ml-drug-classification (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "94047af2",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:12.714180Z",
     "iopub.status.busy": "2024-09-07T22:12:12.713706Z",
     "iopub.status.idle": "2024-09-07T22:12:13.193947Z",
     "shell.execute_reply": "2024-09-07T22:12:13.192594Z"
    },
    "papermill": {
     "duration": 0.498467,
     "end_time": "2024-09-07T22:12:13.196944",
     "exception": false,
     "start_time": "2024-09-07T22:12:12.698477",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/drug-classification/drug200.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d5957e",
   "metadata": {
    "papermill": {
     "duration": 0.012155,
     "end_time": "2024-09-07T22:12:13.221863",
     "exception": false,
     "start_time": "2024-09-07T22:12:13.209708",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 1.Drug Classification\n",
    "- By this notebook we will useing KNeighborsClassifier and DecisionTreeClassifier to building a simple ML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a164f59",
   "metadata": {
    "papermill": {
     "duration": 0.012227,
     "end_time": "2024-09-07T22:12:13.246856",
     "exception": false,
     "start_time": "2024-09-07T22:12:13.234629",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![](https://wisdomml.in/wp-content/uploads/2023/03/drug-768x539.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae19b940",
   "metadata": {
    "papermill": {
     "duration": 0.012394,
     "end_time": "2024-09-07T22:12:13.272822",
     "exception": false,
     "start_time": "2024-09-07T22:12:13.260428",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2.About Notebook\n",
    " - As a beginner in machine learning, predicting the right drug treatment for patients offers a valuable opportunity to apply and refine various modeling techniques. This  dataset provides an excellent starting point for exploring algorithms that can accurately determine the most suitable drug for a patient based on their medical and personal attributes. By analyzing this data, we aim to assist healthcare professionals in making more informed, data-driven decisions, ultimately improving patient outcomes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3e6e9ac",
   "metadata": {
    "papermill": {
     "duration": 0.013669,
     "end_time": "2024-09-07T22:12:13.299534",
     "exception": false,
     "start_time": "2024-09-07T22:12:13.285865",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3.Importing the libraries\n",
    "- that librarys we need it in our notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9878808",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:13.327407Z",
     "iopub.status.busy": "2024-09-07T22:12:13.326806Z",
     "iopub.status.idle": "2024-09-07T22:12:15.334987Z",
     "shell.execute_reply": "2024-09-07T22:12:15.333514Z"
    },
    "papermill": {
     "duration": 2.026658,
     "end_time": "2024-09-07T22:12:15.339170",
     "exception": false,
     "start_time": "2024-09-07T22:12:13.312512",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a0f6b5d",
   "metadata": {
    "papermill": {
     "duration": 0.013643,
     "end_time": "2024-09-07T22:12:15.366109",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.352466",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 4.Export Our Data From CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d120eb5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:15.395903Z",
     "iopub.status.busy": "2024-09-07T22:12:15.395122Z",
     "iopub.status.idle": "2024-09-07T22:12:15.450131Z",
     "shell.execute_reply": "2024-09-07T22:12:15.448243Z"
    },
    "papermill": {
     "duration": 0.073558,
     "end_time": "2024-09-07T22:12:15.453441",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.379883",
     "status": "completed"
    },
    "tags": []
   },
   "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>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>25.355</td>\n",
       "      <td>DrugY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>13.093</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>10.114</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>7.798</td>\n",
       "      <td>drugX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>18.043</td>\n",
       "      <td>DrugY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
       "0   23   F    HIGH        HIGH   25.355  DrugY\n",
       "1   47   M     LOW        HIGH   13.093  drugC\n",
       "2   47   M     LOW        HIGH   10.114  drugC\n",
       "3   28   F  NORMAL        HIGH    7.798  drugX\n",
       "4   61   F     LOW        HIGH   18.043  DrugY"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/drug-classification/drug200.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b2753c",
   "metadata": {
    "papermill": {
     "duration": 0.013767,
     "end_time": "2024-09-07T22:12:15.481057",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.467290",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- If we noticed after we are exporting data we find 4 columns need to changing to numeric values: \n",
    "- The columns are ['sex','BP','Cholesterol','Drug']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c217476",
   "metadata": {
    "papermill": {
     "duration": 0.012763,
     "end_time": "2024-09-07T22:12:15.507016",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.494253",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 5.Value count to each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6ce0b6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:15.534968Z",
     "iopub.status.busy": "2024-09-07T22:12:15.534494Z",
     "iopub.status.idle": "2024-09-07T22:12:15.549489Z",
     "shell.execute_reply": "2024-09-07T22:12:15.548166Z"
    },
    "papermill": {
     "duration": 0.03176,
