Process_shatel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as nb\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "میانگین برای پیوسته -مد برای گسسته"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>code</th>\n",
       "      <th>next operator</th>\n",
       "      <th>reasons</th>\n",
       "      <th>استان</th>\n",
       "      <th>شهر</th>\n",
       "      <th>speed</th>\n",
       "      <th>churn</th>\n",
       "      <th>CSCalls</th>\n",
       "      <th>SalesCalls</th>\n",
       "      <th>namava</th>\n",
       "      <th>shoteltalk</th>\n",
       "      <th>casper</th>\n",
       "      <th>Android Security</th>\n",
       "      <th>VocNo</th>\n",
       "      <th>195No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41661474</td>\n",
       "      <td>استفاده ای ندارند</td>\n",
       "      <td>افت سرعت/قطعی سرویس</td>\n",
       "      <td>تهران</td>\n",
       "      <td>تهران</td>\n",
       "      <td>8192</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41661471</td>\n",
       "      <td>استفاده ای ندارند</td>\n",
       "      <td>افت سرعت/قطعی سرویس</td>\n",
       "      <td>تهران</td>\n",
       "      <td>تهران</td>\n",
       "      <td>8192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41661457</td>\n",
       "      <td>استفاده ای ندارند</td>\n",
       "      <td>افت سرعت/قطعی سرویس</td>\n",
       "      <td>تهران</td>\n",
       "      <td>تهران</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41661447</td>\n",
       "      <td>استفاده ای ندارند</td>\n",
       "      <td>افت سرعت/قطعی سرویس</td>\n",
       "      <td>آذربایجان شرقی</td>\n",
       "      <td>تبریز</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41661306</td>\n",
       "      <td>آسیاتک</td>\n",
       "      <td>افت سرعت/قطعی سرویس</td>\n",
       "      <td>آذربایجان شرقی</td>\n",
       "      <td>تبریز</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>41572306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>41572242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8192</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>41572220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1024</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>41572213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8192</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>41572181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1499 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          code      next operator              reasons           استان    شهر   \n",
       "0     41661474  استفاده ای ندارند  افت سرعت/قطعی سرویس           تهران  تهران  \\\n",
       "1     41661471  استفاده ای ندارند  افت سرعت/قطعی سرویس           تهران  تهران   \n",
       "2     41661457  استفاده ای ندارند  افت سرعت/قطعی سرویس           تهران  تهران   \n",
       "3     41661447  استفاده ای ندارند  افت سرعت/قطعی سرویس  آذربایجان شرقی  تبریز   \n",
       "4     41661306             آسیاتک  افت سرعت/قطعی سرویس  آذربایجان شرقی  تبریز   \n",
       "...        ...                ...                  ...             ...    ...   \n",
       "1494  41572306                NaN                  NaN             NaN    NaN   \n",
       "1495  41572242                NaN                  NaN             NaN    NaN   \n",
       "1496  41572220                NaN                  NaN             NaN    NaN   \n",
       "1497  41572213                NaN                  NaN             NaN    NaN   \n",
       "1498  41572181                NaN                  NaN             NaN    NaN   \n",
       "\n",
       "      speed  churn  CSCalls  SalesCalls  namava  shoteltalk  casper   \n",
       "0      8192      0        7           3       0           0       0  \\\n",
       "1      8192      0        1           6       0           0       0   \n",
       "2       512      0        7           6       0           0       0   \n",
       "3       512      0        5           4       0           0       0   \n",
       "4       512      0        1           4       0           0       1   \n",
       "...     ...    ...      ...         ...     ...         ...     ...   \n",
       "1494   4096      1       23          20       0           0       0   \n",
       "1495   8192      1       20           9       0           0       0   \n",
       "1496   1024      1        3          19       0           0       0   \n",
       "1497   8192      1        2          10       0           0       0   \n",
       "1498   4096      1        5          12       0           0       0   \n",
       "\n",
       "      Android Security  VocNo  195No  \n",
       "0                    0      0      0  \n",
       "1                    0      0      0  \n",
       "2                    0      0      0  \n",
       "3                    0      0      0  \n",
       "4                    0      0      0  \n",
       "...                ...    ...    ...  \n",
       "1494                 0      0      0  \n",
       "1495                 0      0      0  \n",
       "1496                 0      0      0  \n",
       "1497                 0      0      0  \n",
