machine-learning/heart-disease/heart-disease.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "c5016953",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report, precision_score, recall_score, f1_score, RocCurveDisplay\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10f46839",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./data/heart-disease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3f1a9db9",
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   "outputs": [
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       "      <th></th>\n",
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
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       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
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       "300      1   2     3       0  \n",
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       "\n",
       "[303 rows x 14 columns]"
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   "source": [
    "df"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e882f331",
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   "outputs": [
    {
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       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
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   "source": [
    "pd.crosstab(df.target, df.sex)"
   ]
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.target, df.sex).plot(kind='bar',\n",
    "                                    figsize=(10,6),\n",
    "                                    color=['salmon', 'lightblue'])\n",
    "plt.title('Heart Disease Frequency for Sex')\n",
    "plt.xlabel('0= No Disease, 1 = Disease')\n",
    "plt.ylabel('Ammount')\n",
    "plt.legend(['Female','Male'])\n",
    "plt.xticks(rotation=0)\n",
    "                                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75e5ce26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162    11\n",
       "160     9\n",
       "163     9\n",
       "152     8\n",
       "173     8\n",
       "       ..\n",
       "202     1\n",
       "184     1\n",
       "121     1\n",
       "192     1\n",
       "90      1\n",
       "Name: thalach, Length: 91, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['thalach'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3c23ebfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            c='salmon')\n",
    "plt.scatter(df.age[df.target==0],\n",
    "            df.thalach[df.target==0],\n",
    "            c='lightblue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "121d3760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "41c418b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAFvCAYAAACIOIXnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVFklEQVR4nO3df7DddZ3f8dcbEowKqEBCo5c1cc0AogXCxbJFGafUkVIMDmOc0NltXNDYQVfXqm2wfziMY8uMdqfbsayTWXSZFnERGWG0snWirmOZQS9LRiExRRThxggX1l+UBkj49I8cMYtBkns+N+fcy+Pxz7nne873+33/cQae+X6/53uqtRYAAIZ32KgHAABYKIQVAEAnwgoAoBNhBQDQibACAOhk0agHSJLjjjuurVixYtRjAAA8q9tvv/2h1trS/b02FmG1YsWKTE1NjXoMAIBnVVU/fqbXnAoEAOhEWAEAdCKsAAA6GYtrrACA55Ynnngi09PT2bVr16hHeUZLlizJxMREFi9efMDrCCsA4JCbnp7OUUcdlRUrVqSqRj3Ob2mt5eGHH8709HRWrlx5wOs5FQgAHHK7du3KscceO5ZRlSRVlWOPPfagj6gJKwBgJMY1qn5tNvMJKwCAToQVADA2fv7zn+eqq66a8/188YtfzNatW7tvV1gBAGPjYMOqtZYnn3zyoPcjrACABW/jxo255557ctppp+X9739/zj333KxevTqvec1rctNNNyVJ7r333px88sm57LLLsnr16tx///356Ec