--- a
+++ b/HIMA/Age-Heart Diseases.ipynb
@@ -0,0 +1,137 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "22938fc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The average age that has cardio  55.846575342465755\n",
+      "The median age that has cardio  54.95017268678232\n",
+      "The most frequent age that has cardio  59.97808219178082\n",
+      "The older age that has cardio  64.96712328767123\n",
+      "The smaller age that has cardio  39.10958904109589\n",
+      "The Standard Deviation of the ages that have cardio  6.345006976538519\n",
+      "The number of people that have cardio and are smokers  2929\n",
+      "The number of people that have cardio and are not smokers  32050\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\THE LAPTOP SHOP\\AppData\\Local\\Temp\\ipykernel_13480\\3562045099.py:7: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  hascardio['age'] = hascardio['age'] / 365\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "cardio_data = pd.read_csv('../cardio_train.csv', sep=';')\n",
+    "hascardio = cardio_data[cardio_data['cardio'] == 1]\n",
+    "hascardio['age'] = hascardio['age'] / 365\n",
+    "mean_age = hascardio['age'].mean()\n",
+    "mode_age = hascardio['age'].mode()[0]\n",
+    "median_age = hascardio['age'].median()\n",
+    "max_age = hascardio['age'].max()\n",
+    "min_age = hascardio['age'].min()\n",
+    "std_age = hascardio['age'].std()\n",
+    "cardiosmokers = hascardio[hascardio['smoke'] == 1]\n",
+    "cardio_not_smokers = hascardio[hascardio['smoke'] == 0]\n",
+    "\n",
+    "print('The average age that has cardio ', median_age)\n",
+    "print('The median age that has cardio ', mean_age)\n",
+    "print('The most frequent age that has cardio ', mode_age )\n",
+    "print('The older age that has cardio ', max_age )\n",
+    "print('The smaller age that has cardio ', min_age )\n",
+    "print('The Standard Deviation of the ages that have cardio ', std_age )\n",
+    "print('The number of people that have cardio and are smokers ',len(cardiosmokers['smoke']))\n",
+    "print('The number of people that have cardio and are not smokers ',len(cardio_not_smokers['smoke']))\n",
+    "#print(hascardio)\n",
+    "plt.ylabel('Ages')\n",
+    "plt.title(\"Has cardio age box plot\")\n",
+    "plt.boxplot(hascardio['age'])\n",
+    "plt.show()\n",
+    "\n",
+    "plt.hist(hascardio['age'], bins = int(180/25), density=False, alpha=0.5, color='r')\n",
+    "plt.title(\"Age - Cardio\")\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4861b374",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28b5d216",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
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+    "name": "ipython",
+    "version": 3
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+}