       "1498                 0      0      0  \n",
       "\n",
       "[1499 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('churnshatel.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_comp = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code                   0\n",
       "next operator       1429\n",
       "reasons             1429\n",
       "استان                751\n",
       "شهر                  751\n",
       "speed                  0\n",
       "churn                  0\n",
       "CSCalls                0\n",
       "SalesCalls             0\n",
       "namava                 0\n",
       "shoteltalk             0\n",
       "casper                 0\n",
       "Android Security       0\n",
       "VocNo                  0\n",
       "195No                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['next operator'] , df['reasons'] ,df['code'],df['استان']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1499 entries, 0 to 1498\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   شهر               748 non-null    object\n",
      " 1   speed             1499 non-null   int64 \n",
      " 2   churn             1499 non-null   int64 \n",
      " 3   CSCalls           1499 non-null   int64 \n",
      " 4   SalesCalls        1499 non-null   int64 \n",
      " 5   namava            1499 non-null   int64 \n",
      " 6   shoteltalk        1499 non-null   int64 \n",
      " 7   casper            1499 non-null   int64 \n",
      " 8   Android Security  1499 non-null   int64 \n",
      " 9   VocNo             1499 non-null   int64 \n",
      " 10  195No             1499 non-null   int64 \n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 128.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"شهر\" : \"city\"} , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "تهران     498\n",
       "کرج       112\n",
       "اصفهان     83\n",
       "تبریز      55\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['city'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1499 entries, 0 to 1498\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   city              748 non-null    object\n",
      " 1   speed             1499 non-null   int64 \n",
      " 2   churn             1499 non-null   int64 \n",
      " 3   CSCalls           1499 non-null   int64 \n",
      " 4   SalesCalls        1499 non-null   int64 \n",
      " 5   namava            1499 non-null   int64 \n",
      " 6   shoteltalk        1499 non-null   int64 \n",
      " 7   casper            1499 non-null   int64 \n",
      " 8   Android Security  1499 non-null   int64 \n",
      " 9   VocNo             1499 non-null   int64 \n",
      " 10  195No             1499 non-null   int64 \n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 128.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "چون سیتی داده گسسته هستش از مد نمیشه استفاده کرد جون مد یا میانگین خود مقادیر خالی میشود"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"from sklearn.impute import KNNImputer\\nxm = KNNImputer(n_neighbors=3  , weights='uniform' )\\n\\ndf[['city']]=xm.fit_transform(df[['city']])\\n\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''from sklearn.impute import KNNImputer\n",
    "xm = KNNImputer(n_neighbors=3  , weights='uniform' )\n",
    "\n",
    "df[['city']]=xm.fit_transform(df[['city']])\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['city'] = df['city'].fillna('تهران') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                0\n",
       "speed               0\n",
       "churn               0\n",
       "CSCalls             0\n",
       "SalesCalls          0\n",
       "namava              0\n",
       "shoteltalk          0\n",
       "casper              0\n",
       "Android Security    0\n",
       "VocNo               0\n",
       "195No               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>city</th>\n",
       "      <th>speed</th>\n",
       "      <th>churn</th>\n",
       "      <th>CSCalls</th>\n",
       "      <th>SalesCalls</th>\n",
       "      <th>namava</th>\n",
       "      <th>shoteltalk</th>\n",
       "      <th>casper</th>\n",
       "      <th>Android Security</th>\n",
       "      <th>VocNo</th>\n",
       "      <th>195No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>تهران</td>\n",
       "      <td>8192</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>تهران</td>\n",
       "      <td>8192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>تهران</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>تبریز</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>تبریز</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    city  speed  churn  CSCalls  SalesCalls  namava  shoteltalk  casper   \n",
       "0  تهران   8192      0        7           3       0           0       0  \\\n",
       "1  تهران   8192      0        1           6       0           0       0   \n",
       "2  تهران    512      0        7           6       0           0       0   \n",
       "3  تبریز    512      0        5           4       0           0       0   \n",
       "4  تبریز    512      0        1           4       0           0       1   \n",
       "\n",
