cancer_probabilities_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:12: SyntaxWarning: invalid escape sequence '\\c'\n",
      "<>:12: SyntaxWarning: invalid escape sequence '\\c'\n",
      "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_7312\\2939644757.py:12: SyntaxWarning: invalid escape sequence '\\c'\n",
      "  cancer_data = pd.read_csv(\"cancer_dataset_kaggle\\cancer-probabilities.csv\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Sr No. Smoking Habit Drinking Habit Biking Habit Walking Habit  \\\n",
      "0        1         Heavy       Frequent          Low           Low   \n",
      "1        2    Occasional       Moderate         High          High   \n",
      "2        3           NaN            NaN         High          High   \n",
      "3        4         Heavy       Frequent          Low           Low   \n",
      "4        5      Moderate     Occasional       Medium        Medium   \n",
      "5        6           NaN            NaN         High          High   \n",
      "6        7         Heavy       Frequent          Low           Low   \n",
      "7        8      Moderate     Occasional       Medium        Medium   \n",
      "8        9           NaN            NaN         High          High   \n",
      "9       10         Heavy       Frequent          Low           Low   \n",
      "10      11      Moderate     Occasional       Medium        Medium   \n",
      "11      12           NaN            NaN         High          High   \n",
      "12      13         Heavy       Frequent          Low           Low   \n",
      "13      14      Moderate     Occasional       Medium        Medium   \n",
      "14      15           NaN            NaN         High          High   \n",
      "15      16         Heavy       Frequent          Low           Low   \n",
      "16      17      Moderate     Occasional       Medium        Medium   \n",
      "17      18           NaN            NaN         High          High   \n",
      "18      19         Heavy       Frequent          Low           Low   \n",
      "19      20      Moderate     Occasional       Medium        Medium   \n",
      "20      21           NaN            NaN         High          High   \n",
      "21      22         Heavy       Frequent          Low           Low   \n",
      "22      23      Moderate     Occasional       Medium        Medium   \n",
      "23      24           NaN            NaN         High          High   \n",
      "24      25         Heavy       Frequent          Low           Low   \n",
      "25      26      Moderate     Occasional       Medium        Medium   \n",
      "26      27           NaN            NaN         High          High   \n",
      "27      28         Heavy       Frequent          Low           Low   \n",
      "28      29      Moderate     Occasional       Medium        Medium   \n",
      "29      30           NaN            NaN         High          High   \n",
      "30      31         Heavy       Frequent          Low           Low   \n",
      "31      32      Moderate     Occasional       Medium        Medium   \n",
      "32      33           NaN            NaN         High          High   \n",
