEDJUkbNmx4+h34F0mcQypHjhx2rgRZUeLnKi4u7olDKe6+BwAAAABItX79+qlZs2Zq0KDBI/vt3r1b4eHh6tGjR4p9EhIS9Msvv+jZZ59VcHCwfHx89Pzzz2vJkiXpXPW/G5fsISOkx+eKUAoAAAAAkCrz5s3Tnj17FBYW9ti+M2bMUKlSpVStWrUU+1y4cEE3b97UmDFj1LhxY61atUqtW7dWmzZttHHjxvQsHUAmRCgFAAAAAHis06dPa8CAAfruu+9s5ntKzp07dzR37txHjpKS7o+UkqSWLVtq0KBBqlixot599101b95cU6dOTbfa8e9y4sQJWSwWhYeHp/o1s2fPTvfJ85+kjvRiGIZ69+6t3LlzW2uoU6eO9VJYSfL399f48eNNr+1BzCkFAAAAAHis3bt368KFC6pcubK1LT4+Xr/99psmTZqkmJgY67wyCxYs0O3bt9WlS5dHrjNPnjzKnj27SpcubdNeqlQpbd68Of13ApKkkBBzt7d0adpfc/r0aQ0fPlwrVqzQpUuXlD9/frVq1UoffPDBYydtT5x0P0+ePKne3ksvvaSmTZumvdCnVKdOHeuoQGdnZxUtWlT9+/dX3759n2q9K1as0OzZs7VhwwYVLVpUefLk0aJFi+To6JgeZacbRkoBAAAAAB6rfv362r9/v8LDw62PKlWqqHPnzgoPD7eZ6HjGjBlq0aKF8ubN+8h1Ojk5qWrVqoqIiLBpP3LkiAoXLpwh+4HM76+//lKVKlUUGRmp77//XkePHtXUqVO1du1aBQUF6cqVKym+NjY21jrpfvbsqR+H4+rqKh8fn/QoP8169eqlqKgo/fnnn2rfvr369eun77//Ptm+sbGxqVrnsWPHlD9/flWrVs16LHLnzi0PD4/0LP2pEUoBAAAAAB7Lw8NDZcuWtXm4ubnJ29tbZcuWtfY7evSofvvtN/Xs2TPZ9QQEBGjx4sXW50OGDNEPP/ygr7/+WkePHtWkSZO0dOlSm5Ei586dU3h4uI4ePSpJ1nDsUeEE/rn69esnJycnrVq1SrVr11ahQoXUpEkTrVmzRmfOnNF7771n7evv768PP/xQXbp0kaenp3r37p3sZXM///yzSpQoIRcXF9WtW1dz5syRxWLRtWvXJCW9fG/EiBGqWLGivv32W/n7+8vLy0sdOnTQjRs3rH1WrFihGjVqKGfOnPL29lbz5s117NixNO9vjhw55Ovrq6JFi2rEiBEqUaKEfv75Z0n3R1L1799fAwcOVJ48eRQcHCxJ2rhxo5577jk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IlkLRXDQmrB9uNUCixVbNOUVd5CwVd7r5gyl2qDNyCawff5CNbk0pdlBwXgcksfCKstZQciWv7x/uPl8+3CGH2qnt+dj4g8/nfZ/P+/N5j/f7eH0+79fbarWq3qp45JFHMDIyAo/Ho2xLSUlRvTWQlZWFxMREzJkzB6tXr4bD4cClS5dCPi8R3XwiElI5t9sNo9EIo9GobLNYLJg2bZqq30xKSoLBYFB+T0hIUPXzBQUFqK+vV6akORwO5OfnK+NFe3s7KisrVf38mjVrcP78eVV/MX/+/Anr3N3djUuXLiErK0t1vNraWmXMcLvdWLhwoepzo8eha6moqIDL5UJzczMWLlyI7du3w2QyjVv+tddew0svvYTMzExUVVWpxi0AGB4ehs1mQ15eHnbu3DnhIiPFxcXw+XxwOBxYtGgRjh8/jvvvv1+ZQuhyufDggw8iLi4u6OfdbndI4+DYex1qG9F/C4NSRLehmJgYmEwmmEwmLFiwAPv378fFixexb98+Vbm7774bK1aswIsvvoi//voLy5cvv0U1JiIKzaOPPors7Gy8+eabAfu0Wm3APzHBEp+ODXL4V6cbu21sbpFrGRoaUqZYjP7xer2qaWDBpqD8m/zjgdVqRU1NDU6fPo0DBw6MW97/AKO8vBwDAwNYsmRJwPTAJ554Ah0dHfjqq69CqoNOp1PGJJPJBKPRqEyzi42NhcPhQGtrK06cOAHg/4nH9Xr9dV71xMa2g8FgQFtbG44cOYKEhASsX78eVqs1bKtlEVGg++67DxqN5qat2DZRP79y5UqICJqamtDf34+WlhZl6h5wtZ/fuHGjqo//9ttv4fV6ER0drZQLpZ/357lrampSHa+rqysgr9T1uOeee2AymWCz2XD8+HGsXbsWXV1d45bfsGEDOjs78eSTT6K5uRkWi0Xpk4Gr+QkzMzPR2NiIn376KaQ6GAwGrFy5Elu2bEF7eztsNhs2b94M4Ob172PvdahtRP8tDEoR3QE0Gg20Wq0qqbnfCy+8gFOnTqGwsBARERG3oHZERJNTVVWFhoaGgKBIfHw8fD6fKjDlT+B9M4xOqvvPP//gm2++gdlsBgCkpaWhs7MTSUlJqgCMyWSaVCAqNjYWM2fOhNPpVG13Op2wWCw3VH+tVou33noL69atCzoe+MXHx6OoqAjvv/8+duzYgb1796r2v/LKK6iqqkJOTk5AsvdQnT17Fr/99huqqqpgs9mQnJwc8GZaamoqWlpaxl1RSafTBeQrMZvNaG9vx8WLF5VtTqcTWq1WSWg+nilTpiAzMxNbt25FR0cHzp07h+bm5uu6PiK6cXFxccjOzobdblf9Tfv5g8Zmsxn9/f3o7+9X9nV1deGPP/6YVL8ZHR2NvLw8OBwOHDlyBPPmzUNaWpqyPy0tDR6PJ6CPN5lMqrdvx/K/lTm6v7JYLIiKisKPP/4YcCz/G19msxlnzpxRHWv0OBQqo9GIZ599NujDnNHmzp2L8vJyfPbZZ8jLy1MtcqHVanH48GE89NBDWLx4MQYGBiZVB41Gg+TkZKUdU1NT4XK5cOHChaDlzWbzdY2D19tGdGdjyxLdhi5fvgyfzwefzwe3243S0lLlKf5Yy5Ytw+DgICorK29BTYmIJi8lJQUFBQWorq5WbX/ssccwODiIrVu3oqenB3a7XbUE