     "end_time": "2024-09-07T22:12:15.552214",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.520454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "M    104\n",
       "F     96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a68a403",
   "metadata": {
    "papermill": {
     "duration": 0.013248,
     "end_time": "2024-09-07T22:12:15.579202",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.565954",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- The column 'sex' has:\n",
    "- 104 Males\n",
    "- 96 Females\n",
    "- With the total coantty are 200 items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77ca2b0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:15.606445Z",
     "iopub.status.busy": "2024-09-07T22:12:15.606023Z",
     "iopub.status.idle": "2024-09-07T22:12:15.616184Z",
     "shell.execute_reply": "2024-09-07T22:12:15.614889Z"
    },
    "papermill": {
     "duration": 0.02685,
     "end_time": "2024-09-07T22:12:15.618863",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.592013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BP\n",
       "HIGH      77\n",
       "LOW       64\n",
       "NORMAL    59\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BP'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d3fb05",
   "metadata": {
    "papermill": {
     "duration": 0.012508,
     "end_time": "2024-09-07T22:12:15.644529",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.632021",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- The column 'BP' has :\n",
    "- 77 High\n",
    "- 64 Low\n",
    "- 59 Normal\n",
    "- With the total coantty are 200 items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da2e3e61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:15.673750Z",
     "iopub.status.busy": "2024-09-07T22:12:15.673257Z",
     "iopub.status.idle": "2024-09-07T22:12:15.683065Z",
     "shell.execute_reply": "2024-09-07T22:12:15.681869Z"
    },
    "papermill": {
     "duration": 0.026707,
     "end_time": "2024-09-07T22:12:15.685619",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.658912",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cholesterol\n",
       "HIGH      103\n",
       "NORMAL     97\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cholesterol'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbad2ec",
   "metadata": {
    "papermill": {
     "duration": 0.012853,
     "end_time": "2024-09-07T22:12:15.711604",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.698751",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- The column 'Cholestrol' has :\n",
    "- 103 High\n",
    "- 97 Normal\n",
    "- with the total coantty are 200 items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4011502f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:15.740230Z",
     "iopub.status.busy": "2024-09-07T22:12:15.739762Z",
     "iopub.status.idle": "2024-09-07T22:12:15.750103Z",
     "shell.execute_reply": "2024-09-07T22:12:15.748882Z"
    },
    "papermill": {
     "duration": 0.027851,
     "end_time": "2024-09-07T22:12:15.752800",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.724949",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drug\n",
       "DrugY    91\n",
       "drugX    54\n",
       "drugA    23\n",
       "drugC    16\n",
       "drugB    16\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Drug'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10c0d9a",
   "metadata": {
    "papermill": {
     "duration": 0.013634,
     "end_time": "2024-09-07T22:12:15.780506",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.766872",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- The column 'Drug' has :\n",
    "- 23 drugA\n",
    "- 16 drugB\n",
    "- 16 drugC\n",
    "- 54 drugX\n",
    "- 91 DrugY\n",
    "- With the total coantty are 200 items"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c6f884",
   "metadata": {
    "papermill": {
     "duration": 0.013443,
     "end_time": "2024-09-07T22:12:15.807364",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.793921",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 6.Convert columns to the numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7266300d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:15.838753Z",
     "iopub.status.busy": "2024-09-07T22:12:15.838273Z",
     "iopub.status.idle": "2024-09-07T22:12:15.858169Z",
     "shell.execute_reply": "2024-09-07T22:12:15.856585Z"
    },
    "papermill": {
     "duration": 0.039867,
     "end_time": "2024-09-07T22:12:15.861353",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.821486",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,columns = ['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e49bfa7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:15.892735Z",
     "iopub.status.busy": "2024-09-07T22:12:15.892261Z",
     "iopub.status.idle": "2024-09-07T22:12:15.902671Z",
     "shell.execute_reply": "2024-09-07T22:12:15.901365Z"
    },
    "papermill": {
     "duration": 0.028637,
     "end_time": "2024-09-07T22:12:15.905573",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.876936",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,columns=['BP'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f2e7969",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:15.934059Z",
     "iopub.status.busy": "2024-09-07T22:12:15.933598Z",
     "iopub.status.idle": "2024-09-07T22:12:15.944537Z",
     "shell.execute_reply": "2024-09-07T22:12:15.943335Z"
    },
    "papermill": {
     "duration": 0.028456,
     "end_time": "2024-09-07T22:12:15.947325",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.918869",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df,columns=['Cholesterol'],drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f1b8da7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:15.977469Z",
