{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ca95c55a",
   "metadata": {},
   "source": [
    "### Feature Summary\n",
    "\n",
    "Demographics\n",
    "Age, Gender (0 = Female, 1 = Male), BMI\n",
    "\n",
    "Lifestyle & Environmental (0–10 index)\n",
    "Smoking, Alcohol_Use, Obesity,\n",
    "Diet_Red_Meat, Diet_Salted_Processed, Fruit_Veg_Intake,\n",
    "Physical_Activity, Physical_Activity_Level,\n",
    "Air_Pollution, Occupational_Hazards, Calcium_Intake\n",
    "(Higher = greater exposure or intensity)\n",
    "\n",
    "Genetic / Medical Flags (0/1)\n",
    "Family_History, BRCA_Mutation, H_Pylori_Infection\n",
    "\n",
    "\n",
    "Engineered Fields\n",
    "Overall_Risk_Score — composite numeric index\n",
    "Risk_Level — categorical stratification of overall risk\n",
    "\n",
    "### Data Notes\n",
    "\n",
    "Prostate occurs only when Gender = 1 (male).\n",
    "\n",
    "A small number of male cases appear in Breast (rare but realistic).\n",
    "\n",
    "Risk_Level is moderately imbalanced (Medium majority).\n",
    "\n",
    "The composite score aligns directionally with exposure intensity\n",
    "(higher with smoking/pollution, lower with fruit/vegetable intake).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdbad880",
   "metadata": {},
   "source": [
    "### Identification\n",
    "\n",
    "| Column          | Meaning                                 | Example              | Explanation                                                  |\n",
    "| :-------------- | :-------------------------------------- | :------------------- | :----------------------------------------------------------- |\n",
    "| **Patient_ID**  | Unique ID for each patient              | `LU0000`             | Used just to identify each record — not used for prediction. |\n",
    "| **Cancer_Type** | The type of cancer diagnosed or studied | `Breast`, `Prostate` | Indicates which cancer the patient has or is at risk for.    |\n",
    "\n",
    "\n",
    "### Demographics\n",
    "\n",
    "| Column     | Meaning                                                  | Example | Explanation                                                                      |\n",
    "| :--------- | :------------------------------------------------------- | :------ | :------------------------------------------------------------------------------- |\n",
    "| **Age**    | Age of the patient                                       | `68`    | Self-explanatory. Older age often increases risk.                                |\n",
    "| **Gender** | Biological sex of the patient (`0 = Female`, `1 = Male`) | `0`     | Some cancers (like Prostate) are only in males, while Breast can appear in both. |\n",
    "| **BMI**    | Body Mass Index (weight relative to height)              | `28.0`  | Higher BMI often correlates with obesity and higher risk levels.                 |\n",
    "\n",
    "### Lifestyle & Environmental Factors\n",
    "\n",
    "(All are index scores from 0–10, where higher = greater exposure or intensity)\n",
    "\n",
    "| Column                      | Meaning                                         | Example    | Explanation                                                     |\n",
    "| :-------------------------- | :---------------------------------------------- | :--------- | :-------------------------------------------------------------- |\n",
    "| **Smoking**                 | Smoking intensity or frequency                  | `7`        | Higher means the person smokes more heavily.                    |\n",
    "| **Alcohol_Use**             | Alcohol consumption level                       | `2`        | Higher means drinks more often or heavily.                      |\n",
    "| **Obesity**                 | Obesity severity level                          | `8`        | Reflects how overweight the person is — higher = more obese.    |\n",
    "| **Diet_Red_Meat**           | Intake of red meat                              | `5`        | More red meat often linked with higher risk.                    |\n",
    "| **Diet_Salted_Processed**   | Intake of salty or processed foods              | `3`        | High intake can raise certain cancer risks.                     |\n",
    "| **Fruit_Veg_Intake**        | Fruit and vegetable consumption                 | `7`        | Higher is healthier — tends to **lower risk**.                  |\n",
    "| **Physical_Activity**       | Frequency or intensity of exercise              | `4`        | Higher = more active, generally lowers risk.                    |\n",
    "| **Physical_Activity_Level** | Activity rating (can be numeric or categorical) | `5` or `9` | Another indicator of how physically active a person is.         |\n",
    "| **Air_Pollution**           | Environmental air pollution exposure            | `6`        | High value means poor air quality where the person lives.       |\n",
    "| **Occupational_Hazards**    | Exposure to harmful substances at work          | `3`        | E.g., chemicals, dust, radiation — higher = more dangerous job. |\n",
    "| **Calcium_Intake**          | Level of calcium in diet                        | `0` or `5` | Higher may be beneficial depending on the cancer type.          |\n",
    "\n",
    "\n",
    "### Genetic / Medical Factors\n",
    "\n",
    "| Column                 | Meaning                                    | Example | Explanation                                                |\n",