/mpNOOilvfOMbc/HFF+cTn/hEkuSee+7JeeedlzPOOCOvf/3r8/3vfz+33nprbr755nzoQx/Kaaedlnvuuafb/G63AACMjSuvvDJ33nlntmzZkt27d+fRRx/N0UcfnYceeihnnXVW1qxZkyTZvn17PvOZz+Sqq67K1NRUvvCFL+SOO+7I7t27s3r16pxxxhlJkg0bNuRTn/pUVq1aldtuuy2XXXZZvva1r2XNmjW54IIL8ta3vrXr/MIKABhLrbV8+MMfzje/+c0cdthh2bFjRx544IEkyctf/vKcddZZSZJvfetbufDCC/P85z8/SfLmN785SfLII4/k1ltvzdq1a5/a5mOPPTanMwsrAGAsXXvttZmZmcntt9+exYsXZ8WKFU/dV+qFL3zhU+9rre13/SeffDIvfvGLs2XLlkMxbhJh9VueuOIDox7hKYs/8p9HPQIAHFJHHXVUfvWrXyVJfvGLX2TZsmVZvHhxvv71r+fHP/7xftd53etel3e96125/PLLs3v37nz5y1/OO9/5zhx99NFZuXJlPv/5z2ft2rVpreW73/1uTj311H+wn55cvA4AjI1jjz02Z599dl796ldny5YtmZqayuTkZK699tqcdNJJ+13nzDPPzJo1a3LqqafmoosuyuTkZF70ohcl2XvU6+qrr86pp56aU0455akL4NetW5ePf/zjOf3007tevF7PdPjsUJqcnGxTU1OjHiOJI1YAcChs27YtJ598crftPfLIIznyyCPz6KOP5pxzzsmmTZuyevXqobe7vzmr6vbW2uT+3u9UIAAw723YsCFbt27Nrl27sn79+i5RNRvCCgCY9z772c+OeoQkrrECAOhGWAEAdCKsAAA6EVYAAJ0868XrVfXpJBckebC19urBsmOS/HWSFUnuTfK21trPBq9dnuTSJHuSvLe19jdzMjkAsGD0vt3Rgd6y6JZbbsn73ve+7NmzJ+94xzuycePGofZ7IEes/irJeU9btjHJ5tbaqiSbB89TVa9Ksi7JKYN1rqqqw4eaEABgDuzZsyfvfve785WvfCVbt27Nddddl61btw61zWcNq9baN5P8/dMWX5jkmsHf1yR5yz7LP9dae6y19qMkP0jy2qEmBACYA9/+9rfzyle+Mq94xStyxBFHZN26dU/dmX22ZnuN1fGttZ1JMnhcNlj+siT37/O+6cGy31JVG6pqqqqmZmZmZjkGAMDs7NixIyeccMJTzycmJrJjx46httn74vXaz7L9/mZOa21Ta22ytTa5dOnSzmMAAPxu+/tZv6r9pcyBm21YPVBVywcDLE/y4GD5dJIT9nnfRJKfzH48AIC5MTExkfvv/82Jtunp6bz0pS8dapuzDaubk6wf/L0+yU37LF9XVc+rqpVJViX59lATAgDMgTPPPDN33313fvSjH+Xxxx/P5z73uaxZs2aobR7I7RauS/KGJMdV1XSSjyS5Msn1VXVpkvuSrE2S1tpdVXV9kq1Jdid5d2ttz1ATAgAL3oHeHqGnRYsW5ZOf/GTe9KY3Zc+ePbnkkktyyimnDLfNZ3tDa+3iZ3jp3Gd4/8eSfGyYoQAADoXzzz8/559/frftufM6AEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6edbbLQAAzLUbt+/sur2LTlz+rO+55JJL8qUvfSnLli3LnXfe2WW/jlgBAM9Jb3/723PLLbd03aawAgCek84555wcc8wxXbcprAAAOhFWAACdCCsAgE6EFQBAJ263AACM3IHcHqG3iy++ON/4xjfy0EMPZWJiIldccUUuvfTSobYprACA56Trrruu+zadCgQA6ERYAQB0IqwAgJForY16hN9pNvMJKwDgkFuyZEkefvjhsY2r1loefvjhLFmy5KDWc/E6AHDITUxMZHp6OjMzM6Me5RktWbIkExMTB7WOsAIADrnFixdn5cqVox6jO6cCAQA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoJOhwqqq3l9Vd1XVnVV1XVUtqapjquqrVXX34PElvYYFABhnsw6rqnpZkvcmmWytvTrJ4UnWJdmYZHNrbVWSzYPnAAAL3rCnAhcleX5VLUrygiQ/SXJhkmsGr1+T5C1D7gMAYF6YdVi11nYk+USS+5LsTPKL1tr/SnJ8a23n4D07kyzb3/pVtaGqpqpqamZmZrZjAACMjWFOBb4ke49OrUzy0iQvrKo/PND1W2ubWmuTrbXJpUuXznYMAICxMcypwH+e5EettZnW2hNJbkzyT5M8UFXLk2Tw+ODwYwIAjL9hwuq+JGdV1QuqqpKcm2RbkpuTrB+8Z32Sm4YbEQBgflg02xVba7dV1Q1J/i7J7iR3JNmU5Mgk11fVpdkbX2t7DAoAMO5mHVZJ0lr7SJKPPG3xY9l79AoA4DnFndcBADoRVgAAnQgrAIBOhBUAQCfCCgCgE2EFANCJsAIA6ERYAQB0IqwAADoRVgAAnQgrAIBOhBUAQCfCCgCgE2EFANCJsAIA6ERYAQB0IqwAADoRVgAAnQgrAIBOhBUAQCfCCgCgE2EFANCJsAIA6ERYAQB0IqwAADoRVgAAnQgrAIBOFo16AAAOrRu37xz1CE+56MTlox4BunLECgCgE2EFANCJsAIA6ERYAQB0IqwAADoRVgAAnQgrAIBOhBUAQCfCCgCgE2EFANCJsAIA6ERYAQB0IqwAADoRVgAAnQgrAIBOhBUAQCfCCgCgE2EFANCJsAIA6ERYAQB0IqwAADoRVgAAnQgrAIBOhgqrqnpxVd1QVd+vqm1V9QdVdUxVfbWq7h48vqTXsAAA42zYI1Z/nuSW1tpJSU5Nsi3JxiSbW2urkmwePAcAWPBmHVZVdXSSc5JcnSSttcdbaz9PcmGSawZvuybJW4YbEQBgfhjmiNUrkswk+UxV3VFVf1lVL0xyfGttZ5IMHpftb+Wq2lBVU1U1NTMzM8QYAADjYZiwWpRkdZK/aK2dnuT/5iBO+7XWNrXWJltrk0uXLh1iDACA8TBMWE0nmW6t3TZ4fkP2htYDVbU8SQaPDw43IgDA/DDrsGqt/TTJ/VV14mDRuUm2Jrk5yfrBsvVJbhpqQgCAeWLRkOv/SZJrq+qIJD9M8sfZG2vXV9WlSe5LsnbIfQAAzAtDhVVrbUuSyf28dO4w2wUAmI/ceR0AoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhk0bAbqKrDk0wl2dFau6Cqjkny10lWJLk3ydtaaz8bdj/A73bj9p2jHiFJctGJy0c9AsDI9Dhi9b4k2/Z5vjHJ5tbaqiSbB88BABa8ocKqqiaS/Mskf7nP4guTXDP4+5okbxlmHwAA88WwR6z+S5J/l+TJfZYd31rbmSSDx2X7W7GqNlTVVFVNzczMDDkGAMDozTqsquqCJA+21m6fzfqttU2ttcnW2uTSpUtnOwYAwNgY5uL1s5OsqarzkyxJcnRV/Y8kD1TV8tbazqpanuTBHoMCAIy7WYdVa+3yJJcnSVW9IckHW2t/WFUfT7I+yZWDx5uGH/O5aVy+5ZX4phcAHIi5uI/VlUneWFV3J3nj4DkAwII39H2skqS19o0k3xj8/XCSc3tsFwBgPnHndQCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoJNZh1VVnVBVX6+qbVV1V1W9b7D8mKr6alXdPXh8Sb9xAQDG1zBHrHYn+UBr7eQkZyV5d1W9KsnGJJtba6uSbB48BwBY8GYdVq21na21vxv8/ask25K8LMmFSa4ZvO2aJG8ZckYAgHmhyzVWVbUiyelJbktyfGttZ7I3vpIse4Z1NlTVVFVNzczM9BgDAGCkhg6rqjoyyReS/Glr7ZcHul5rbVNrbbK1Nrl06dJhxwAAGLmhwqqqFmdvVF3bWrtxsPiBqlo+eH15kgeHGxEAYH4Y5luBleTqJNtaa3+2z0s3J1k/+Ht9kptmPx4AwPyxaIh1z07yR0m+V1VbBss+nOTKJNdX1aVJ7kuydqgJAQDmiVmHVWvtW0nqGV4+d7bbBQCYr4Y5YgXAAXriig+MeoTfWPfBUU8AC5aftAEA6ERYAQB0IqwAADoRVgAAnQgrAIBOfCsQAHhGN27fOeoRkiQXnbh81CMcEEesAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAnwgoAoBNhBQDQibACAOhEWAEAdCKsAAA6EVYAAJ0IKwCAToQVAEAni0Y9AMxnT1zxgVGP8BvrPjjqCQCe8xyxAgDoRFgBAHQirAAAOhFWAACdCCsAgE58KxAAxoxvHM9fjlgBAHQirAAAOhFWAACdCCsAgE6EFQBAJ8IKAKATYQUA0ImwAgDoRFgBAHQirAAAOhFWAACdCCsAgE6EFQBAJ8IKAKATYQUA0ImwAgDoZM7CqqrOq6rtVfWDqto4V/sBABgXcxJWVXV4kv+W5F8keVWSi6vqVXOxLwCAcTFXR6xem+QHrbUfttYeT/K5JBfO0b4AAMZCtdb6b7TqrUnOa629Y/D8j5L8k9bae/Z5z4YkGwZPT0yyvfsg899xSR4a9RDMGz4vHCifFQ6Gz8tve3lrben+Xlg0Rzus/Sz7BwXXWtuUZNMc7X9BqKqp1trkqOdgfvB54UD5rHAwfF4OzlydCpxOcsI+zyeS/GSO9gUAMBbmKqy+k2RVVa2sqiOSrEty8xztCwBgLMzJqcDW2u6qek+Sv0lyeJJPt9bumot9LXBOlXIwfF44UD4rHAyfl4MwJxevAwA8F7nzOgBAJ8IKAKATYQUA0ImwAgDoZK5uEMosVNVJ2fvTPy/L3huq/iTJza21bSMdDJjXBv9teVmS21prj+yz/LzW2i2jm4xxU1WvTdJaa98Z/MbveUm+31r7nyMebd5wxGpMVNW/z97fVKwk387ee4FVkuuqauMoZ2N+qao/HvUMjI+qem+Sm5L8SZI7q2rf3239j6OZinFUVR9J8l+T/EVV/ackn0xyZJKNVfUfRjrcPOJ2C2Oiqv5PklNaa088bfkRSe5qra0azWTMN1V1X2vt90Y9B+Ohqr6X5A9aa49U1YokNyT57621P6+qO1prp492QsbF4LNyWpLnJflpkonW2i+r6vnZe7TzH49yvvnCqcDx8WSSlyb58dOWLx+8Bk+pqu8+00tJjj+UszD2Dv/16b/W2r1V9YYkN1TVy7P/33XluWt3a21Pkker6p7W2i+TpLX2/6rK/4cOkLAaH3+aZHNV3Z3k/sGy30vyyiTvGdVQjK3jk7wpyc+etryS3Hrox2GM/bSqTmutbUmSwZGrC5J8OslrRjoZ4+bxqnpBa+3RJGf8emFVvSj+gX/AnAocI1V1WJLXZu9FppW9P2b9ncG/IOApVXV1ks+01r61n9c+21r7VyMYizFUVRPZeyTip/t57ezW2v8ewViMoap6Xmvtsf0sPy7J8tba90Yw1rwjrAAAOvGtQACAToQVAEAnwgoAoBNhBQDQidstAAtGVf3rJB/M3p+E+m6SPUl2JTkle29R8W9ba18a3YTAQudbgcCCUFWnJLkxydmttYeq6pgkf5bkHyU5P8nvJ/l6kle21naNblJgIXMqEFgo/lmSG1prDyVJa+3vB8uvb6092Vq7O8kPk5w0qgGBhU9YAQtFZe8pwKd7+jKH6YE5I6yAhWJzkrdV1bFJMjgVmCRrq+qwqvr9JK9Isn1UAwILn4vXgQWhtXZXVX0syd9W1Z4kdwxe2p7kb7P34vV/4/oqYC65eB1YsKrqr5J8qbV2w6hnAZ4bnAoEAOjEESsAgE4csQIA6ERYAQB0IqwAADoRVgAAnQgrAIBO/j8THp2L7BDtXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target).plot(kind='bar',\n",
    "                                  figsize=(10,6),\n",
    "                                  color=['salmon','lightblue']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "822e9c85",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "56ec6c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                 annot=True,\n",
    "                 linewidths=0.5,\n",
    "                 fmt='.2f',\n",
    "                 cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3e5cc94d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('target',axis=1)\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ecf72189",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3d34cf32",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {'Logistic Regression' : LogisticRegression(),\n",
