iris_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b5d85c6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "188f71cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\Nischala\\Downloads\\Iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf103e1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
      "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
      "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
      "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
      "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
      "4   5            5.0           3.6            1.4           0.2  Iris-setosa\n",
      "5   6            5.4           3.9            1.7           0.4  Iris-setosa\n",
      "6   7            4.6           3.4            1.4           0.3  Iris-setosa\n",
      "7   8            5.0           3.4            1.5           0.2  Iris-setosa\n",
      "8   9            4.4           2.9            1.4           0.2  Iris-setosa\n",
      "9  10            4.9           3.1            1.5           0.1  Iris-setosa\n"
     ]
    }
   ],
   "source": [
    "print(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "52908443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id               0\n",
      "SepalLengthCm    0\n",
      "SepalWidthCm     0\n",
      "PetalLengthCm    0\n",
      "PetalWidthCm     0\n",
      "Species          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())  # Count of missing values per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a9007021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NmxKfKC/+9Pr777/v0l78dIDSPoF+ttWrV5sbb7zR1KxZ04SEhJhrrrnGfPzxx6WeryJPG1i/fr0ZMWKEadOmjalfv77x9/c3l1xyifnTn/5kPvvssxL9t2zZYvr3728iIiJMjRo1TKNGjcyNN95oZs6c6exTvIa++OILM2jQIFO3bl0TEhJikpOTTVZWlsv5fvrpJ9O9e3cTFhZm6tWrZ+68805jt9tLzHFlnzZQ1qv4+OzsbDN06FDTuHFjU6NGDXPJJZeYTp06uTwJoTJfL4fDYSZNmmSaNGliAgMDzRVXXGE++eQT06ZNmxJPXZg/f75JSEgwNWrUcHmfKSkppmbNmiXeU2lrHsC52Yw5x8eLAQDQ6T9SMGTIEG3YsKHMq9UXg+zsbCUkJGj8+PF6+umnvV0OcNHhtgEAAMqwZcsWzZ8/X506dVLt2rWVmZmpl19+WbVr19b999/v7fKAixLhFQCAMtSsWVPp6el666239Pvvv6tOnTpKSkrSCy+8UOnHZQGoGtw2AAAAAMvgUVkAAACwDMIrAAAALIPwCgAAAMu44D+w5XA4tG/fPoWFhbn1oG8AAABUL2OM8vPzFRUV5fIHQEpzwYfXffv2KTo62ttlAAAA4Bz27NmjJk2alNvngg+vxX+Te8+ePapdu7aXqwEAAMDZ8vLyFB0d7cxt5bngw2vxrQK1a9cmvAIAAPiwitziyQe2AAAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACW4dXw+vXXX6tPnz6KioqSzWbT4sWLS/TJyMjQLbfcojp16igsLEzXXHON7Ha754sFAACA13k1vB47dkxt2rTRtGnTSt2/Y8cOde7cWQkJCVq1apW2bNmi5557TsHBwR6uFAAAAL7AZowx3i5Ckmw2mxYtWqTbbrvN2TZgwADVqFFD//73v90+b15enurUqaMjR46odu3aVVApAAAAqlJl8lqAh2qqNIfDoU8//VRjx45Vz5499