       "   Android Security  VocNo  195No  \n",
       "0                 0      0      0  \n",
       "1                 0      0      0  \n",
       "2                 0      0      0  \n",
       "3                 0      0      0  \n",
       "4                 0      0      0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df['city'] = le.fit_transform(df['city'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "licat = ['churn','Android Security','casper','shoteltalk','namava','city']\n",
    "df[licat]=df[licat].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>speed</th>\n",
       "      <th>CSCalls</th>\n",
       "      <th>SalesCalls</th>\n",
       "      <th>VocNo</th>\n",
       "      <th>195No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1499.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5158.596398</td>\n",
       "      <td>4.060040</td>\n",
       "      <td>10.559706</td>\n",
       "      <td>0.030687</td>\n",
       "      <td>0.013342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4807.360146</td>\n",
       "      <td>4.543411</td>\n",
       "      <td>6.099498</td>\n",
       "      <td>0.194360</td>\n",
       "      <td>0.170864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>128.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>512.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4096.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8192.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16384.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              speed      CSCalls   SalesCalls        VocNo        195No\n",
       "count   1499.000000  1499.000000  1499.000000  1499.000000  1499.000000\n",
       "mean    5158.596398     4.060040    10.559706     0.030687     0.013342\n",
       "std     4807.360146     4.543411     6.099498     0.194360     0.170864\n",
       "min      128.000000     0.000000     2.000000     0.000000     0.000000\n",
       "25%      512.000000     1.000000     6.000000     0.000000     0.000000\n",
       "50%     4096.000000     3.000000     9.000000     0.000000     0.000000\n",
       "75%     8192.000000     5.000000    13.000000     0.000000     0.000000\n",
       "max    16384.000000    49.000000    43.000000     2.000000     5.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mean + 3*std < max -----#we have outlier!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19874"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5453+ 3*4807  #  19874>max  so thats okay! Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.56"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4.06+ 3*4.5   #  17.56 < max  thats problem!  CSCalls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>city</th>\n",
       "      <th>speed</th>\n",
       "      <th>churn</th>\n",
       "      <th>CSCalls</th>\n",
       "      <th>SalesCalls</th>\n",
       "      <th>namava</th>\n",
       "      <th>shoteltalk</th>\n",
       "      <th>casper</th>\n",
       "      <th>Android Security</th>\n",
       "      <th>VocNo</th>\n",
       "      <th>195No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>2</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>2</td>\n",
       "      <td>8192</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1038</th>\n",
       "      <td>2</td>\n",
       "      <td>8192</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>2</td>\n",
       "      <td>1024</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>2</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>2</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city  speed churn  CSCalls  SalesCalls namava shoteltalk casper   \n",
       "885     2   4096     1       32          11      0          0      0  \\\n",
       "975     2   8192     1       49          18      0          0      0   \n",
       "1038    2   8192     1       37          12      0          0      0   \n",
       "1055    2   1024     1       31          39      0          0      0   \n",
       "1327    2    512     1       35          20      0          0      1   \n",
       "1341    2   4096     1       32          24      0          0      0   \n",
       "\n",
       "     Android Security  VocNo  195No  \n",
       "885                 0      0      0  \n",
       "975                 0      0      0  \n",
       "1038                0      0      0  \n",
       "1055                0      0      2  \n",
       "1327                0      0      0  \n",
       "1341                0      0      0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('CSCalls>30')  #این هارا حدف نخواهیم کرد چون اظلاعات مهمی هستندهم چنین مستری هایی هستند که شرکت را ترک کردند"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box',figsize=(10,10) , layout=(4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "speed            Axes(0.125,0.653529;0.227941x0.226471)\n",
       "CSCalls       Axes(0.398529,0.653529;0.227941x0.226471)\n",
       "SalesCalls    Axes(0.672059,0.653529;0.227941x0.226471)\n",
       "VocNo            Axes(0.125,0.381765;0.227941x0.226471)\n",