    "| :--------------------- | :----------------------------------------- | :------ | :--------------------------------------------------------- |\n",
    "| **Family_History**     | 1 if cancer runs in the family, else 0     | `0`     | Indicates if close relatives had cancer.                   |\n",
    "| **BRCA_Mutation**      | 1 if BRCA gene mutation present            | `1`     | Strong genetic risk factor (especially for breast cancer). |\n",
    "| **H_Pylori_Infection** | 1 if Helicobacter pylori infection present | `0`     | Linked to stomach and digestive cancers.                   |\n",
    "\n",
    "### Engineered (Calculated) Features\n",
    "\n",
    "| Column                 | Meaning                                          | Example    | Explanation                                                                    |\n",
    "| :--------------------- | :----------------------------------------------- | :--------- | :----------------------------------------------------------------------------- |\n",
    "| **Overall_Risk_Score** | Combined numeric score of all risk factors       | `0.398696` | A number that summarizes how risky the profile is overall. Higher = more risk. |\n",
    "| **Risk_Level**         | Category of risk (e.g., `Low`, `Medium`, `High`) | `Medium`   | Derived from the Overall_Risk_Score — easier for interpretation.               |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cfa68452",
   "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>Patient_ID</th>\n",
       "      <th>Cancer_Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>Alcohol_Use</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>Family_History</th>\n",
       "      <th>Diet_Red_Meat</th>\n",
       "      <th>Diet_Salted_Processed</th>\n",
       "      <th>Fruit_Veg_Intake</th>\n",
       "      <th>Physical_Activity</th>\n",
       "      <th>Air_Pollution</th>\n",
       "      <th>Occupational_Hazards</th>\n",
       "      <th>BRCA_Mutation</th>\n",
       "      <th>H_Pylori_Infection</th>\n",
       "      <th>Calcium_Intake</th>\n",
       "      <th>Overall_Risk_Score</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Physical_Activity_Level</th>\n",
       "      <th>Risk_Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LU0000</td>\n",
       "      <td>Breast</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.398696</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LU0001</td>\n",
       "      <td>Prostate</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.424299</td>\n",
       "      <td>25.4</td>\n",
       "      <td>9</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LU0002</td>\n",
       "      <td>Skin</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.605082</td>\n",
       "      <td>28.6</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LU0003</td>\n",
       "      <td>Colon</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.318449</td>\n",
       "      <td>32.1</td>\n",
       "      <td>7</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LU0004</td>\n",
       "      <td>Lung</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.524358</td>\n",
       "      <td>25.1</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Patient_ID Cancer_Type  Age  Gender  Smoking  Alcohol_Use  Obesity  \\\n",
       "0     LU0000      Breast   68       0        7            2        8   \n",
       "1     LU0001    Prostate   74       1        8            9        8   \n",
       "2     LU0002        Skin   55       1        7           10        7   \n",
       "3     LU0003       Colon   61       0        6            2        2   \n",
       "4     LU0004        Lung   67       1       10            7        4   \n",
       "\n",
       "   Family_History  Diet_Red_Meat  Diet_Salted_Processed  Fruit_Veg_Intake  \\\n",
       "0               0              5                      3                 7   \n",
       "1               0              0                      3                 7   \n",
       "2               0              3                      3                 4   \n",
       "3               0              6                      2                 4   \n",
       "4               0              6                      3                10   \n",
       "\n",
       "   Physical_Activity  Air_Pollution  Occupational_Hazards  BRCA_Mutation  \\\n",
       "0                  4              6                     3              1   \n",
       "1                  1              3                     3              0   \n",
       "2                  1              8                    10              0   \n",
       "3                  6              4                     8              0   \n",
       "4                  9             10                     9              0   \n",
       "\n",
       "   H_Pylori_Infection  Calcium_Intake  Overall_Risk_Score   BMI  \\\n",
       "0                   0               0            0.398696  28.0   \n",
       "1                   0               5            0.424299  25.4   \n",
       "2                   0               6            0.605082  28.6   \n",
       "3                   0               8            0.318449  32.1   \n",
       "4                   0               5            0.524358  25.1   \n",
       "\n",
       "   Physical_Activity_Level Risk_Level  \n",
       "0                        5     Medium  \n",
       "1                        9     Medium  \n",
       "2                        2     Medium  \n",
