Disease Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a24a28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc91f06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')\n",
    "matplotlib.rcParams['font.size'] = 14\n",
    "matplotlib.rcParams['figure.facecolor'] = '#00000000'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "39005e7b",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>B.P</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Naveen</td>\n",
       "      <td>70</td>\n",
       "      <td>M</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>'120/80'</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mahesh</td>\n",
       "      <td>90</td>\n",
       "      <td>M</td>\n",
       "      <td>155</td>\n",
       "      <td>60</td>\n",
       "      <td>'90/60'</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Raja</td>\n",
       "      <td>78</td>\n",
       "      <td>M</td>\n",
       "      <td>168</td>\n",
       "      <td>80</td>\n",
       "      <td>120/60'</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Leela</td>\n",
       "      <td>67</td>\n",
       "      <td>F</td>\n",
       "      <td>165</td>\n",
       "      <td>88</td>\n",
       "      <td>130/60'</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shakeela</td>\n",
       "      <td>65</td>\n",
       "      <td>F</td>\n",
       "      <td>158</td>\n",
       "      <td>60</td>\n",
       "      <td>'105/80'</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sita</td>\n",
       "      <td>45</td>\n",
       "      <td>F</td>\n",
       "      <td>148</td>\n",
       "      <td>65</td>\n",
       "      <td>'140/60'</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Singh</td>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>171</td>\n",
       "      <td>73</td>\n",
       "      <td>'150/90'</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Riya</td>\n",
       "      <td>12</td>\n",
       "      <td>F</td>\n",
       "      <td>150</td>\n",
       "      <td>45</td>\n",
       "      <td>'135/90'</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dev</td>\n",
       "      <td>56</td>\n",
       "      <td>M</td>\n",
       "      <td>160</td>\n",
       "      <td>78</td>\n",
       "      <td>'120/105'</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sach</td>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "      <td>158</td>\n",
       "      <td>85</td>\n",
       "      <td>130/80'</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age Gender  Height  Weight        B.P  Smoking  History\n",
       "0     Naveen   70      M     170      72   '120/80'        0        0\n",
       "1     Mahesh   90      M     155      60    '90/60'        1        1\n",
       "2       Raja   78      M     168      80    120/60'        1        0\n",
       "3      Leela   67      F     165      88    130/60'        0        1\n",
       "4   Shakeela   65      F     158      60   '105/80'        1        0\n",
       "5       Sita   45      F     148      65   '140/60'        0        0\n",
       "6      Singh   28      M     171      73   '150/90'        1        1\n",
       "7       Riya   12      F     150      45   '135/90'        0        1\n",
       "8        Dev   56      M     160      78  '120/105'        1        0\n",
       "9       Sach   20      M     158      85    130/80'        1        0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"C:\\\\Users\\\\HP\\\\Downloads\\\\People.csv\")\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "797b2f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "dataset = pd.read_csv(\"C:\\\\Users\\\\HP\\\\Downloads\\\\People.csv\")\n",
    "ids = dataset['Name']\n",
    "ages = dataset['Age']\n",
    "bins = [10,20,30,40,50,60,70,80,90,100]\n",
    "plt.hist(ages, bins=bins, edgecolor='black', color='#bec4ed')\n",
    "plt.title('Ages of Patients')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35458014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "slices = [60,40]\n",
    "labels = [\"Male\",\"Female\"]\n",
    "colors = [\"#74dff6\",\"#f7838d\"]\n",
    "plt.pie(slices, labels = labels, colors = colors, wedgeprops = {'edgecolor':'black'})\n",
    "plt.title(\"Gender Pie Chart\")\n",
    "plt.tight_layout()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40f1057f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No. of patients')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "dataset = pd.read_csv(\"C:\\\\Users\\\\HP\\\\Downloads\\\\People.csv\")\n",
    "ids = dataset['Name']\n",
    "h = dataset['Height']\n",
    "bins = [140,145,150,155,160,165,170]\n",
    "plt.hist(h, bins=bins, edgecolor='black', color='#7cc644')\n",
    "plt.title('Heights of Patients')\n",
    "plt.xlabel('Height Range (cm)')\n",
    "plt.ylabel('No. of patients')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d0f8b48e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'No. of patients')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "dataset = pd.read_csv(\"C:\\\\Users\\\\HP\\\\Downloads\\\\People.csv\")\n",
    "ids = dataset['Name']\n",
    "w = dataset['Weight']\n",
    "bins = [40,50,60,70,80,90,100]\n",
    "plt.hist(w, bins=bins, edgecolor='black', color='#ff944e')\n",
    "plt.title('Weights of Patients')\n",
    "plt.xlabel('Weight Range(kg)')\n",
    "plt.ylabel('No. of patients')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bbe48785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "slices = [50,60]\n",
    "labels = [\"Smokers\",\"Non-Smokers\"]\n",
    "colors = [\"#fa5a69\",\"#f1ac38\"]\n",
    "plt.pie(slices, labels = labels, colors = colors, wedgeprops = {'edgecolor':'black'})\n",
    "plt.title(\"Smoking Pie Chart\")\n",
    "plt.tight_layout()\n",
    "plt.show\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d2f08682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "slices = [70,30]\n",
    "labels = [\"Family History\",\"No Family History\"]\n",
    "colors = [\"#318f9a\",\"#fcc0c5\"]\n",
    "plt.pie(slices, labels = labels, colors = colors, wedgeprops = {'edgecolor':'black'})\n",
    "plt.title(\"Family History Pie Chart\")\n",
    "plt.tight_layout()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "440949e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}