       "195No         Axes(0.398529,0.381765;0.227941x0.226471)\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box',figsize=(8,8) , layout=(3,3) , subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>city</th>\n",
       "      <th>speed</th>\n",
       "      <th>churn</th>\n",
       "      <th>CSCalls</th>\n",
       "      <th>SalesCalls</th>\n",
       "      <th>namava</th>\n",
       "      <th>shoteltalk</th>\n",
       "      <th>casper</th>\n",
       "      <th>Android Security</th>\n",
       "      <th>VocNo</th>\n",
       "      <th>195No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>8192</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city  speed churn  CSCalls  SalesCalls namava shoteltalk casper   \n",
       "0    2   8192     0        7           3      0          0      0  \\\n",
       "1    2   8192     0        1           6      0          0      0   \n",
       "2    2    512     0        7           6      0          0      0   \n",
       "3    1    512     0        5           4      0          0      0   \n",
       "4    1    512     0        1           4      0          0      1   \n",
       "\n",
       "  Android Security  VocNo  195No  \n",
       "0                0      0      0  \n",
       "1                0      0      0  \n",
       "2                0      0      0  \n",
       "3                0      0      0  \n",
       "4                0      0      0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classification stage"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split input and output\n",
    "x = df.drop('churn' , axis=1)\n",
    "y = df['churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test =train_test_split(x,y,test_size=0.25,random_state=42 , shuffle=True)\n",
    "\n",
    "#shuffle نسبت هارو رعایت میکنه برخلاف سمپلینگ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "churn\n",
       "0    570\n",
       "1    554\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## درخت تصمیم DT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=15, min_samples_split=3,\n",
       "                       random_state=42)</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=15, min_samples_split=3,\n",
       "                       random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=15, min_samples_split=3,\n",
       "                       random_state=42)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(criterion='entropy' , max_depth=15 ,min_samples_split=3 , min_samples_leaf=1 , random_state=42)\n",
    "\n",
    "\n",
    "#training on train set\n",
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "نرمال کردن و اسکیل کردن برای درخت تصمیم خوب نیست دقت رو میاره پایین"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#apply model on test\n",
    "y_pred = dt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(375,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8266666666666667"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model evaluation\n",
    "from sklearn.metrics import accuracy_score , confusion_matrix\n",
    "from IPython.display import display\n",
    "\n",
    "acc = accuracy_score(y_test,y_pred)\n",
    "confusion_matrix(y_test,y_pred)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9341637010676157"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#acc on train data\n",
    "ytp=dt.predict(x_train)\n",
    "accuracy_score(y_train, ytp)\n",
    "#اگر اختلاف این با ج روی داده های تست خیلی زیاد بود در این صورت اورفیتینگ رخ داده"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>col_0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144</td>\n",
       "      <td>38</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>166</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>171</td>\n",
       "      <td>204</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    0    1  All\n",
       "churn               \n",
       "0      144   38  182\n",
       "1       27  166  193\n",
       "All    171  204  375"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = pd.crosstab(y_test,y_pred , margins=True )\n",
    "display(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.79      0.82       182\n",
      "           1       0.81      0.86      0.84       193\n",
      "\n",
      "    accuracy                           0.83       375\n",
      "   macro avg       0.83      0.83      0.83       375\n",
      "weighted avg       0.83      0.83      0.83       375\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "#precision معیار صحت _ تا چه اندازه میتونیم به مدلمون اعتماد بکنیم"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#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>KNeighborsClassifier(n_neighbors=10, p=1)</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=10, p=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=10, p=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=10 , weights='uniform', p=1 )\n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "yknn=knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8293333333333334"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,yknn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82       182\n",
      "           1       0.83      0.83      0.83       193\n",