    "         'KNN': KNeighborsClassifier(),\n",
    "         'Random Forest': RandomForestClassifier()}\n",
    "\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    model_scores = {}\n",
    "    for name, model in models.items():\n",
    "        model.fit(X_train, y_train)\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dae0a798",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AGrudev\\Desktop\\ml\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8360655737704918,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.7868852459016393}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models=models,\n",
    "                             X_train=X_train,\n",
    "                             X_test=X_test,\n",
    "                             y_train=y_train,\n",
    "                             y_test=y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "71ce3b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=['accuracy'])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "57c9992c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scores = []\n",
    "test_scores  = []\n",
    "neighbors = range(1, 150)\n",
    "knn = KNeighborsClassifier()\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "4b83ca3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label='Train score')\n",
    "plt.plot(neighbors, test_scores, label='Test score')\n",
    "plt.xticks(np.arange(0, 150, 50))\n",
    "plt.xlabel = ('Number of neighbors')\n",
    "plt.ylabel = ('Model score')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "98065117",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_grid = {'C': np.logspace(-4, 4, 20),\n",
    "                'solver': ['liblinear']}\n",
    "rf_grid = {'n_estimators': np.arange(10, 1011, 50),\n",
    "            'max_depth': [None, 3, 5, 10],\n",
    "            'min_samples_split': np.arange(2, 20, 2),\n",
    "            'min_samples_leaf': np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3639d093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  10,   60,  110,  160,  210,  260,  310,  360,  410,  460,  510,\n",
       "        560,  610,  660,  710,  760,  810,  860,  910,  960, 1010])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10, 1011, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d9ede107",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_reg_grid,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                verbose=True)\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7f40c908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.819672131147541"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "231c827e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([  10,   60,  110,  160,  210,  260,  310,  360,  410,  460,  510,\n",
       "        560,  610,  660,  710,  760,  810,  860,  910,  960, 1010])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                                param_distributions=rf_grid,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                verbose=True)\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "3bd0a8cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 5,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e1d43f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7704918032786885"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "c20af730",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg_grid = {'C': np.logspace(-4, 4, 30),\n",