     "iopub.status.busy": "2024-09-07T22:12:15.977004Z",
     "iopub.status.idle": "2024-09-07T22:12:15.985609Z",
     "shell.execute_reply": "2024-09-07T22:12:15.984317Z"
    },
    "papermill": {
     "duration": 0.026212,
     "end_time": "2024-09-07T22:12:15.988340",
     "exception": false,
     "start_time": "2024-09-07T22:12:15.962128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "label_mapping = {\n",
    "    'drugA': 0,\n",
    "    'drugB': 1,\n",
    "    'drugC': 2,\n",
    "    'drugX': 3,\n",
    "    'DrugY': 4,\n",
    "}\n",
    "# Map the labels to their respective categories\n",
    "df['label_Drug'] = df['Drug'].map(label_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4515f5c2",
   "metadata": {
    "papermill": {
     "duration": 0.012704,
     "end_time": "2024-09-07T22:12:16.014311",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.001607",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 7.Select values ti X and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2955775",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:16.043279Z",
     "iopub.status.busy": "2024-09-07T22:12:16.042831Z",
     "iopub.status.idle": "2024-09-07T22:12:16.050420Z",
     "shell.execute_reply": "2024-09-07T22:12:16.049159Z"
    },
    "papermill": {
     "duration": 0.024868,
     "end_time": "2024-09-07T22:12:16.053070",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.028202",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = df[['Age','Na_to_K','Sex_M','BP_LOW','BP_NORMAL','Cholesterol_NORMAL']]\n",
    "y = df['label_Drug']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8399c3",
   "metadata": {
    "papermill": {
     "duration": 0.01277,
     "end_time": "2024-09-07T22:12:16.080038",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.067268",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 8.Silpte values to values testing and training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd8fb316",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:16.108466Z",
     "iopub.status.busy": "2024-09-07T22:12:16.107988Z",
     "iopub.status.idle": "2024-09-07T22:12:16.118396Z",
     "shell.execute_reply": "2024-09-07T22:12:16.116933Z"
    },
    "papermill": {
     "duration": 0.027761,
     "end_time": "2024-09-07T22:12:16.121139",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.093378",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train , X_test , y_train , y_test = train_test_split(x,y,test_size=.20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee1ba47",
   "metadata": {
    "papermill": {
     "duration": 0.012874,
     "end_time": "2024-09-07T22:12:16.147341",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.134467",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 9.Bilduing the model 'KNeighborsClassifier'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee3b14a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:16.176825Z",
     "iopub.status.busy": "2024-09-07T22:12:16.175694Z",
     "iopub.status.idle": "2024-09-07T22:12:16.190586Z",
     "shell.execute_reply": "2024-09-07T22:12:16.189399Z"
    },
    "papermill": {
     "duration": 0.032826,
     "end_time": "2024-09-07T22:12:16.193571",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.160745",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=5)\n",
    "model.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8043ae7",
   "metadata": {
    "papermill": {
     "duration": 0.013316,
     "end_time": "2024-09-07T22:12:16.220521",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.207205",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 10.Predict the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8e039a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:16.249283Z",
     "iopub.status.busy": "2024-09-07T22:12:16.248842Z",
     "iopub.status.idle": "2024-09-07T22:12:16.264080Z",
     "shell.execute_reply": "2024-09-07T22:12:16.262918Z"
    },
    "papermill": {
     "duration": 0.032654,
     "end_time": "2024-09-07T22:12:16.266735",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.234081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 2, 4, 4, 1, 0, 4, 0, 4, 0, 3, 1, 2, 1, 4, 4, 3, 3, 1, 4, 4,\n",
       "       4, 4, 4, 3, 3, 4, 4, 0, 0, 4, 4, 3, 4, 3, 4, 4, 3, 4])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf50ae7",
   "metadata": {
    "papermill": {
     "duration": 0.014227,
     "end_time": "2024-09-07T22:12:16.294655",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.280428",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 11.Check accuracy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79d1f641",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:16.324604Z",
     "iopub.status.busy": "2024-09-07T22:12:16.324048Z",
     "iopub.status.idle": "2024-09-07T22:12:16.334943Z",
     "shell.execute_reply": "2024-09-07T22:12:16.333551Z"
    },
    "papermill": {
     "duration": 0.028948,
     "end_time": "2024-09-07T22:12:16.337602",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.308654",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test , y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa09c1fb",
   "metadata": {
    "papermill": {
     "duration": 0.013885,
     "end_time": "2024-09-07T22:12:16.365499",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.351614",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 12.Find the best value of K(the number of neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "78df755e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:16.395331Z",
     "iopub.status.busy": "2024-09-07T22:12:16.394858Z",
     "iopub.status.idle": "2024-09-07T22:12:16.445328Z",
     "shell.execute_reply": "2024-09-07T22:12:16.444055Z"
    },
    "papermill": {
     "duration": 0.068521,