9o2y2+04ceIEzp49i+LiYvz+++/KtOfi4mJcuHABzz33HFpbW9HT04NPP/0Uzz///LiJXsdTUVGBd955B8eOHYPH48Ebb7wBl8uFsrKyG76GVatWISIiAna7Pej+9evX46OPPkJ3dzc6OzvR2NioBN5GKy0txebNm7FixQp8+eWXk67H7NmzodPpsGvXLvT29uLkyZPYtGmTqkxJSQn+/PNP5Ofn4+uvv4bX68Xhw4eVaXhJSUno6OiAx+PBr7/+ir///hsFBQWIjo5GUVERvvvuO3z++ecoLS3F6tWrce+9945bn8bGRlRXV8PlcqGvrw+1tbUYGRmZMJBFRP8uu92OK1euID09HfX19fB6vXC73aiurlamsmVmZirjQltbG86cOYPCwkJkZGSENJVutIKCAjQ1NaGmpkb1lhRwtX+sra3Fxo0b0dnZCbfbjaNHj2LdunXXPGZiYiI0Gg0aGxsxODiIoaEhGAwGvP766ygvL8ehQ4fQ09ODtrY27Nq1C4cOHQIAvPzyy/B6vaioqIDH40FdXR0OHjw4qevxKysrQ0NDQ9AV9YaHh1FSUoJTp06hr68PTqcTra2tAX1/REQEHA4HrFYrHn/8cfh8vqDncrlcyM3NxQcffICuri50d3fjwIEDqKmpQW5uLoCr08BnzJiBp556Ck6nE729vaivr1ceNlVUVODgwYPYs2cPvF4v3nvvPXz44YcTLuxxvW1Ed7hbm9KKiMYqKioSAMqPwWCQBQsWqBLb4hqJy5nonIhuN8ESWv/www+i0+lk7FeRPXv2iNFolJiYGCksLJQtW7YEJDofe6yMjAwpKytTbUtMTJTt27cr5wIgdXV1kp6eLjqdTiwWizQ3N6s+8/3338vTTz8t06ZNE71eL8nJyfLqq68qCWGDnSeYK1euyIYNG2TWrFkSGRkpVqtVPv74Y1WZUBOdj71WEZG3335b4uPjZWhoKCDR+aZNm8RsNoter5e4uDjJzc2V3t5e1X3wlxUReffdd8VgMIjT6Qxah2DJ5/3q6uokKSlJoqKiZNGiRXLy5MmA47e3t8vSpUuVhLk2m01JlvvLL79IVlaWTJ06VTVudXR0yOLFiyU6Olri4uJkzZo1SvL08e5LS0uLZGRkyPTp00Wv10tqaqocO3Zs/JtLRGEzMDAgxcXFSgLvWbNmSU5Ojuq7al9fn+Tk5EhMTIwYDAZZtWqVsriBSGgLYYhc7X8TEhIEgCoxt98nn3wiDz/8sOj1eomNjZX09HTZu3evsn+878yVlZUyY8YM0Wg0UlRUJCIiIyMjsmPHDpk3b55ERkZKfHy8ZGdnyxdffKF8rqGhQUwmk0RFRYnNZpOampqQEp37x6/RsrOzZfny5SKiTnR++fJlyc/PF6PRKDqdTmbOnCklJSUyPDwcUFbk6kIfeXl5Yjab5eeffw44z+DgoKxdu1YeeOABmTp1qhgMBklJSZFt27apFhk5d+6cPPPMMxIbGyt33XWXzJ8/X06fPq3s3717t8yZM0ciIyNl7ty5UltbqzrPePd6ojai/x6NSIgZ6IiIiIiIiIiIiG4STt8jIiIiIiIiIqKwY1CKiIiIiIiIiIjCjkEpIiIiIiIiIiIKOwaliIiIiIiIiIgo7BiUIiIiIiIiIiKisGNQioiIiIiIiIiIwo5BKSIiIiIiIiIiCjsGpYiIiIiIiIiIKOwYlCIiIiIiIiIiorBjUIqIiIiIiIiIiMKOQSkiIiIiIiIiIgo7BqWIiIiIiIiIiCjs/gfWO7+ejQ+sgwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Percentage Changes:\n",
      "Weight Change: -28.09%\n",
      "BMI Change: -28.10%\n",
      "Number of Risk Factors Change: -18.18%\n",
      "Converted Risk Score Change: -28.06%\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def get_risk_category(score):\n",
    "    if pd.isna(score):\n",
    "        return \"Unable to determine (No matching cohort)\"\n",
    "    score = int(score)\n",
    "    if score == 0:\n",
    "        return \"No Risk\"\n",
    "    elif 1 <= score <= 22:\n",
    "        return \"Low Risk\"\n",
    "    elif 23 <= score <= 44:\n",
    "        return \"Moderate Risk\"\n",
    "    elif 45 <= score <= 67:\n",
    "        return \"High Risk\"\n",
    "    else:\n",
    "        return \"Very High Risk\"\n",
    "\n",
    "def perform_cohort_analysis(gender, age, height, weight, bmi):\n",
    "    user_bmi_rounded = round(bmi)\n",
    "    \n",
    "    cohort = df_adult[\n",
    "        (df_adult['Gender'] == gender) &\n",
    "        (df_adult['BMI_Rounded'].between(user_bmi_rounded - 1, user_bmi_rounded + 1)) &\n",
    "        (df_adult['Age'].between(age - 1, age + 1))\n",
    "    ]\n",
    "    \n",