    "                'solver': ['liblinear']}\n",
    "\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "gs_log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "ca32c3b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.38566204211634725, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "40a270a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.819672131147541"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c3cc62e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "559be17d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_estimator(gs_log_reg, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "bc2f502d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(font_scale=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "8da8be4e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O1GHixIkkJSXRrFkz+vTpw+bNm0lPT6/2vo78OimcOsTFxVFaWsqKFStITU0lLCyMefPmcdttt1k9TSymTw6IGNA5jogBhSNiQOGIGFA4IgYUjogBhSNiQOGIGFA4Igb+H0B3RDC2m1QuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_conf_mat(y_test, y_preds):\n",
    "    fig, ax = plt.subplots(figsize=(3,3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True,\n",
    "                     cbar=False)\n",
    "    plt.xlabel = ('Predicted label')\n",
    "    plt.ylabel = ('True labe')\n",
    "\n",
    "plot_conf_mat(y_test,y_preds)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "fb94424a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.69      0.77        26\n",
      "           1       0.80      0.91      0.85        35\n",
      "\n",
      "    accuracy                           0.82        61\n",
      "   macro avg       0.83      0.80      0.81        61\n",
      "weighted avg       0.82      0.82      0.82        61\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'C': 0.38566204211634725, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "7f18e3f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(C=38566204211634725,\n",
    "                         solver='liblinear')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "453bb684",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_acc = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "17f23f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_acc = np.mean(cv_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "f937cbde",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_precision = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring='precision')\n",
    "cv_precision = np.mean(cv_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3ccf70dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_recall = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring='recall')\n",
    "cv_recall = np.mean(cv_recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "9700e80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_f1 = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring='f1')\n",
    "cv_f1 = np.mean(cv_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1785091d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8283060109289618,\n",
       " 0.8230422730422731,\n",
       " 0.8787878787878787,\n",
       " 0.8485836385836386)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc,cv_precision,cv_recall,cv_f1"
   ]
  }
 ],
 "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": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}