      "\n",
      "    accuracy                           0.83       375\n",
      "   macro avg       0.83      0.83      0.83       375\n",
      "weighted avg       0.83      0.83      0.83       375\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,yknn))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "روی داده های گسسته روش \" ک ن ن \" خطای زیادی داره"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gaussian nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7653333333333333"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb=GaussianNB()\n",
    "nb.fit(x_train, y_train)\n",
    "ynb=nb.predict(x_test)\n",
    "accuracy_score(y_test,ynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.87      0.78       182\n",
      "           1       0.85      0.66      0.74       193\n",
      "\n",
      "    accuracy                           0.77       375\n",
      "   macro avg       0.78      0.77      0.76       375\n",
      "weighted avg       0.78      0.77      0.76       375\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,ynb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>col_0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>159</td>\n",
       "      <td>23</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>128</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>224</td>\n",
       "      <td>151</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    0    1  All\n",
       "churn               \n",
       "0      159   23  182\n",
       "1       65  128  193\n",
       "All    224  151  375"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = pd.crosstab(y_test,ynb, margins=True )\n",
    "display(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>city</th>\n",
       "      <th>speed</th>\n",
       "      <th>churn</th>\n",
       "      <th>CSCalls</th>\n",
       "      <th>SalesCalls</th>\n",
       "      <th>namava</th>\n",
       "      <th>shoteltalk</th>\n",
       "      <th>casper</th>\n",
       "      <th>Android Security</th>\n",
       "      <th>VocNo</th>\n",
       "      <th>195No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021664</td>\n",
       "      <td>0.126620</td>\n",
       "      <td>0.036836</td>\n",
       "      <td>0.097297</td>\n",
       "      <td>0.012335</td>\n",
       "      <td>-0.038615</td>\n",
       "      <td>-0.005439</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.020065</td>\n",
       "      <td>0.009924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speed</th>\n",
       "      <td>0.021664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.264172</td>\n",
       "      <td>0.091574</td>\n",
       "      <td>0.156488</td>\n",
       "      <td>0.043554</td>\n",
       "      <td>-0.029347</td>\n",
       "      <td>-0.130163</td>\n",
       "      <td>-0.033765</td>\n",
       "      <td>0.028361</td>\n",
       "      <td>0.014352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <td>0.126620</td>\n",
       "      <td>0.264172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269423</td>\n",
       "      <td>0.566277</td>\n",
       "      <td>0.097420</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>-0.079903</td>\n",
       "      <td>0.012017</td>\n",
       "      <td>0.137867</td>\n",
       "      <td>0.070563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSCalls</th>\n",
       "      <td>0.036836</td>\n",
       "      <td>0.091574</td>\n",
       "      <td>0.269423</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.272337</td>\n",
       "      <td>0.029256</td>\n",
       "      <td>-0.017767</td>\n",
       "      <td>-0.054376</td>\n",
       "      <td>0.034307</td>\n",
       "      <td>0.101479</td>\n",
       "      <td>0.056582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalesCalls</th>\n",
       "      <td>0.097297</td>\n",
       "      <td>0.156488</td>\n",
       "      <td>0.566277</td>\n",
       "      <td>0.272337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.093456</td>\n",
       "      <td>0.001614</td>\n",
       "      <td>-0.043807</td>\n",
       "      <td>-0.003046</td>\n",
       "      <td>0.160063</td>\n",
       "      <td>0.093394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>namava</th>\n",
       "      <td>0.012335</td>\n",
       "      <td>0.043554</td>\n",
       "      <td>0.097420</td>\n",
       "      <td>0.029256</td>\n",
       "      <td>0.093456</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005022</td>\n",
       "      <td>-0.031895</td>\n",
       "      <td>-0.011291</td>\n",
       "      <td>-0.015335</td>\n",
       "      <td>-0.007584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shoteltalk</th>\n",
       "      <td>-0.038615</td>\n",
       "      <td>-0.029347</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>-0.017767</td>\n",
       "      <td>0.001614</td>\n",
       "      <td>-0.005022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016992</td>\n",
       "      <td>-0.006015</td>\n",
       "      <td>-0.008170</td>\n",
       "      <td>-0.004040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casper</th>\n",