      "33      34         Heavy       Frequent          Low           Low   \n",
      "34      35      Moderate     Occasional       Medium        Medium   \n",
      "35      36           NaN            NaN         High          High   \n",
      "36      37         Heavy       Frequent          Low           Low   \n",
      "37      38      Moderate     Occasional       Medium        Medium   \n",
      "38      39           NaN            NaN         High          High   \n",
      "39      40         Heavy       Frequent          Low           Low   \n",
      "40      41      Moderate     Occasional       Medium        Medium   \n",
      "41      42           NaN            NaN         High          High   \n",
      "42      43         Heavy       Frequent          Low           Low   \n",
      "43      44      Moderate     Occasional       Medium        Medium   \n",
      "44      45           NaN            NaN         High          High   \n",
      "45      46         Heavy       Frequent          Low           Low   \n",
      "46      47      Moderate     Occasional       Medium        Medium   \n",
      "47      48           NaN            NaN         High          High   \n",
      "48      49         Heavy       Frequent          Low           Low   \n",
      "49      50      Moderate     Occasional       Medium        Medium   \n",
      "\n",
      "   Jogging Habit  Probability of Cancer  \n",
      "0            Low                   0.80  \n",
      "1           High                   0.20  \n",
      "2           High                   0.10  \n",
      "3            Low                   0.90  \n",
      "4         Medium                   0.40  \n",
      "5           High                   0.05  \n",
      "6            Low                   0.75  \n",
      "7         Medium                   0.30  \n",
      "8           High                   0.02  \n",
      "9            Low                   0.85  \n",
      "10        Medium                   0.45  \n",
      "11          High                   0.01  \n",
      "12           Low                   0.90  \n",
      "13        Medium                   0.35  \n",
      "14          High                   0.03  \n",
      "15           Low                   0.70  \n",
      "16        Medium                   0.40  \n",
      "17          High                   0.05  \n",
      "18           Low                   0.80  \n",
      "19        Medium                   0.30  \n",
      "20          High                   0.02  \n",
      "21           Low                   0.90  \n",
      "22        Medium                   0.40  \n",
      "23          High                   0.01  \n",
      "24           Low                   0.75  \n",
      "25        Medium                   0.35  \n",
      "26          High                   0.03  \n",
      "27           Low                   0.80  \n",
      "28        Medium                   0.40  \n",
      "29          High                   0.05  \n",
      "30           Low                   0.90  \n",
      "31        Medium                   0.30  \n",
      "32          High                   0.02  \n",