     "end_time": "2024-09-07T22:12:16.448000",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.379479",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best k value is: 3\n"
     ]
    }
   ],
   "source": [
    "# Define the range of k values to test\n",
    "k_values = range(3,10,2)\n",
    "test_accuracies = []\n",
    "\n",
    "# Train the k-NN classifier with different values of k and evaluate on the test set\n",
    "for k in k_values:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)  # Train the model\n",
    "    y_pred = knn.predict(X_test)  # Predict on the test set\n",
    "    accuracy = accuracy_score(y_test, y_pred)  # Calculate accuracy\n",
    "    test_accuracies.append(accuracy)\n",
    "\n",
    "# Determine the best k\n",
    "best_k = k_values[np.argmax(test_accuracies)]\n",
    "print(f\"The best k value is: {best_k}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3ca709",
   "metadata": {
    "papermill": {
     "duration": 0.013595,
     "end_time": "2024-09-07T22:12:16.475769",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.462174",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 13.Viualize K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c88d6713",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:16.505767Z",
     "iopub.status.busy": "2024-09-07T22:12:16.505289Z",
     "iopub.status.idle": "2024-09-07T22:12:16.823121Z",
     "shell.execute_reply": "2024-09-07T22:12:16.821853Z"
    },
    "papermill": {
     "duration": 0.33621,
     "end_time": "2024-09-07T22:12:16.826033",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.489823",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the k values vs. accuracy on the test set\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(k_values, test_accuracies, marker='o')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Test Set Accuracy')\n",
    "plt.title('k-NN Varying k Value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38ad536",
   "metadata": {
    "papermill": {
     "duration": 0.01427,
     "end_time": "2024-09-07T22:12:16.855353",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.841083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 14.Bilduing the model 'DecisionTreeClassifier'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "18b8e00f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:16.886443Z",
     "iopub.status.busy": "2024-09-07T22:12:16.886014Z",
     "iopub.status.idle": "2024-09-07T22:12:16.902368Z",
     "shell.execute_reply": "2024-09-07T22:12:16.901023Z"
    },
    "papermill": {
     "duration": 0.035451,
     "end_time": "2024-09-07T22:12:16.905402",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.869951",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model DecisionTree\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5684f9c7",
   "metadata": {
    "papermill": {
     "duration": 0.01469,
     "end_time": "2024-09-07T22:12:16.935142",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.920452",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 15.Predict the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d4576e84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:16.967782Z",
     "iopub.status.busy": "2024-09-07T22:12:16.967338Z",
     "iopub.status.idle": "2024-09-07T22:12:16.977727Z",
     "shell.execute_reply": "2024-09-07T22:12:16.976377Z"
    },
    "papermill": {
     "duration": 0.03007,
     "end_time": "2024-09-07T22:12:16.980147",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.950077",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 0, 4, 4, 2, 0, 4, 3, 4, 0, 3, 3, 1, 2, 4, 4, 1, 1, 3, 4, 4,\n",
       "       4, 4, 4, 0, 2, 4, 4, 3, 3, 4, 4, 1, 4, 3, 4, 4, 3, 4])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict and accuracy\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c28e9e",
   "metadata": {
    "papermill": {
     "duration": 0.014786,
     "end_time": "2024-09-07T22:12:17.010761",
     "exception": false,
     "start_time": "2024-09-07T22:12:16.995975",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 16.Check accuracy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "838279e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:17.042739Z",
     "iopub.status.busy": "2024-09-07T22:12:17.042277Z",
     "iopub.status.idle": "2024-09-07T22:12:17.051728Z",
     "shell.execute_reply": "2024-09-07T22:12:17.050472Z"
    },
    "papermill": {
     "duration": 0.028919,
     "end_time": "2024-09-07T22:12:17.054642",
     "exception": false,
     "start_time": "2024-09-07T22:12:17.025723",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8774914a",
   "metadata": {
    "papermill": {
     "duration": 0.015799,
     "end_time": "2024-09-07T22:12:17.086036",
     "exception": false,
     "start_time": "2024-09-07T22:12:17.070237",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 17.Visualize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "947bb068",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-07T22:12:17.119274Z",
     "iopub.status.busy": "2024-09-07T22:12:17.118811Z",
     "iopub.status.idle": "2024-09-07T22:12:17.862818Z",
     "shell.execute_reply": "2024-09-07T22:12:17.861314Z"
    },
    "papermill": {
     "duration": 0.767259,
     "end_time": "2024-09-07T22:12:17.869575",
     "exception": false,
     "start_time": "2024-09-07T22:12:17.102316",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "tree.plot_tree(model)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 830916,
     "sourceId": 1419436,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30761,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "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": "3.10.14"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 9.201579,
   "end_time": "2024-09-07T22:12:18.626744",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-09-07T22:12:09.425165",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}