f333ysuLk7jxo1zCbhnKywsVGFhoXM7Ly/PA9WiutntduXm5npsvPDwcMXExHhsPMDX8W8QgK/w2fCak5Ojo0eP6qWXXtKkSZM0efJkLV26VP369dPKlSvVpUuXUo9LTU3VxIkTPVwtqpPdbldCYqKOFxR4bMyQ0FD9nJHBD09A/BsE4Ft8Nrw6HA5J0q233qpHH31UktS2bVt98803mjlzZpnhddy4cRozZoxzOy8vT9HR0dVfMKpNbm6ujhcUqP+kGYqIi6/28XKys7Tg2YeUm5vLD05A/BsE4Ft8NryGh4crICBALVu2dGlPTEzUmjVryjwuKChIQUFB1V0evCAiLl6NE9t4uwzgosW/QQC+wGef8xoYGKiOHTsqMzPTpX3btm2KjY31UlUAAADwJq9eeT169Ki2b9/u3M7OztbmzZtVv359xcTE6IknntBdd92lG264QV27dtXSpUv18ccfa9WqVd4rGgAAAF7j1fCanp6url27OreL71VNSUnRnDlz1LdvX82cOVOpqakaOXKkWrRooQ8//FCdO3f2VskAAADwIq+G16SkJJ3rMbNDhw7V0KFDPVQRAAAAfJnP3vMKAAAAnI3wCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDK+G16+//lp9+vRRVFSUbDabFi9eXGbf4cOHy2azaerUqR6rDwAAAL7Fq+H12LFjatOmjaZNm1Zuv8WLF+vbb79VVFSUhyoDAACALwrw5uC9evVSr169yu2zd+9e/eUvf9GyZcvUu3dvD1UGAAAAX+TV8HouDodDgwYN0hNPPKFWrVpV6JjCwkIVFhY6t/Py8qqrPAAVZLfblZub69Exw8PDFRMT49ExAQDVz6fD6+TJkxUQEKCRI0dW+JjU1FRNnDixGqsCUBl2u10JiYk6XlDg0XFDQkP1c0YGARYALjA+G143btyo119/XZs2bZLNZqvwcePGjdOYMWOc23l5eYqOjq6OEgFUQG5uro4XFKj/pBmKiIv3yJg52Vla8OxDys3NJbwCwAXGZ8Pr6tWrlZOT4/KDp6ioSI899pimTp2qXbt2lXpcUFCQgoKCPFQlgIqKiItX48Q23i4DAGBxPhteBw0apJtuusmlrWfPnho0aJCGDBnipaoAAADgTV4Nr0ePHtX27dud29nZ2dq8ebPq16+vmJgYNWjQwKV/jRo11KhRI7Vo0cLTpQIAAMAHeDW8pqenq2vXrs7t4ntVU1JSNGfOHC9VBQAAAF/l1fCalJQkY0yF+5d1nysAAAAuDl79C1sAAABAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFhGgLcLAOB5drtdubm5HhkrIyPDI+MAAC4OhFfgImO325WQmKjjBQXeLgUAgEojvAIXmdzcXB0vKFD/STMUERdf7eNlrl2utOmp1T4OAODiQHgFLlIRcfFqnNim2sfJyc6q9jEAABcPPrAFAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAy/BqeP3666/Vp08fRUVFyWazafHixc59J0+e1JNPPqnLL79cNWvWVFRUlO677z7t27fPewUDAADAq7waXo8dO6Y2bdpo2rRpJfYVFBRo06ZNeu6557Rp0yYtXLhQ27Zt0y233OKFSgEAAOALArw5eK9evdSrV69S99WpU0dpaWkubf/4xz901VVXyW63KyYmxhMlAgAAwId4NbxW1pEjR2Sz2VS3bt0y+xQWFqqwsNC5nZeX54HKAAAA4AmW+cDWiRMn9NRTT+mee+5R7dq1y+yXmpqqOnXqOF/R0dEerBIAAADVyRLh9eTJkxowYIAcDoemT59ebt9x48bpyJEjzteePXs8VCUAAACqm8/fNnDy5En1799f2dnZWrFiRblXXSUpKChIQUFBHqoOAAAAnuTT4bU4uGZlZWnlypVq0KCBt0sCAACAF3k1vB49elTbt293bmdnZ2vz5s2qX7++oqKidMcdd2jTpk365JNPVFRUpAMHDkiS6tevr8DAQG+VDQAAAC/xanhNT09X165dndtjxoyRJKWkpGjChAlasmSJJKlt27Yux61cuVJJSUmeKhMAAAA+wqvhNSkpScaYMveXtw8AAAAXH0s8bQAAAACQCK8AAACwEMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALCPA2wUAAOAL7Ha7cnNzPTZeeHi4YmJiPDYecKEgvAIALnp2u10JiYk6XlDgsTFDQkP1c0YGARaoJMIrAOCil5ubq+MFBeo/aYYi4uKrfbyc7CwtePYh5ebmEl6BSiK8AgDwvyLi4tU4sY23ywBQDj6wBQAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMvwanj9+uuv1adPH0VFRclms2nx4sUu+40xmjBhgqKiohQSEqKkpCT9+OOP3ikWAAAAXufV8Hrs2DG1adNG06ZNK3X/yy+/rClTpmjatGnasGGDGjVqpO7duys/P9/DlQIAAMAXBHhz8F69eqlXr16l7jPGaOrUqXrmmWfUr18/SdLcuXPVsGFDvfvuuxo+fLgnSwUAAIAP8Gp4LU92drYOHDigHj16ONuCgoLUpUsXffPNN2WG18LCQhUWFjq38/Lyqr1WAL4pIyPDY2OFh4crJibGY+PZ7Xbl5uZ6ZCxPziMAnIvPhtcDBw5Ikho2bOjS3rBhQ+3evbvM41JTUzVx4sRqrQ2Ab8vP/VU2Pz8NHDjQY2OGhIbq54wMjwRYu92uhMREHS8oqPaxAMDX+Gx4LWaz2Vy2jTEl2s40btw4jRkzxrmdl5en6OjoaqsPgO85np8n43Co/6QZioiLr/bxcrKztODZh5Sbm+uR8Jqbm6vjBQUee3+Za5crbXpqtY8DABXhs+G1UaNGkk5fgY2MjHS25+TklLgae6agoCAFBQVVe30AfF9EXLwaJ7bxdhnVxlPvLyc7q9rHAICK8tnnvMbFxalRo0ZKS0tztv3xxx/66quv1KlTJy9WBgAAAG/x6pXXo0ePavv27c7t7Oxsbd68WfXr11dMTIxGjx6tF198UfHx8YqPj9eLL76o0NBQ3XPPPV6sGgAAAN7i1fCanp6url27OreL71VNSUnRnDlzNHbsWB0/flwPP/ywDh8+rKuvvlpffPGFwsLCvFUyAAAAvMir4TUpKUnGmDL322w2TZgwQRMmTPBcUQAAAPBZPnvPKwAAAHA2wisAAAAsg/AKAAAAyyC8AgAAwDLcCq/Z2dlVXQcAAABwTm6F18suu0xdu3bVvHnzdOLEiaquCQAAACiVW+F1y5YtateunR577DE1atRIw4cP13fffVfVtQEAAAAu3AqvrVu31pQpU7R3717Nnj1bBw4cUOfOndWqVStNmTJFv/32W1XXCQAAAJzfB7YCAgLUt29fLViwQJMnT9aOHTv0+OOPq0mTJrrvvvu0f//+qqoTAAAAOL/wmp6erocffliRkZGaMmWKHn/8ce3YsUMrVqzQ3r17deutt1ZVnQAAAIB7fx52ypQpmj17tjIzM5WcnKy3335bycnJ8vM7nYXj4uL05ptvKiEhoUqLBQAAwMXNrfA6Y8YMDR06VEOGDFGjRo1K7RMTE6O33nrrvIoDAAAAzuRWeM3Kyjpnn8DAQKWkpLhzegAAAKBUbt3zOnv2bL3//vsl2t9//33NnTv3vIsCAAAASuNWeH3ppZcUHh5eoj0iIkIvvvjieRcFAAAAlMat8Lp7927FxcWVaI+NjZXdbj/vogAAAIDSuBVeIyIi9MMPP5Ro37Jlixo0aHDeRQEAAAClcSu8DhgwQCNHjtTKlStVVFSkoqIirVixQqNGjdKAAQOqukYAAABAkptPG5g0aZJ2796tbt26KSDg9CkcDofuu+8+7nkFAABAtXErvAYGBuq9997T3/72N23ZskUhISG6/PLLFRsbW9X1AV6TkZHhsbHCw8MVExPjsfEAALAqt8JrsebNm6t58+ZVVQvgE/Jzf5XNz08DBw702JghoaH6OSODAAsAwDm4FV6Lioo0Z84cLV++XDk5OXI4HC77V6xYUSXFAd5wPD9PxuFQ/0kzFBEXX+3j5WRnacGzDyk3N5fwCgDAObgVXkeNGqU5c+aod+/eat26tWw2W1XXBXhdRFy8Gie28XYZAADgDG6F1//85z9asGCBkpOTq7oeAAAAoExuPSorMDBQl112WVXXAgAAAJTLrfD62GOP6fXXX5cxpqrrAQAAAMrk1m0Da9as0cqVK/X555+rVatWqlGjhsv+hQsXVklxAAAAwJncCq9169ZV3759q7oWAAAAoFxuhdfZs2dXdR0AAADAObl1z6sknTp1Sl9++aXefPNN5efnS5L27duno0ePVllxAAAAwJncuvK6e/du/elPf5LdbldhYaG6d++usLAwvfzyyzpx4oRmzpxZ1XUCAAAA7l15HTVqlDp06KDDhw8rJCTE2d63b18tX768yooDAAAAzuT20wbWrl2rwMBAl/bY2Fjt3bu3SgoDAAAAzubWlVeHw6GioqIS7b/88ovCwsLOuygAAACgNG6F1+7du2vq1KnObZvNpqNHj2r8+PH8yVgAAABUG7duG3jttdfUtWtXtWzZUidOnNA999yjrKwshYeHa/78+VVdIwAAACDJzfAaFRWlzZs3a/78+dq0aZMcDofuv/9+3XvvvS4f4AIAAACqklvhVZJCQkI0dOhQDR06tCrrAQAAAMrkVnh9++23y91/3333uVUMAAAAUB63wuuoUaNctk+ePKmCggIFBgYqNDSU8AoAAIBq4dbTBg4fPuzyOnr0qDIzM9W5c2c+sAUAAIBq41Z4LU18fLxeeumlEldlAQAAgKpSZeFVkvz9/bVv376qPCUAAADg5NY9r0uWLHHZNsZo//79mjZtmq677roqKQwAAAA4m1vh9bbbbnPZttlsuuSSS3TjjTfq1VdfrYq6JEmnTp3ShAkT9M477+jAgQOKjIzU4MGD9eyzz8rPr0ovGgMAAMAC3AqvDoejquso1eTJkzVz5kzNnTtXrVq1Unp6uoYMGaI6depwby0AAMBFyO0/UuAJ69at06233qrevXtLkpo2bar58+crPT3dy5UBAADAG9wKr2PGjKlw3ylTprgzhCSpc+fOmjlzprZt26bmzZtry5YtWrNmjaZOnVrmMYWFhSosLHRu5+XluT0+AAAAfItb4fX777/Xpk2bdOrUKbVo0UKStG3bNvn7+6t9+/bOfjab7byKe/LJJ3XkyBElJCTI399fRUVFeuGFF3T33XeXeUxqaqomTpx4XuMCAADAN7kVXvv06aOwsDDNnTtX9erVk3T6DxcMGTJE119/vR577LEqKe69997TvHnz9O6776pVq1bavHmzRo8eraioKKWkpJR6zLhx41yuDOfl5Sk6OrpK6gEAAIB3uRVeX331VX3xxRfO4CpJ9erV06RJk9SjR48qC69PPPGEnnrqKQ0YMECSdPnll2v37t1KTU0tM7wGBQUpKCioSsYHAACAb3HreVN5eXn69ddfS7Tn5OQoPz//vIsqVlBQUOKRWP7+/h572gEAAAB8i1tXXvv27ashQ4bo1Vdf1TXXXCNJWr9+vZ544gn169evyorr06ePXnjhBcXExKhVq1b6/vvvNWXKFA0dOrTKxgAAAIB1uBVeZ86cqccff1wDBw7UyZMnT58oIED333+/XnnllSor7h//+Ieee+45Pfzww8rJyVFUVJSGDx+u559/vsrGAAAAgHW4FV5DQ0M1ffp0vfLKK9qxY4eMMbrssstUs2bNKi0uLCxMU6dOLffRWAAAALh4nNffWN2/f7/279+v5s2bq2bNmjLGVFVdAAAAQAluhdeDBw+qW7duat68uZKTk7V//35J0rBhw6rsSQMAAADA2dwKr48++qhq1Kghu92u0NBQZ/tdd92lpUuXVllxAAAAwJncuuf1iy++0LJly9SkSROX9vj4eO3evbtKCgMAAADO5taV12PHjrlccS2Wm5vLHwgAAABAtXErvN5www16++23nds2m00Oh0OvvPKKunbtWmXFAQAAAGdy67aBV155RUlJSUpPT9cff/yhsWPH6scff9ShQ4e0du3aqq4RAAAAkOTmldeWLVvqhx9+0FVXXaXu3bvr2LFj6tevn77//ns1a9asqmsEAAAAJLlx5fXkyZPq0aOH3nzzTU2cOLE6agIAAABKVekrrzVq1NDWrVtls9mqox4AAACgTG7dNnDffffprbfequpaAAAAgHK59YGtP/74Q//zP/+jtLQ0dejQQTVr1nTZP2XKlCopDgAAADhTpcLrzp071bRpU23dulXt27eXJG3bts2lD7cTAAAAoLpUKrzGx8dr//79WrlypaTTfw72jTfeUMOGDaulOAAAAOBMlbrn1Rjjsv3555/r2LFjVVoQAAAAUBa3PrBV7OwwCwAAAFSnSoVXm81W4p5W7nEFAACAp1TqnldjjAYPHqygoCBJ0okTJ/Tggw+WeNrAwoULq65CAAAA4H9VKrympKS4bA8cOLBKiwEAAADKU6nwOnv27OqqAxZjt9uVm5vrkbEyMjI8Mg4A3+LJf/t8nwGsw60/UoCLm91uV0Jioo4XFHi7FAAXoPzcX2Xz8+O3ewBKRXhFpeXm5up4QYH6T5qhiLj4ah8vc+1ypU1PrfZxAPiG4/l5Mg6Hx77HSHyfAayE8Aq3RcTFq3Fim2ofJyc7q9rHAOB7PPU9RuL7DGAl5/WcVwAAAMCTCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyfD687t27VwMHDlSDBg0UGhqqtm3bauPGjd4uCwAAAF4Q4O0CynP48GFdd9116tq1qz7//HNFRERox44dqlu3rrdLAwAAgBf4dHidPHmyoqOjNXv2bGdb06ZNvVcQAAAAvMqnw+uSJUvUs2dP3Xnnnfrqq6/UuHFjPfzww3rggQfKPKawsFCFhYXO7by8PE+UCgDKyMi4oMYBAF/k0+F1586dmjFjhsaMGaOnn35a3333nUaOHKmgoCDdd999pR6TmpqqiRMnerhSABez/NxfZfPz08CBA71dCgBc8Hw6vDocDnXo0EEvvviiJKldu3b68ccfNWPGjDLD67hx4zRmzBjndl5enqKjoz1SL4CL0/H8PBmHQ/0nzVBEXHy1j5e5drnSpqdW+zgA4It8OrxGRkaqZcuWLm2JiYn68MMPyzwmKChIQUFB1V0aAJQQERevxoltqn2cnOysah8DAHyVTz8q67rrrlNmZqZL27Zt2xQbG+uligAAAOBNPh1eH330Ua1fv14vvviitm/frnfffVezZs3SiBEjvF0aAAAAvMCnw2vHjh21aNEizZ8/X61bt9bf/vY3TZ06Vffee6+3SwMAAIAX+PQ9r5J088036+abb/Z2GQAAAPABPn3lFQAAADgT4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZQR4uwCcP7vdrtzcXI+Nl5GR4bGxLiaemle+fgBw/jz9s1eSwsPDFRMT49ExfRHh1eLsdrsSEhN1vKDA26XATfm5v8rm56eBAwd6uxQAQAV462dvSGiofs7IuOgDLOHV4nJzc3W8oED9J81QRFy8R8bMXLtcadNTPTLWxeB4fp6Mw+GxryFfPwA4P9742ZuTnaUFzz6k3Nxcwqu3C0DViIiLV+PENh4ZKyc7yyPjXGw89TXk6wcAVcOTP3vxf/jAFgAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACzDUuE1NTVVNptNo0eP9nYpAAAA8ALLhNcNGzZo1qxZuuKKK7xdCgAAALzEEuH16NGjuvfee/XPf/5T9erV83Y5AAAA8JIAbxdQESNGjFDv3r110003adKkSeX2LSwsVGFhoXM7Ly+vussrwW63Kzc31yNjZWRkeGQcAEDV8+T38PDwcMXExHhsPE/+LJQ8//7gPT4fXv/zn/9o06ZN2rBhQ4X6p6amauLEidVcVdnsdrsSEhN1vKDAazUAAHxbfu6vsvn5aeDAgR4bMyQ0VD9nZHgk4HnjZ6En3x+8y6fD6549ezRq1Ch98cUXCg4OrtAx48aN05gxY5zbeXl5io6Orq4SS8jNzdXxggL1nzRDEXHx1T5e5trlSpueWu3jAACqzvH8PBmHw2M/K3Kys7Tg2YeUm5vrkXDn6Z+Fnn5/8C6fDq8bN25UTk6OrrzySmdbUVGRvv76a02bNk2FhYXy9/d3OSYoKEhBQUGeLrWEiLh4NU5sU+3j5GRnVfsYAIDq4amfFd5yob8/eIdPh9du3brpv//9r0vbkCFDlJCQoCeffLJEcAUAAMCFzafDa1hYmFq3bu3SVrNmTTVo0KBEOwAAAC58lnhUFgAAACD5+JXX0qxatcrbJQAAAMBLuPIKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsw6fDa2pqqjp27KiwsDBFRETotttuU2ZmprfLAgAAgJf4dHj96quvNGLECK1fv15paWk6deqUevTooWPHjnm7NAAAAHhBgLcLKM/SpUtdtmfPnq2IiAht3LhRN9xwg5eqAgAAgLf4dHg925EjRyRJ9evXL7NPYWGhCgsLndt5eXnVXhcAAFaQkZFxQY2Di5NlwqsxRmPGjFHnzp3VunXrMvulpqZq4sSJHqwMAADflp/7q2x+fho4cKC3SwHOm2XC61/+8hf98MMPWrNmTbn9xo0bpzFjxji38/LyFB0dXd3lAQDgs47n58k4HOo/aYYi4uKrfbzMtcuVNj212sfBxckS4fWRRx7RkiVL9PXXX6tJkybl9g0KClJQUJCHKgMAwDoi4uLVOLFNtY+Tk51V7WPg4uXT4dUYo0ceeUSLFi3SqlWrFBcX5+2SAAAA4EU+HV5HjBihd999Vx999JHCwsJ04MABSVKdOnUUEhLi5eoAAADgaT79nNcZM2boyJEjSkpKUmRkpPP13nvvebs0AAAAeIFPX3k1xni7BAAAAPgQn77yCgAAAJyJ8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgK8XQAAAEBVyMjIuKDG8fbY4eHhiomJ8dh4FUV4BQAAlpaf+6tsfn4aOHCgt0upNt54jyGhofo5I8PnAizhFQAAWNrx/DwZh0P9J81QRFx8tY+XuXa50qanVvs4Z/L0e8zJztKCZx9Sbm4u4RUAAKA6RMTFq3Fim2ofJyc7q9rHKIun3qMv4wNbAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAxLhNfp06crLi5OwcHBuvLKK7V69WpvlwQAAAAv8Pnw+t5772n06NF65pln9P333+v6669Xr169ZLfbvV0aAAAAPMznw+uUKVN0//33a9iwYUpMTNTUqVMVHR2tGTNmeLs0AAAAeFiAtwsozx9//KGNGzfqqaeecmnv0aOHvvnmm1KPKSwsVGFhoXP7yJEjkqS8vLzqK/QMR48elSTtzfhBfxQcq/bxftuV5dHxvDEm4zGer4/JeNYezxtjMh7j+fqYv+3eIel0rvFEhioewxhz7s7Gh+3du9dIMmvXrnVpf+GFF0zz5s1LPWb8+PFGEi9evHjx4sWLFy+Lvfbs2XPOfOjTV16L2Ww2l21jTIm2YuPGjdOYMWOc2w6HQ4cOHVKDBg3KPOZCkpeXp+joaO3Zs0e1a9f2djmWwby5h3lzD/PmPubOPcybe5g391V27owxys/PV1RU1Dn7+nR4DQ8Pl7+/vw4cOODSnpOTo4YNG5Z6TFBQkIKCglza6tatW10l+qzatWvzD80NzJt7mDf3MG/uY+7cw7y5h3lzX2Xmrk6dOhXq59Mf2AoMDNSVV16ptLQ0l/a0tDR16tTJS1UBAADAW3z6yqskjRkzRoMGDVKHDh107bXXatasWbLb7XrwwQe9XRoAAAA8zOfD61133aWDBw/qr3/9q/bv36/WrVvrs88+U2xsrLdL80lBQUEaP358iVsnUD7mzT3Mm3uYN/cxd+5h3tzDvLmvOufOZkxFnkkAAAAAeJ9P3/MKAAAAnInwCgAAAMsgvAIAAMAyCK8AAACwDMKrRaWmpspms2n06NFl9lm1apVsNluJ188//+y5Qn3AhAkTSsxBo0aNyj3mq6++0pVXXqng4GBdeumlmjlzpoeq9R2VnTfW2//Zu3evBg4cqAYNGig0NFRt27bVxo0byz2GNXdaZeeOdSc1bdq01DkYMWJEmcew3io/b6y1006dOqVnn31WcXFxCgkJ0aWXXqq//vWvcjgc5R5XlWvO5x+VhZI2bNigWbNm6YorrqhQ/8zMTJe/bnHJJZdUV2k+q1WrVvryyy+d2/7+/mX2zc7OVnJysh544AHNmzdPa9eu1cMPP6xLLrlEt99+uyfK9RmVmbdiF/t6O3z4sK677jp17dpVn3/+uSIiIrRjx45y/9Ifa+40d+au2MW87jZs2KCioiLn9tatW9W9e3fdeeedpfZnvZ1W2XkrdjGvNUmaPHmyZs6cqblz56pVq1ZKT0/XkCFDVKdOHY0aNarUY6p8zRlYSn5+vomPjzdpaWmmS5cuZtSoUWX2XblypZFkDh8+7LH6fNH48eNNmzZtKtx/7NixJiEhwaVt+PDh5pprrqniynxbZeeN9Xbak08+aTp37lypY1hzp7kzd6y7kkaNGmWaNWtmHA5HqftZb6U717yx1k7r3bu3GTp0qEtbv379zMCBA8s8pqrXHLcNWMyIESPUu3dv3XTTTRU+pl27doqMjFS3bt20cuXKaqzOd2VlZSkqKkpxcXEaMGCAdu7cWWbfdevWqUePHi5tPXv2VHp6uk6ePFndpfqUysxbsYt9vS1ZskQdOnTQnXfeqYiICLVr107//Oc/yz2GNXeaO3NX7GJfd8X++OMPzZs3T0OHDpXNZiu1D+utpIrMW7GLfa117txZy5cv17Zt2yRJW7Zs0Zo1a5ScnFzmMVW95givFvKf//xHmzZtUmpqaoX6R0ZGatasWfrwww+1cOFCtWjRQt26ddPXX39dzZX6lquvvlpvv/22li1bpn/+8586cOCAOnXqpIMHD5ba/8CBA2rYsKFLW8OGDXXq1Cnl5uZ6omSfUNl5Y72dtnPnTs2YMUPx8fFatmyZHnzwQY0cOVJvv/12mcew5k5zZ+5Yd64WL16s33//XYMHDy6zD+utpIrMG2vttCeffFJ33323EhISVKNGDbVr106jR4/W3XffXeYxVb7m3LpeC4+z2+0mIiLCbN682dl2rtsGSnPzzTebPn36VHF11nL06FHTsGFD8+qrr5a6Pz4+3rz44osubWvWrDGSzP79+z1Rok8617yV5mJcbzVq1DDXXnutS9sjjzxS7q/HWHOnuTN3pbkY112xHj16mJtvvrncPqy3kioyb6W5GNfa/PnzTZMmTcz8+fPNDz/8YN5++21Tv359M2fOnDKPqeo1x5VXi9i4caNycnJ05ZVXKiAgQAEBAfrqq6/0xhtvKCAgwOWm8/Jcc801ysrKquZqfVvNmjV1+eWXlzkPjRo10oEDB1zacnJyFBAQoAYNGniiRJ90rnkrzcW43iIjI9WyZUuXtsTERNnt9jKPYc2d5s7cleZiXHeStHv3bn355ZcaNmxYuf1Yb64qOm+luRjX2hNPPKGnnnpKAwYM0OWXX65Bgwbp0UcfLfe3wlW95givFtGtWzf997//1ebNm52vDh066N5779XmzZsr9ClwSfr+++8VGRlZzdX6tsLCQmVkZJQ5D9dee63S0tJc2r744gt16NBBNWrU8ESJPulc81aai3G9XXfddcrMzHRp27Ztm2JjY8s8hjV3mjtzV5qLcd1J0uzZsxUREaHevXuX24/15qqi81aai3GtFRQUyM/PNT76+/uX+6isKl9zlb5WC59x9m0DTz31lBk0aJBz+7XXXjOLFi0y27ZtM1u3bjVPPfWUkWQ+/PBDL1TrPY899phZtWqV2blzp1m/fr25+eabTVhYmNm1a5cxpuS87dy504SGhppHH33U/PTTT+att94yNWrUMB988IG33oJXVHbeWG+nfffddyYgIMC88MILJisry7zzzjsmNDTUzJs3z9mHNVc6d+aOdXdaUVGRiYmJMU8++WSJfay3slVm3lhrp6WkpJjGjRubTz75xGRnZ5uFCxea8PBwM3bsWGef6l5zhFcLOzu8pqSkmC5duji3J0+ebJo1a2aCg4NNvXr1TOfOnc2nn37q+UK97K677jKRkZGmRo0aJioqyvTr18/8+OOPzv1nz5sxxqxatcq0a9fOBAYGmqZNm5oZM2Z4uGrvq+y8sd7+z8cff2xat25tgoKCTEJCgpk1a5bLftZc2So7d6y705YtW2YkmczMzBL7WG9lq8y8sdZOy8vLM6NGjTIxMTEmODjYXHrppeaZZ54xhYWFzj7VveZsxhhT+eu1AAAAgOdxzysAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAVAGbzabFixd7uwy3DB48WLfddptbx95www169913q7ags3Ts2FELFy6s1jEAWAfhFYBl5eTkaPjw4YqJiVFQUJAaNWqknj17at26dd4urYTzCYhVZdeuXbLZbNq8eXOVnO+TTz7RgQMHNGDAgCo5X1mee+45PfXUU3I4HNU6DgBrILwCsKzbb79dW7Zs0dy5c7Vt2zYtWbJESUlJOnTokLdLuyi88cYbGjJkiPz8qvdHSe/evXXkyBEtW7asWscBYA2EVwCW9Pvvv2vNmjWaPHmyunbtqtjYWF111VUaN26cevfu7ex35MgR/fnPf1ZERIRq166tG2+8UVu2bHHunzBhgtq2bas333xT0dHRCg0N1Z133qnff//d2WfDhg3q3r27wsPDVadOHXXp0kWbNm2q0vfz008/KTk5WbVq1VLDhg01aNAg5ebmOvcnJSVp5MiRGjt2rOrXr69GjRppwoQJLuf4+eef1blzZwUHB6tly5b68ssvXW5niIuLkyS1a9dONptNSUlJLsf//e9/V2RkpBo0aKARI0bo5MmTZdabm5urL7/8UrfccotL+++//64///nPatiwoYKDg9W6dWt98sknkqQ5c+aobt26+uSTT9SiRQuFhobqjjvu0LFjxzR37lw1bdpU9erV0yOPPKKioiLnOf39/ZWcnKz58+dXdloBXIAIrwAsqVatWqpVq5YWL16swsLCUvsYY9S7d28dOHBAn332mTZu3Kj27durW7duLldnt2/frgULFujjjz/W0qVLtXnzZo0YMcK5Pz8/XykpKVq9erXWr1+v+Ph4JScnKz8/v0rey/79+9WlSxe1bdtW6enpWrp0qX799Vf179/fpd/cuXNVs2ZNffvtt3r55Zf117/+VWlpaZIkh8Oh2267TaGhofr22281a9YsPfPMMy7Hf/fdd5KkL7/8Uvv373e5j3TlypXasWOHVq5cqblz52rOnDmaM2dOmTWvWbNGoaGhSkxMdLY5HA716tVL33zzjebNm6effvpJL730kvz9/Z19CgoK9MYbb+g///mPli5dqlWrVqlfv3767LPP9Nlnn+nf//63Zs2apQ8++MBlvKuuukqrV6+u3MQCuDAZALCoDz74wNSrV88EBwebTp06mXHjxpktW7Y49y9fvtzUrl3bnDhxwuW4Zs2amTfffNMYY8z48eONv7+/2bNnj3P/559/bvz8/Mz+/ftLHffUqVMmLCzMfPzxx842SWbRokVl1pqSkmJuvfXWUvc999xzpkePHi5te/bsMZJMZmamMcaYLl26mM6dO7v06dixo3nyySedNQcEBLjUnJaW5lJXdna2kWS+//77ErXFxsaaU6dOOdvuvPNOc9ddd5X5fl577TVz6aWXurQtW7bM+Pn5OWs+2+zZs40ks337dmfb8OHDTWhoqMnPz3e29ezZ0wwfPtzl2I8++sj4+fmZoqKiMmsCcHHgyisAy7r99tu1b98+LVmyRD179tSqVavUvn175xXDjRs36ujRo2rQoIHzSm2tWrWUnZ2tHTt2OM8TExOjJk2aOLevvfZaORwOZWZmSjr9wbAHH3xQzZs3V506dVSnTh0dPXpUdru9St7Hxo0btXLlSpcaExISJMmlziuuuMLluMjISOXk5EiSMjMzFR0drUaNGjn3X3XVVRWuoVWrVi5XSM88d2mOHz+u4OBgl7bNmzerSZMmat68eZnHhYaGqlmzZs7thg0bqmnTpqpVq5ZL29ljh4SEyOFwlHmVHcDFI8DbBQDA+QgODlb37t3VvXt3Pf/88xo2bJjGjx+vwYMHy+FwKDIyUqtWrSpxXN26dcs8p81mc/nfwYMH67ffftPUqVMVGxuroKAgXXvttfrjjz+q5D04HA716dNHkydPLrEvMjLS+f9r1KhRos7iT+AbY5z1uqO8c5cmPDxchw8fdmkLCQlxa5yKjH3o0CGFhoZWaAwAFzbCK4ALSsuWLZ0fUGrfvr0OHDiggIAANW3atMxj7Ha79u3bp6ioKEnSunXr5Ofn57yCuHr1ak2fPl3JycmSpD179rh8mOp8tW/fXh9++KGaNm2qgAD3vi0nJCTIbrfr119/VcOGDSWd/qDZmQIDAyXJ5cNQ7mrXrp0OHDigw4cPq169epJOXxn+5ZdftG3btnKvvrpj69atat++fZWeE4A1cdsAAEs6ePCgbrzxRs2bN08//PCDsrOz9f777+vll1/WrbfeKkm66aabdO211+q2227TsmXLtGvXLn3zzTd69tlnlZ6e7jxXcHCwUlJStGXLFq1evVojR45U//79nb+Cv+yyy/Tvf/9bGRkZ+vbbb3Xvvfe6dQXwyJEj2rx5s8vLbrdrxIgROnTokO6++25999132rlzp7744gsNHTq0wkGze/fuatasmVJSUvTDDz9o7dq1zg9sFV+RjYiIUEhIiPMDYUeOHKn0eyjWrl07XXLJJVq7dq2zrUuXLrrhhht0++23Ky0tTdnZ2fr888+1dOlSt8cptnr1avXo0eO8zwPA+givACypVq1auvrqq/Xaa6/phhtuUOvWrfXcc8/pgQce0LRp0ySdDm2fffaZbrjhBg0dOlTNmzfXgAEDtGvXLufVSel0OO3Xr5+Sk5PVo0cPtW7dWtOnT3fu/9e//qXDhw+rXbt2GjRokEaOHKmIiIhK17xq1Sq1a9fO5fX8888rKipKa9euVVFRkXr27KnWrVtr1KhRqlOnToWfoerv76/Fixfr6NGj6tixo4YNG6Znn31Wkpz3pgYEBOiNN97Qm2++qaioKGfId4e/v7+GDh2qd955x6X9ww8/VMeOHXX33XerZcuWGjt27Hlf6d27d6+++eYbDRky5LzOA+DCYDPGGG8XAQDeMmHCBC1evLjK/uqUL1m7dq06d+6s7du3u3xIqqr8+uuvatWqlTZu3KjY2NgqP3+xJ554QkeOHNGsWbOqbQwA1sE9rwBwgVi0aJFq1aql+Ph4bd++XaNGjdJ1111XLcFVOv1UgLfeekt2u71aw2tERIQef/zxajs/AGshvALABSI/P19jx47Vnj17FB4erptuukmvvvpqtY55PrceVNQTTzxR7WMAsA5uGwAAAIBl8IEtAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGf8fDe+jgVG75t0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of sepal length\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(data['SepalLengthCm'], bins=20, color='skyblue', edgecolor='k')\n",