       "3                        7        Low  \n",
       "4                        2     Medium  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "df=pd.read_csv(\"../Data/cancer-risk-factors.csv\")\n",
    "pd.set_option('display.max_columns',None)\n",
    "\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39375ea3",
   "metadata": {},
   "source": [
    "### Understanding the dataset by EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7e2282fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk_Level\n",
      "Medium    1574\n",
      "Low        324\n",
      "High       102\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "risk_counts=df['Risk_Level'].value_counts()\n",
    "\n",
    "print(risk_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3e72f17d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Risk_Level'].value_counts().plot(kind='bar',title='Distribution of Risk Levels',xlabel='Risk level',ylabel='Number of Patients')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a39a23",
   "metadata": {},
   "source": [
    "Highly imabalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6457cb9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender\n",
      "0    1022\n",
      "1     978\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "gender_counts=df['Gender'].value_counts()\n",
    "print(gender_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bb77545e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Gender'].map({0:'Female',1:'Male'}).value_counts().plot(kind='bar',title='Gender Distribution',xlabel='Gender',ylabel='Number of Patients')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "39643953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gender Cancer_Type  Patient_Count\n",
      "0  Female      Breast            455\n",
      "1  Female       Colon            197\n",
      "2  Female        Lung            238\n",
      "3  Female        Skin            132\n",
      "4    Male      Breast              5\n",
      "5    Male       Colon            221\n",
      "6    Male        Lung            289\n",
      "7    Male    Prostate            305\n",
      "8    Male        Skin            158\n"
     ]
    }
   ],
   "source": [
    "gender_cancer_counts=df.groupby(['Gender','Cancer_Type']).size().reset_index(name='Patient_Count')\n",
    "gender_cancer_counts['Gender']=gender_cancer_counts['Gender'].map({0:'Female',1:'Male'})\n",
    "print(gender_cancer_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "78cfffa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=gender_cancer_counts,x='Cancer_Type',y='Patient_Count',hue='Gender')\n",
    "plt.title(\"Cancer Types by Gender\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "797624ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Patient_ID Cancer_Type  Age  Gender  Smoking  Alcohol_Use  Obesity  \\\n",
      "433     BR0033        Lung   25       0        8            6        5   \n",
      "454     BR0054        Lung   29       0        8            6        7   \n",
      "621     BR0221        Lung   25       0        7           10        6   \n",
      "\n",
      "     Family_History  Diet_Red_Meat  Diet_Salted_Processed  Fruit_Veg_Intake  \\\n",
      "433               0              3                      5                 9   \n",
      "454               0              3                      0                 4   \n",
      "621               0              2                      7                 4   \n",
      "\n",
      "     Physical_Activity  Air_Pollution  Occupational_Hazards  BRCA_Mutation  \\\n",
      "433                  5              8                     9              0   \n",
      "454                  0              7                     2              0   \n",
      "621                  4              7                    10              0   \n",
      "\n",
      "     H_Pylori_Infection  Calcium_Intake  Overall_Risk_Score   BMI  \\\n",
      "433                   0               3            0.550154  28.9   \n",
      "454                   0               2            0.412898  23.9   \n",
      "621                   0               0            0.583443  27.9   \n",
      "\n",
      "     Physical_Activity_Level Risk_Level  \n",
      "433                        8     Medium  \n",
      "454                        6     Medium  \n",
      "621                        4     Medium  \n"
     ]
    }
   ],
   "source": [
    "young_patients=df[df['Age']<30]\n",
    "print(young_patients)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fb78ae0",
   "metadata": {},
   "source": [
    "showing all rows (patients) where age is less than 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "34606020",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Patients under 30 : 3\n"
     ]
    }
   ],
   "source": [
    "count_young=(df['Age']<30).sum()\n",
    "print(f\"Number of Patients under 30 :\",count_young)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b57719",
   "metadata": {},
   "source": [
    "Counting how many such patients exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "19f1045a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cancer_Type\n",
      "Lung    3\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "young_cancer_types=df[df['Age']<30]['Cancer_Type'].value_counts()\n",
    "print(young_cancer_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fd57ac3",
   "metadata": {},
   "source": [