      "33           Low                   0.75  \n",
      "34        Medium                   0.45  \n",
      "35          High                   0.01  \n",
      "36           Low                   0.80  \n",
      "37        Medium                   0.35  \n",
      "38          High                   0.03  \n",
      "39           Low                   0.90  \n",
      "40        Medium                   0.40  \n",
      "41          High                   0.05  \n",
      "42           Low                   0.70  \n",
      "43        Medium                   0.30  \n",
      "44          High                   0.02  \n",
      "45           Low                   0.80  \n",
      "46        Medium                   0.40  \n",
      "47          High                   0.01  \n",
      "48           Low                   0.75  \n",
      "49        Medium                   0.35  \n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Cancer data is downloaded from Kaggle (https://www.kaggle.com/datasets/tusharkute/cancer-probabilities)\n",
    "This is a side-project (data visualisation, ML, etc.)\n",
    "'''\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "cancer_data = pd.read_csv(\"cancer_dataset_kaggle\\cancer-probabilities.csv\")\n",
    "print(cancer_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sr No.                     int64\n",
      "Smoking Habit             object\n",
      "Drinking Habit            object\n",
      "Biking Habit              object\n",
      "Walking Habit             object\n",
      "Jogging Habit             object\n",
      "Probability of Cancer    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# check attribute type for each features/variables\n",
    "print(cancer_data.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploratory Analysis: To identify potential correlations between lifestyle habits and cancer risk (https://www.kaggle.com/datasets/tusharkute/cancer-probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Smoking Habit', ylabel='Probability of Cancer'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigating potential correlations between categorical lifestyle habits\n",
    "# and probability of cancer (via boxplots)\n",
    "\n",
    "# to see correlation between Smoking Habit and cancer probability\n",
    "sns.boxplot(x=\"Smoking Habit\", y=\"Probability of Cancer\", data=cancer_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Drinking Habit', ylabel='Probability of Cancer'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to see correlation between Drinking Habit and cancer probability\n",
    "sns.boxplot(x=\"Drinking Habit\", y=\"Probability of Cancer\", data=cancer_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Drinking Habit', ylabel='Probability of Cancer'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to see correlation between Drinking Habit and cancer probability\n",
    "sns.boxplot(x=\"Drinking Habit\", y=\"Probability of Cancer\", data=cancer_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Biking Habit', ylabel='Probability of Cancer'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to see correlation between Biking Habit and cancer probability\n",