    "plt.xlabel('Sepal Length (cm)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Sepal Length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "15444eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of sepal length\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(data['PetalLengthCm'], bins=20, color='skyblue', edgecolor='k')\n",
    "plt.xlabel('Petal Length Cm')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Petal Length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d4fc28d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of samples :  150\n",
      "number of samples in training data :  120\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = data.drop(columns=['Species'])\n",
    "y = data['Species']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "print(\"total number of samples : \",len(data))\n",
    "print(\"number of samples in training data : \",len(X_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de556b9",
   "metadata": {},
   "source": [
    "## Logistic Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10e4a420",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "model = LogisticRegression(max_iter=200)\n",
    "model.fit(X_train_scaled, y_train)\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "# model = LogisticRegression(max_iter=500)\n",
    "# model.fit(X_train,y_train)\n",
    "# y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b90a0cf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_pred,y_test),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "470d5d30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-versicolor' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "ans = model.predict([[0,5.1, 3.5, 1.4, 0.2],\n",
    "                     [1,6.7, 3.0, 5.2, 2.3]])\n",
    "print(ans)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4019af3",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "801968c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model2 = DecisionTreeClassifier()\n",
    "model2.fit(X_train,y_train)\n",
    "y_pred = model2.predict(X_test)\n",
    "print(len(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3f22d7f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_pred,y_test)*100,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5f3ed5e",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}