    "This shows which cancer types appear among young patients and how many cases of each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "72df6fe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Gender Cancer_Type  Patient_Count\n",
      "0  Female        Lung              3\n"
     ]
    }
   ],
   "source": [
    "young_gender_cancer=df[df['Age']<30].groupby(['Gender','Cancer_Type']).size().reset_index(name='Patient_Count')\n",
    "young_gender_cancer['Gender']=young_gender_cancer['Gender'].map({0:'Female',1:'Male'})\n",
    "print(young_gender_cancer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf82c9d2",
   "metadata": {},
   "source": [
    "Females are affected in young age with Lung cancer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ed104ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk_Level                    High        Low     Medium\n",
      "Age                      63.745098  63.685185  63.125794\n",
      "Gender                    0.509804   0.493827   0.486658\n",
      "Smoking                   7.519608   2.845679   5.479670\n",
      "Alcohol_Use               7.519608   2.984568   5.296061\n",
      "Obesity                   7.274510   4.824074   6.118170\n",
      "Family_History            0.205882   0.138889   0.205210\n",
      "Diet_Red_Meat             7.362745   3.972222   5.299238\n",
      "Diet_Salted_Processed     7.039216   2.888889   4.747776\n",
      "Fruit_Veg_Intake          3.911765   5.666667   4.841169\n",
      "Physical_Activity         4.921569   3.669753   4.027319\n",
      "Air_Pollution             8.539216   2.947531   5.603558\n",
      "Occupational_Hazards      6.990196   3.364198   5.181067\n",
      "BRCA_Mutation             0.049020   0.037037   0.030496\n",
      "H_Pylori_Infection        0.245098   0.175926   0.197586\n",
      "Calcium_Intake            4.323529   3.651235   3.975222\n",
      "Overall_Risk_Score        0.699991   0.269458   0.476616\n",
      "BMI                      26.225490  25.814506  26.256544\n",
      "Physical_Activity_Level   5.186275   4.848765   4.940915\n"
     ]
    }
   ],
   "source": [
    "numeric_cols=df.select_dtypes(include='number').columns\n",
    "\n",
    "risk_means=df.groupby('Risk_Level')[numeric_cols].mean().T \n",
    "print(risk_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0cfd9fd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Air_Pollution              5.591685\n",
      "Smoking                    4.673929\n",
      "Alcohol_Use                4.535040\n",
      "Diet_Salted_Processed      4.150327\n",
      "Occupational_Hazards       3.625999\n",
      "Diet_Red_Meat              3.390523\n",
      "Obesity                    2.450436\n",
      "Physical_Activity          1.251816\n",
      "Calcium_Intake             0.672295\n",
      "Overall_Risk_Score         0.430533\n",
      "BMI                        0.410984\n",
      "Physical_Activity_Level    0.337509\n",
      "H_Pylori_Infection         0.069172\n",
      "Family_History             0.066993\n",
      "Age                        0.059913\n",
      "Gender                     0.015977\n",
      "BRCA_Mutation              0.011983\n",
      "Fruit_Veg_Intake          -1.754902\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "risk_diff=risk_means['High']- risk_means['Low']\n",
    "risk_diff=risk_diff.sort_values(ascending=False)\n",
    "\n",
    "print(risk_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a51cf2c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "risk_diff.sort_values(ascending=True).plot(kind='barh',figsize=(8,6),title=\"Top Factors Contributing to High Risk Level\")\n",
    "plt.xlabel('Difference (High-Low)')\n",
    "plt.ylabel('Feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04454b00",
   "metadata": {},
   "source": [
    "Air Pollution, Smoking, Alcohol use are top 3 contributing factors in High risk level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2fdb4650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk_Level\n",
      "High      26.225490\n",
      "Low       25.814506\n",
      "Medium    26.256544\n",
      "Name: BMI, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "bmi_by_risk=df.groupby('Risk_Level')['BMI'].mean()\n",
    "print(bmi_by_risk)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95b2fa7",
   "metadata": {},
   "source": [
    "This shows the average BMI for Low, Medium, and High risk groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "64b74dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df,x='Risk_Level',y='BMI')\n",
    "plt.title(\"BMI Distribution by Risk Level\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "63a3f76a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Between BMI and Overall Risk Score : 0.037\n"
     ]
    }
   ],
   "source": [
    "corr_value=df['BMI'].corr(df['Overall_Risk_Score'])\n",
    "print(f\"Correlation Between BMI and Overall Risk Score : {corr_value:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a56b3b",
   "metadata": {},
   "source": [
    "Interpretation:\n",
    "\n",
    "If correlation is positive (e.g., +0.5) → higher BMI is linked to higher risk\n",
    "\n",
    "If correlation is negative → higher BMI is linked to lower risk\n",
    "\n",
    "If near 0 → BMI doesn’t strongly affect risk level.\n",
    "\n",
    "Here, BMI doesn't strongly affect risk level as the correlation value is near to 0."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ai-ml",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}