    "sns.boxplot(x=\"Biking Habit\", y=\"Probability of Cancer\", data=cancer_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Walking Habit', ylabel='Probability of Cancer'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to see correlation between Walking Habit and cancer probability\n",
    "sns.boxplot(x=\"Walking Habit\", y=\"Probability of Cancer\", data=cancer_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Jogging Habit', ylabel='Probability of Cancer'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rBQAA8LASh5t//OMfatu2rfLy8oosu3Tpktq1a6ePP/7Yo8UBAAC4q8ThZs6cOXr22WdVtWrVIsuqVaum5557Tm+++aZHiwMAAHBXicPNN998oy5dulx3eadOnbRr1y5P1AQAAFBqJQ43Z86c0ZUrV667/PLlyzpz5oxHigIAACitEoeb+vXra8uWLdddvmXLFkVFRXmkKAAAgNIqcbjp2bOnJk6cqOzs7CLLsrKylJiYqEcffdSjxQEAALirxPPcnDt3TnFxccrMzNQTTzyh6OhoSdLevXu1ePFi2Ww2ffXVVwoODi7XgsuKeW4AAKh43Dl/l3iG4uDgYG3cuFETJkzQsmXLnONratSooSeeeELJycleH2wAAID5lWqGYofDoVOnTsnhcKhOnTqyWCzlUVu5oOcGAICKp1x6bn7OYrGoTp06pSoOAACgPLn9VHAAAABvRrgBAACmQrgBAACmUqJwU6tWLZ06dUqSNHDgQJ07d65ciwIAACitEoWbgoIC5ebmSpIWLFhQ7JPBAQAAvEGJ7paKi4tTjx491KZNGzkcDo0ePVpBQUHFrjtv3jyPFggAAOCOEoWbRYsW6c9//rMOHjwoi8WinJwcem8AAIBXcnsSvwYNGmjLli2qXbt2edVUrpjEDwCAiqdcJ/E7fPhwqQsDAAAob6W6FXz9+vXq3r27GjVqpEaNGumhhx7SF1984enaAAAA3OZ2uFm0aJG6du2qqlWravTo0c7Bxffcc4+WLFlSHjUCAACUmNtjbm655RYNGTJEY8eOdWmfMWOG3n77be3Zs8ejBXoaY24AAKh43Dl/u91zc+jQIXXv3r1I+0MPPcR4HAAAYDi3w43NZlN6enqR9jVr1shms3mkKAAAgNJy+26pp59+WqNHj9aOHTvUoUMHSdLGjRs1f/58vfHGGx4vEAAAwB1uh5thw4YpPDxcr7/+uj744ANJV8fhLFu2TA8//LDHCwQAAHCH2wOKKzoGFAMAUPGU64BiAAAAb0a4AQAApkK4AQAApkK4AQAApuJ2uFm7dm151AEAAOARbt8Kft999+nmm2/WgAED1K9fPybuAwB4rby8PGVmZhpdhteIjIxUYGCg0WWUO7fDzQ8//KD33ntPCxYs0OTJk3X33Xdr0KBB6tGjh/z9/cujRgAASiUzM1NDhgwxugyvMXfuXDVp0sToMspdmea52bZtm9599129//77kqTHH39cgwYNUkxMjMcK9DTmuQGAysMbem6OHj2q5ORkTZw4UVFRUYbWUpF7btw5f7vdc/NzrVu3Vnh4uGrXrq1XX31V8+bN0+zZsxUXF6e0tDQ1b968LLsHAKBMAgMDvaanIioqymtqMbtS3S11+fJlLV++XA888ICioqK0evVqvfnmm8rOztaBAwcUFRWlxx57zNO1AgAA/Ca3e25GjRql999/Xw6HQ3369NG0adN06623OpdXq1ZNr732mm666SaPFgoAAFASboebb7/9VrNmzVLPnj0VEBBQ7DqhoaHcMg4AAAzh9mWppKQkPfbYY0WCzZUrV7RhwwZJUpUqVdS5c2fPVAgAAOAGt8PNXXfdpdOnTxdpz8nJ0V133eWRogAAAErL7XDjcDhksViKtP/000+qVq2aR4oCAAAorRKPuenZs6ckyWKxqH///i6XpQoLC7Vz50516NDB8xUCAAC4ocThxmq1SrracxMcHKygoCDnMn9/f91+++0aPHiw5ysEAABwQ4nDzbvvvitJql+/vsaNG8clKAAA4JXcvhU8KSmpPOoAAADwiBKFm9atWys9PV01a9bUbbfdVuyA4mu2bdvmseIAAADcVaJw8/DDDzsHEPfo0aM86wEAACiTEoWbn1+KKo/LUqmpqZo+fbqysrIUExOjWbNmKTY29je3W7p0qXr16qWHH35YH374ocfrAgAAFU+pHpzpScuWLVNCQoKSkpK0bds2xcTEqFu3bjpx4sSvbnfkyBGNGzdOd9555w2qFAAAVAQlCjc1a9ZUrVq1SvRy14wZMzR48GANGDBAzZo1U1pamqpWrap58+Zdd5vCwkL17t1bkydPVsOGDd0+JgAAMK8SXZaaOXNmuRy8oKBAW7du1YQJE5xtPj4+6tq1qzIyMq673UsvvaS6detq0KBB+uKLL371GPn5+crPz3e+z83NLXvhAIDflJ2drZycHKPLMNzRo0dd/qzMrFarwsLCyv04JQo3/fr1K5eDnzp1SoWFhUU+aFhYmPbu3VvsNv/+97/1zjvvaMeOHSU6RkpKiiZPnlzWUgEAbsjOztYTffrqckH+b69cSSQnJxtdguH8/AO06L2F5R5wShRucnNzFRIS4vz7r7m2Xnk4d+6c+vTpo7fffluhoaEl2mbChAlKSEhwvs/NzZXNZiuvEgEAuvow5csF+brUsLPsgVajy4EX8MnLkQ6tV05OjneEm5o1a+r48eOqW7euatSoUew8N9ceqFlYWFjig4eGhsrX11fZ2dku7dnZ2QoPDy+y/sGDB3XkyBF1797d2Wa3269+kCpVtG/fPv3ud79z2SYgIMDlOVgAgBvHHmiVvVrJfhkFPKVE4ebzzz93DhZeu3atxw7u7++vNm3aKD093Tl/jt1uV3p6ukaOHFlk/aZNm2rXrl0ubYmJiTp37pzeeOMNemQAAEDJwk3nzp2L/bsnJCQkqF+/fmrbtq1iY2M1c+ZMXbhwQQMGDJAk9e3bV/Xq1VNKSooCAwN16623umxfo0YNSSrSDgAAKie3ny0lSWfOnNE777yjPXv2SJKaNWumAQMGlOpW8Pj4eJ08eVIvvPCCsrKy1KpVK61atcp5PS4zM1M+PoZPxwMAACoIi8PhcLizwYYNG9S9e3dZrVa1bdtWkrR161adPXtWH3/8sTp16lQuhXpKbm6urFarcnJyynXwMwBUZvv379eQIUN0odlDjLmBJMnnwilV+/YjzZ07V02aNHF7e3fO32733IwYMULx8fGaM2eOfH19JV2dVG/48OEaMWJEkTExAAAAN5Lb4ebAgQNavny5M9hIkq+vrxISErRw4UKPFgcAqNh8Lp01ugR4iRv5XXA73LRu3Vp79uxRdHS0S/uePXsUExPjscIAABVf0OENRpeASqhE4Wbnzp3Ov48ePVpjxozRgQMHdPvtt0uSvvrqK6WmpurVV18tnyoBABXSpQadZA+qYXQZ8AI+l87esLBbonDTqlUrWSwW/Xzs8bPPPltkvccff1zx8fGeqw4AUKHZg2owoBg3XInCzeHDh8u7DgAAAI8oUbiJiooq7zoAAAA8olST+EnSt99+q8zMTBUUFLi0P/TQQ2UuCgAAoLTcDjeHDh3SI488ol27drmMw7n2ME13HpwJAADgaW4/12DMmDFq0KCBTpw4oapVq2r37t3asGGD2rZtq3Xr1pVDiQAAACXnds9NRkaGPv/8c4WGhsrHx0c+Pj664447lJKSotGjR2v79u3lUScAAECJuN1zU1hYqODgYElSaGiofvzxR0lXBx3v27fPs9UBAAC4ye2em1tvvVVff/21GjRooPbt22vatGny9/fX3Llz1bBhw/KoEQAAoMTcDjeJiYm6cOGCJOmll17SH/7wB915552qXbu2li1b5vECAQAA3OF2uOnWrZvz740aNdLevXt1+vRp1axZ03nHFAAAgFFKPc+NJB07dkySZLPZPFIMAMBcfPJyjC4BXuJGfhfcDjdXrlzR5MmT9Ze//EXnz5+XJFWvXl2jRo1SUlKS/Pz8PF4kAKBisVqt8vMPkA6tN7oUeBE//wBZrdZyP47b4WbUqFFasWKFpk2bpri4OElXbw9/8cUX9dNPP2nOnDkeLxIAULGEhYVp0XsLlZNDz83Ro0eVnJysiRMnVvrHGVmtVoWFhZX7cdwON0uWLNHSpUt1//33O9tatmwpm82mXr16EW4AAJKuBpwbcSKrKKKiotSkSROjy6gU3J7nJiAgQPXr1y/S3qBBA/n7+3uiJgAAgFJzO9yMHDlSL7/8svLz851t+fn5Sk5O1siRIz1aHAAAgLtKdFmqZ8+eLu/XrFmjm2++WTExMZKkr7/+WgUFBbrnnns8XyEAAIAbShRufjmy+dFHH3V5z63gAADAW5Qo3Lz77rvlXQcAAIBHlHoSv5MnTzoflBkdHa06dep4rCgAAIDScntA8YULFzRw4EBFRESoU6dO6tSpk2666SYNGjRIFy9eLI8aAQAASsztcJOQkKD169fr448/1tmzZ3X27Fn985//1Pr16/X000+XR40AAAAl5vZlqb///e9avny5unTp4mx74IEHFBQUpD/96U9M4gcAAAzlds/NxYsXi51xsm7dulyWAgAAhnO75yYuLk5JSUlauHChAgMDJUmXLl3S5MmTnc+aAgDAG+Tl5SkzM9PQGo4ePeryp5EiIyOd524zszgcDoc7G+zatUv33Xef8vPzXSbxCwwM1OrVq9W8efNyKdRTcnNzZbValZOTo5CQEKPLAQCUo/3792vIkCFGl+E15s6dW2Gfb+XO+dvtcCNdvTS1ePFi7d27V5J0yy23qHfv3goKCipdxTcQ4QYAKg9v6LnxJhW558ad87dbl6UuX76spk2b6pNPPtHgwYPLVCQAAOUtMDCwwvZUoPTcGlDs5+envLy88qoFAACgzNy+W2rEiBGaOnWqrly5Uh71AAAAlInbd0tt3rxZ6enp+te//qUWLVqoWrVqLstXrFjhseIAAADc5Xa4qVGjRpGnggMAAHgLt8MNTwgHAADerMRjbux2u6ZOnaqOHTuqXbt2Gj9+vC5dulSetQEAALitxOEmOTlZzz//vKpXr6569erpjTfe0IgRI8qzNgAAALeVONwsXLhQs2fP1urVq/Xhhx/q448/1uLFi2W328uzPgAAALeUONxkZmbqgQcecL7v2rWrLBaLfvzxx3IpDAAAoDRKHG6uXLlSZMpmPz8/Xb582eNFAQAAlFaJ75ZyOBzq37+/AgICnG15eXkaOnSoy1w3zHMDAACMVOJw069fvyJtTzzxhEeLAQAAKKsShxvmtwEAABWB28+WAgAA8GaEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCpVjC4AADyhsLBQO3fu1OnTp1WrVi21bNlSvr6+RpcFwACEGwAV3oYNGzR79mxlZWU528LDwzV8+HB16tTJwMoAGMErLkulpqaqfv36CgwMVPv27bVp06brrvv222/rzjvvVM2aNVWzZk117dr1V9cHYG4bNmxQUlKSGjZsqNTUVK1cuVKpqalq2LChkpKStGHDBqNLBHCDGR5uli1bpoSEBCUlJWnbtm2KiYlRt27ddOLEiWLXX7dunXr16qW1a9cqIyNDNptNv//97/XDDz/c4MoBGK2wsFCzZ89WXFycpkyZoubNm6tq1apq3ry5pkyZori4OM2ZM0eFhYVGlwrgBjI83MyYMUODBw/WgAED1KxZM6Wlpalq1aqaN29esesvXrxYw4cPV6tWrdS0aVP99a9/ld1uV3p6erHr5+fnKzc31+UFwBx27typrKws9e7dWz4+rj/OfHx81Lt3bx0/flw7d+40qEIARjA03BQUFGjr1q3q2rWrs83Hx0ddu3ZVRkZGifZx8eJFXb58WbVq1Sp2eUpKiqxWq/Nls9k8UjsA450+fVqS1KBBg2KXX2u/th6AysHQcHPq1CkVFhYqLCzMpT0sLMxlYOCvee6553TTTTe5BKSfmzBhgnJycpyvY8eOlbluAN7h2i81hw8fLnb5tfbr/fIDwJwMvyxVFq+++qqWLl2qf/zjHwoMDCx2nYCAAIWEhLi8AJhDy5YtFR4ersWLF8tut7sss9vtWrx4sSIiItSyZUuDKgRgBEPDTWhoqHx9fZWdne3Snp2drfDw8F/d9rXXXtOrr76qf/3rX/zgAiopX19fDR8+XBkZGUpMTNTu3bt18eJF7d69W4mJicrIyNCwYcOY7waoZCwOh8NhZAHt27dXbGysZs2aJenqb1uRkZEaOXKkxo8fX+w206ZNU3JyslavXq3bb7/drePl5ubKarUqJyeHXhzAJIqb5yYiIkLDhg1jnhvAJNw5fxs+iV9CQoL69euntm3bKjY2VjNnztSFCxc0YMAASVLfvn1Vr149paSkSJKmTp2qF154QUuWLFH9+vWdP8yqV6+u6tWrG/Y5ABinU6dO6tixIzMUA5DkBeEmPj5eJ0+e1AsvvKCsrCy1atVKq1atcg4yzszMdLnFc86cOSooKNAf//hHl/0kJSXpxRdfvJGlA/Aivr6+uu2224wuA4AXMPyy1I3GZSkAACoed87fFfpuKQAAgF8i3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFOpYnQBAOAJhYWF2rlzp06fPq1atWqpZcuW8vX1NbosAAYg3ACo8DZs2KDZs2crKyvL2RYeHq7hw4erU6dOBlYGwAhclgJQoW3YsEFJSUlq2LChUlNTtXLlSqWmpqphw4ZKSkrShg0bjC4RwA1mcTgcDqOLuJFyc3NltVqVk5OjkJAQo8sBUAaFhYXq3bu3GjZsqClTpsjH57+/r9ntdiUmJurw4cNatGgRl6iACs6d8zc9NwAqrJ07dyorK0u9e/d2CTaS5OPjo969e+v48ePauXOnQRUCMALhBkCFdfr0aUlSgwYNil1+rf3aegAqB8INgAqrVq1akqTDhw8Xu/xa+7X1AFQOhBsAFVbLli0VHh6uxYsXy263uyyz2+1avHixIiIi1LJlS4MqBGAEwg2ACsvX11fDhw9XRkaGEhMTtXv3bl28eFG7d+9WYmKiMjIyNGzYMAYTA5UMd0sBqPCKm+cmIiJCw4YNY54bwCTcOX8TbgCYAjMUA+bmzvmbGYoBmIKvr69uu+02o8sA4AUYcwMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFZ0sBJpGdna2cnBzDjp+fn+/yVO7KLjw8XAEBAYYd32q1KiwszLDjA0Yi3AAmkJ2drSf69NXlgnyjS4GX8PMP0KL3FhJwUCkRbgATyMnJ0eWCfF1q2Fn2QKsxRdivyCf/vDHH9kL2gOqSjzE/Yn3ycqRD65WTk0O4QaVEuAFMxB5olb1aqHHHDzbs0ADgxIBiAABgKoQbAABgKoQbAABgKoy5AUzE59JZo0uAF+B7gMqOcAOYSNDhDUaXAACGI9wAJnKpQSfZg2oYXQYM5nPpLEEXlRrhBjARe1ANQ28FBwBvQLgBTMQnz7jHL8B78D1AZUe4AUzAarXKzz9AOrTe6FLgJfz8A2S1GjRbNWAwwg1gAmFhYVr03kJDH5zpDY4ePark5GRNnDhRUVFRRpdjKB6cicqMcAOYRFhYGCez/xMVFaUmTZoYXQYAgxBuAHhEXl6eMjMzDa3h6NGjLn8aKTIyUoGBgUaXAVRKXhFuUlNTNX36dGVlZSkmJkazZs1SbGzsddf/29/+pkmTJunIkSNq3Lixpk6dqgceeOAGVgzglzIzMzVkyBCjy5AkJScnG12C5s6dS+8RYBDDw82yZcuUkJCgtLQ0tW/fXjNnzlS3bt20b98+1a1bt8j6X375pXr16qWUlBT94Q9/0JIlS9SjRw9t27ZNt956qwGfAIB0tadi7ty5RpfhNSIjI40uAai0LA6Hw2FkAe3bt1e7du305ptvSpLsdrtsNptGjRql8ePHF1k/Pj5eFy5c0CeffOJsu/3229WqVSulpaX95vFyc3NltVqVk5OjkJAQz30QAABQbtw5fxv64MyCggJt3bpVXbt2dbb5+Pioa9euysjIKHabjIwMl/UlqVu3btddPz8/X7m5uS4vAABgXoaGm1OnTqmwsLDIHR5hYWHKysoqdpusrCy31k9JSZHVanW+bDabZ4oHAABeydBwcyNMmDBBOTk5ztexY8eMLgkAAJQjQwcUh4aGytfXV9nZ2S7t2dnZCg8PL3ab8PBwt9YPCAhQQECAZwoGAABez9CeG39/f7Vp00bp6enONrvdrvT0dMXFxRW7TVxcnMv6kvTZZ59dd30AAFC5GH4reEJCgvr166e2bdsqNjZWM2fO1IULFzRgwABJUt++fVWvXj2lpKRIksaMGaPOnTvr9ddf14MPPqilS5dqy5Yt3IIKAAAkeUG4iY+P18mTJ/XCCy8oKytLrVq10qpVq5yDhjMzM+Xj898Opg4dOmjJkiVKTEzU888/r8aNG+vDDz9kjhsAACDJC+a5udGY5wYAgIqnwsxzAwAA4GmEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqG3wp+o127OYwHaAIAUHFcO2+X5CbvShduzp07J0k8QBMAgAro3Llzslqtv7pOpZvnxm6368cff1RwcLAsFovR5VRoubm5stlsOnbsGHMGwSvwnYQ34nvpGQ6HQ+fOndNNN93kMrlvcSpdz42Pj49uvvlmo8swlZCQEP7DwqvwnYQ34ntZdr/VY3MNA4oBAICpEG4AAICpEG5QagEBAUpKSlJAQIDRpQCS+E7CO/G9vPEq3YBiAABgbvTcAAAAUyHcAAAAUyHcAAAAUyHcAKiw5s+frxo1ari1Tf/+/dWjR49yqQf4uXXr1slisejs2bOSSvd9RekQbuCCH/zwFtf7Lv78hBEfH6/9+/ff+OJgCv3795fFYtHQoUOLLBsxYoQsFov69+/vsePxfb1xCDcAKqygoCDVrVvX6DJQgdlsNi1dulSXLl1ytuXl5WnJkiWKjIz06LH4vt44hBuU2Pr16xUbG6uAgABFRERo/PjxunLliiTpk08+UY0aNVRYWChJ2rFjhywWi8aPH+/c/sknn9QTTzxhSO0wp+K6+adMmaK6desqODhYTz75pMaPH69WrVoV2fa1115TRESEateurREjRujy5cs3pmh4ldatW8tms2nFihXOthUrVigyMlK33Xabs81utyslJUUNGjRQUFCQYmJitHz5cpd9rVy5Uk2aNFFQUJDuuusuHTlyxGX5L7+vxfVOPvXUU+rSpYvzfZcuXTRq1Cg99dRTqlmzpsLCwvT222/rwoULGjBggIKDg9WoUSN9+umnZf63MBPCDUrkhx9+0AMPPKB27drp66+/1pw5c/TOO+9oypQpkqQ777xT586d0/bt2yVdDUKhoaFat26dcx/r1693+U8LeNrixYuVnJysqVOnauvWrYqMjNScOXOKrLd27VodPHhQa9eu1YIFCzR//nzNnz//xhcMrzBw4EC9++67zvfz5s3TgAEDXNZJSUnRwoULlZaWpt27d2vs2LF64okntH79eknSsWPH1LNnT3Xv3l07duxwBmtPWLBggUJDQ7Vp0yaNGjVKw4YN02OPPaYOHTpo27Zt+v3vf68+ffro4sWLHjmeKTiAn+nXr5/j4YcfLtL+/PPPO6Kjox12u93Zlpqa6qhevbqjsLDQ4XA4HK1bt3ZMnz7d4XA4HD169HAkJyc7/P39HefOnXN8//33DkmO/fv335DPgYqvX79+Dl9fX0e1atVcXoGBgQ5JjjNnzjjeffddh9VqdW7Tvn17x4gRI1z207FjR0dMTIzLfqOiohxXrlxxtj322GOO+Pj48v5I8DLXft6dOHHCERAQ4Dhy5IjjyJEjjsDAQMfJkycdDz/8sKNfv36OvLw8R9WqVR1ffvmly/aDBg1y9OrVy+FwOBwTJkxwNGvWzGX5c8895/yuOhyOIt/X4n7ejhkzxtG5c2fn+86dOzvuuOMO5/srV644qlWr5ujTp4+z7fjx4w5JjoyMjDL8a5gLPTcokT179iguLk4Wi8XZ1rFjR50/f17ff/+9JKlz585at26dHA6HvvjiC/Xs2VO33HKL/v3vf2v9+vW66aab1LhxY6M+Aiqgu+66Szt27HB5/fWvf73u+vv27VNsbKxL2y/fS1Lz5s3l6+vrfB8REaETJ054rnBUKHXq1NGDDz6o+fPn691339WDDz6o0NBQ5/IDBw7o4sWLuvfee1W9enXna+HChTp48KCkqz8j27dv77LfuLg4j9TXsmVL5999fX1Vu3ZttWjRwtkWFhYmSXyHf6aK0QXAPLp06aJ58+bp66+/lp+fn5o2baouXbpo3bp1OnPmjDp37mx0iahgqlWrpkaNGrm0XQvTZeHn5+fy3mKxyG63l3m/qLgGDhyokSNHSpJSU1Ndlp0/f16S9L//+7+qV6+ey7KyPC/Kx8dHjl88Aam4sV/FfV9/3nbtl06+w/9Fzw1K5JZbblFGRobLf8SNGzcqODhYN998s6T/jrv585//7Awy18LNunXrGG+DchcdHa3Nmze7tP3yPVCc++67TwUFBbp8+bK6devmsqxZs2YKCAhQZmamGjVq5PKy2WySrv6M3LRpk8t2X3311a8es06dOjp+/LhL244dO8r+YUC4QVE5OTlFLgUMGTJEx44d06hRo7R3717985//VFJSkhISEuTjc/VrVLNmTbVs2VKLFy92BplOnTpp27Zt2r9/Pz03KHejRo3SO++8owULFui7777TlClTtHPnTpfLqUBxfH19tWfPHn377bculywlKTg4WOPGjdPYsWO1YMECHTx4UNu2bdOsWbO0YMECSdLQoUP13Xff6ZlnntG+ffu0ZMmS3xykfvfdd2vLli1auHChvvvuOyUlJembb74pr49YqXBZCkWsW7fO5RZISRo0aJBWrlypZ555RjExMapVq5YGDRqkxMREl/U6d+6sHTt2OMNNrVq11KxZM2VnZys6OvpGfQRUUr1799ahQ4c0btw45eXl6U9/+pP69+9f5DdqoDghISHXXfbyyy+rTp06SklJ0aFDh1SjRg21bt1azz//vCQpMjJSf//73zV27FjNmjVLsbGxeuWVVzRw4MDr7rNbt26aNGmSnn32WeXl5WngwIHq27evdu3a5fHPVtlYHL+84AcAJnLvvfcqPDxc7733ntGlALhB6LkBYBoXL15UWlqaunXrJl9fX73//vtas2aNPvvsM6NLA3AD0XMDwDQuXbqk7t27a/v27crLy1N0dLQSExPVs2dPo0sDcAMRbgAAgKlwtxQAADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0Ar/Liiy+qVatWRpfhlv79+6tHjx6/uk6XLl301FNP3ZB6gMqOcAPgukpy0va0cePGKT09vdyPc70QdeTIEVksFo8/wHDFihV6+eWXne/r16+vmTNnevQYAK5ihmIAXqV69eqqXr260WV4XK1atYwuAag06LkBUCL5+fkaPXq06tatq8DAQN1xxx3avHmzyzofffSRGjdurMDAQN11111asGCBLBaLzp4961zn7bffls1mU9WqVfXII49oxowZqlGjhnP5L3tUrvUevfbaa4qIiFDt2rU1YsQIXb582bnO8ePH9eCDDyooKEgNGjTQkiVLPNYzUlhYqEGDBqlBgwYKCgpSdHS03njjjWLXnTx5surUqaOQkBANHTpUBQUFzmU/vyzVpUsXHT16VGPHjpXFYuGp5YCH0XMDoESeffZZ/f3vf9eCBQsUFRWladOmqVu3bjpw4IBq1aqlw4cP649//KPGjBmjJ598Utu3b9e4ceNc9rFx40YNHTpUU6dO1UMPPaQ1a9Zo0qRJv3nstWvXKiIiQmvXrtWBAwcUHx+vVq1aafDgwZKkvn376tSpU1q3bp38/PyUkJCgEydOeORz2+123Xzzzfrb3/6m2rVr68svv9SQIUMUERGhP/3pT8710tPTFRgYqHXr1unIkSMaMGCAateureTk5CL7XLFihWJiYjRkyBDnZwDgOYQbAL/pwoULmjNnjubPn6/7779f0tUemM8++0zvvPOOnnnmGb311luKjo7W9OnTJUnR0dH65ptvXE7us2bN0v333+8MPU2aNNGXX36pTz755FePX7NmTb355pvy9fVV06ZN9eCDDyo9PV2DBw/W3r17tWbNGm3evFlt27aVJP31r39V48aNf/Nz7dq1q8glsF8+kcbPz0+TJ092vm/QoIEyMjL0wQcfuIQbf39/zZs3T1WrVlXz5s310ksv6ZlnntHLL78sHx/XTvJatWrJ19dXwcHBCg8P/806AbiHy1IAftPBgwd1+fJldezY0dnm5+en2NhY7dmzR5K0b98+tWvXzmW72NhYl/f79u0r0vbL98Vp3ry5fH19ne8jIiKcPTP79u1TlSpV1Lp1a+fyRo0