    "    cohort_size = len(cohort)\n",
    "    \n",
    "    if cohort_size > 0:\n",
    "        avg_health_risk_score = cohort['Health_Risk_Score'].mean()\n",
    "        avg_converted_risk_score = cohort['Converted_Risk_Score'].mean()\n",
    "        avg_risk_factors = cohort['Risk_Factors'].mean()\n",
    "        risk_category = get_risk_category(avg_converted_risk_score)\n",
    "    else:\n",
    "        avg_health_risk_score = avg_converted_risk_score = avg_risk_factors = np.nan\n",
    "        risk_category = \"Unable to determine (No matching cohort)\"\n",
    "    \n",
    "    return cohort_size, avg_health_risk_score, avg_converted_risk_score, risk_category, avg_risk_factors\n",
    "\n",
    "# Original user data (from previous input)\n",
    "print(\"Original Profile:\")\n",
    "print(f\"Gender: {user_gender}\")\n",
    "print(f\"Age: {user_age}\")\n",
    "print(f\"Height: {height} inches\")\n",
    "print(f\"Weight: {weight} pounds\")\n",
    "print(f\"BMI: {user_bmi:.2f}\")\n",
    "\n",
    "cohort_size, avg_health_risk_score, avg_converted_risk_score, risk_category, avg_risk_factors = perform_cohort_analysis(user_gender, user_age, height, weight, user_bmi)\n",
    "\n",
    "print(f\"Number of comparables in cohort: {cohort_size}\")\n",
    "print(f\"Average Health Risk Score: {avg_health_risk_score:.2f}\")\n",
    "print(f\"Average Converted Risk Score: {avg_converted_risk_score:.2f}\")\n",
    "print(f\"Risk Category: {risk_category}\")\n",
    "print(f\"Average Number of Risk Factors: {avg_risk_factors:.2f}\")\n",
    "\n",
    "# Get weight change input from user\n",
    "weight_change = float(input(\"\\nEnter weight change (negative for loss, positive for gain) in pounds: \"))\n",
    "\n",
    "# Calculate new weight and BMI\n",
    "new_weight = weight + weight_change\n",
    "new_bmi = (new_weight / height ** 2) * 703\n",
    "\n",
    "print(\"\\nNew Profile After Weight Change:\")\n",
    "print(f\"Gender: {user_gender}\")\n",
    "print(f\"Age: {user_age}\")\n",
    "print(f\"Height: {height} inches\")\n",
    "print(f\"New Weight: {new_weight} pounds\")\n",
    "print(f\"New BMI: {new_bmi:.2f}\")\n",
    "\n",
    "new_cohort_size, new_avg_health_risk_score, new_avg_converted_risk_score, new_risk_category, new_avg_risk_factors = perform_cohort_analysis(user_gender, user_age, height, new_weight, new_bmi)\n",
    "\n",
    "print(f\"Number of comparables in new cohort: {new_cohort_size}\")\n",
    "print(f\"New Average Health Risk Score: {new_avg_health_risk_score:.2f}\")\n",
    "print(f\"New Average Converted Risk Score: {new_avg_converted_risk_score:.2f}\")\n",
    "print(f\"New Risk Category: {new_risk_category}\")\n",
    "print(f\"New Average Number of Risk Factors: {new_avg_risk_factors:.2f}\")\n",
    "\n",
    "# Compare original and new profiles\n",
    "print(\"\\nComparison:\")\n",
    "print(f\"Weight Change: {weight_change:.2f} pounds\")\n",
    "print(f\"BMI Change: {new_bmi - user_bmi:.2f}\")\n",
    "print(f\"Health Risk Score Change: {new_avg_health_risk_score - avg_health_risk_score:.2f}\")\n",
    "print(f\"Converted Risk Score Change: {new_avg_converted_risk_score - avg_converted_risk_score:.2f}\")\n",
    "print(f\"Risk Factor Change: {new_avg_risk_factors - avg_risk_factors:.2f}\")\n",
    "\n",
    "# Visualization\n",
    "categories = ['BMI', 'Number of Risk Factors', 'Converted Risk Score']\n",
    "original_values = [user_bmi, avg_risk_factors, avg_converted_risk_score]\n",
    "new_values = [new_bmi, new_avg_risk_factors, new_avg_converted_risk_score]\n",
    "\n",
    "x = range(len(categories))\n",
    "width = 0.35\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "rects1 = ax.bar([i - width/2 for i in x], original_values, width, label='Original Profile', color='blue', alpha=0.7)\n",
    "rects2 = ax.bar([i + width/2 for i in x], new_values, width, label='New Profile', color='red', alpha=0.7)\n",
    "\n",
    "ax.set_ylabel('Values')\n",
    "ax.set_title('Comparison of Health Metrics Before and After Weight Change')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(categories)\n",
    "ax.legend()\n",
    "\n",
    "def autolabel(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate(f'{height:.2f}',\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Calculate and display percentage changes\n",
    "bmi_change_pct = ((new_bmi - user_bmi) / user_bmi) * 100\n",
    "risk_factors_change_pct = ((new_avg_risk_factors - avg_risk_factors) / avg_risk_factors) * 100\n",
    "converted_risk_score_change_pct = ((new_avg_converted_risk_score - avg_converted_risk_score) / avg_converted_risk_score) * 100\n",
    "\n",
    "print(\"\\nPercentage Changes:\")\n",
    "print(f\"Weight Change: {(weight_change / weight) * 100:.2f}%\")\n",
    "print(f\"BMI Change: {bmi_change_pct:.2f}%\")\n",
    "print(f\"Number of Risk Factors Change: {risk_factors_change_pct:.2f}%\")\n",
    "print(f\"Converted Risk Score Change: {converted_risk_score_change_pct:.2f}%\")"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Conclusion**\n",
    "\n",
    "This small project demonstrated the value of leveraging  metabolic profiles and related calculations in a synthetic dataset. This type of analysis greatly expands upon the binary Metabolic Syndrome classification. The potential of the Metabolic Syndrome variables is utilzed to a far greater extent than the simple binary classification model allows.  Changes to BMI status, number of risk factors and changes to the health risk score (along with a bar graph for visualization) are the chosen key metric points.  This is only one example of how individual metrics can be leveraged with comparison to large dataset profiles and related analysis. Additional profile predictions could include a large variety of metric comparisons and additional novel risk calculations associated with the data set. Similar analysis with real world datasets can be leveraged in a similar manner.  \n",
    "\n"
   ]
  }
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