aqWbNmr+53+joaO3YscPltXLlyiLrpaamqk2bNqpTp46qV6+uuXPnKjMz02WdmJgYVa1a1fk+Li5O58+f17Fjx36zDgCeRc8NAK/n5+fn8t5ischut5d5v/7+/mrUqJFLW5Uqrj8Wly5dqnHjxun1119XXFycgoODNX36dP3nP/8p8/EBlA96bgD8pt/97nfy9/fXxo0bnW2XL1/W5s2b1axZM0lXe0G2bNnist0vBxxHR0cXafvle3dFR0frypUr2r59u7PtwIEDOnPmTJn2e83GjRvVoUMHDR8+XLfddpsaNWqkgwcPFlnv66+/1qVLl5zvv/rqK1WvXl02m63Y/fr7+6uwsNAjNQJwRbgB8JuqVaumYcOG6ZlnntGqVav07bffavDgwbp48aIGDRokSfp//+//ae/evXruuee0f/9+ffDBB5o/f74kOe8GGjVqlFauXKkZM2bou+++01tvvaVPP/20THcLNW3aVF27dtWQIUO0adMmbd++XUOGDFFQUJBH7kJq3LixtmzZotWrV2v//v2aNGlSsYGsoKBAgwYN0rfffquVK1cqKSlJI0eOLDLe5pr69etrw4YN+uGHH3Tq1Kky1wngvwg3AK7Lbrc7L9O8+uqrevTRR9WnTx+1bt1aBw4c0OrVq51jWxo0aKDly5drxYoVatmypebMmaOJEydKkgICAiRJHTt2VFpammbMmKGYmBitWrVKY8eOVWBgYJnqXLhwocLCwtSpUyc98sgjGjx4sIKDg8u8X+lqaOvZs6fi4+PVvn17/fTTTxo+fHiR9e655x41btxYnTp1Unx8vB566CG9+OKL193vSy+9pCNHjuh3v/ud6tSpU+Y6AfyXxfHLWwMA4P/cd999atSokd58881SbZ+cnKy0tLRfHVR77Y6nL774orRlFvH999/LZrNpzZo1uueeezy2XwAVAwOKARRx5swZbdy4UevWrdPQoUNLvN3s2bPVrl071a5dWxs3btT06dM1cuRIl3Vee+013XvvvapWrZo+/fRTLViwQLNnzy5TvZ9//rnOnz+vFi1a6Pjx43r22WdVv359derUqUz7BVAxEW4AFDFw4EBt3rxZTz/9tB5++OESb/fdd99pypQpOn36tCIjI/X0009rwoQJLuts2rRJ06ZN07lz59SwYUP95S9/0ZNPPlmmei9fvqznn39ehw4dUnBwsDp06KDFixcXucsKQOXAZSkAAGAqDCgGAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACm8v8BIHR2lUqylKAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to see correlation between Jogging Habit and cancer probability\n",
    "sns.boxplot(x=\"Jogging Habit\", y=\"Probability of Cancer\", data=cancer_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding: \n",
    "1. The boxplot between 'Smoking Habit' and 'Probability of Cancer' indicates a positive correlation between the likelihood of getting cancer and the severity of the smoking habit.\n",
    "\n",
    "2. The boxplot between 'Drinking Habit' and 'Probability of Cancer' also indicates a positive correlation between the likelihood of getting cancer and the frequency of drinking, similar to smoking habit.\n",
    "\n",
    "3. For biking habit, we can see a negative correlation between the frequency of biking habit and the likelihood of developing cancer. This can be seen as people with high biking frequency has the least likelihood of getting cancer, and vice versa. Unlike correlations between smoking habit and drinking habit with regards to probability of cancer.\n",
    "\n",
    "4. There is also a negative correlation seen between walking habit and probability of cancer, as people who frequently walk have the least likelihood of developing cancer. But people who rarely walk have higher likelihood of developing cancer.\n",
    "\n",
    "5. There is also a negative correlation seen between jogging habit and probability of cancer, as the frequency of jogging increases, the likelihood of developing cancer decreases, and vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference List:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Srivatsa, V. (2022, August 26). Data Analysis with Python - Exploratory Data Analysis (EDA) - Part 2. Medium. https://medium.com/@varunsrivatsa27/data-analysis-with-python-exploratory-data-analysis-eda-part-2-c2460874d49f\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}