       "      <td>-0.005439</td>\n",
       "      <td>-0.130163</td>\n",
       "      <td>-0.079903</td>\n",
       "      <td>-0.054376</td>\n",
       "      <td>-0.043807</td>\n",
       "      <td>-0.031895</td>\n",
       "      <td>-0.016992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.236340</td>\n",
       "      <td>-0.005562</td>\n",
       "      <td>0.000685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Android Security</th>\n",
       "      <td>0.004615</td>\n",
       "      <td>-0.033765</td>\n",
       "      <td>0.012017</td>\n",
       "      <td>0.034307</td>\n",
       "      <td>-0.003046</td>\n",
       "      <td>-0.011291</td>\n",
       "      <td>-0.006015</td>\n",
       "      <td>0.236340</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018366</td>\n",
       "      <td>-0.009084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VocNo</th>\n",
       "      <td>0.020065</td>\n",
       "      <td>0.028361</td>\n",
       "      <td>0.137867</td>\n",
       "      <td>0.101479</td>\n",
       "      <td>0.160063</td>\n",
       "      <td>-0.015335</td>\n",
       "      <td>-0.008170</td>\n",
       "      <td>-0.005562</td>\n",
       "      <td>-0.018366</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.188679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195No</th>\n",
       "      <td>0.009924</td>\n",
       "      <td>0.014352</td>\n",
       "      <td>0.070563</td>\n",
       "      <td>0.056582</td>\n",
       "      <td>0.093394</td>\n",
       "      <td>-0.007584</td>\n",
       "      <td>-0.004040</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>-0.009084</td>\n",
       "      <td>0.188679</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      city     speed     churn   CSCalls  SalesCalls   \n",
       "city              1.000000  0.021664  0.126620  0.036836    0.097297  \\\n",
       "speed             0.021664  1.000000  0.264172  0.091574    0.156488   \n",
       "churn             0.126620  0.264172  1.000000  0.269423    0.566277   \n",
       "CSCalls           0.036836  0.091574  0.269423  1.000000    0.272337   \n",
       "SalesCalls        0.097297  0.156488  0.566277  0.272337    1.000000   \n",
       "namava            0.012335  0.043554  0.097420  0.029256    0.093456   \n",
       "shoteltalk       -0.038615 -0.029347  0.000173 -0.017767    0.001614   \n",
       "casper           -0.005439 -0.130163 -0.079903 -0.054376   -0.043807   \n",
       "Android Security  0.004615 -0.033765  0.012017  0.034307   -0.003046   \n",
       "VocNo             0.020065  0.028361  0.137867  0.101479    0.160063   \n",
       "195No             0.009924  0.014352  0.070563  0.056582    0.093394   \n",
       "\n",
       "                    namava  shoteltalk    casper  Android Security     VocNo   \n",
       "city              0.012335   -0.038615 -0.005439          0.004615  0.020065  \\\n",
       "speed             0.043554   -0.029347 -0.130163         -0.033765  0.028361   \n",
       "churn             0.097420    0.000173 -0.079903          0.012017  0.137867   \n",
       "CSCalls           0.029256   -0.017767 -0.054376          0.034307  0.101479   \n",
       "SalesCalls        0.093456    0.001614 -0.043807         -0.003046  0.160063   \n",
       "namava            1.000000   -0.005022 -0.031895         -0.011291 -0.015335   \n",
       "shoteltalk       -0.005022    1.000000 -0.016992         -0.006015 -0.008170   \n",
       "casper           -0.031895   -0.016992  1.000000          0.236340 -0.005562   \n",
       "Android Security -0.011291   -0.006015  0.236340          1.000000 -0.018366   \n",
       "VocNo            -0.015335   -0.008170 -0.005562         -0.018366  1.000000   \n",
       "195No            -0.007584   -0.004040  0.000685         -0.009084  0.188679   \n",
       "\n",
       "                     195No  \n",
       "city              0.009924  \n",
       "speed             0.014352  \n",
       "churn             0.070563  \n",
       "CSCalls           0.056582  \n",
       "SalesCalls        0.093394  \n",
       "namava           -0.007584  \n",
       "shoteltalk       -0.004040  \n",
       "casper            0.000685  \n",
       "Android Security -0.009084  \n",
       "VocNo             0.188679  \n",
       "195No             1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ماشین بردار پشتیبان فقط برای مسائل دو کلاسه هستند"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVR support vector regression //\n",
    "SVC support vector classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.8, kernel=&#x27;linear&#x27;)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.8, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.8, kernel='linear')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "sv = SVC(kernel='linear' , C=0.8)\n",
    "sv.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8026666666666666"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv_pre = sv.predict(x_test)\n",
    "accuracy_score(y_test , sv_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "#multi class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ترکیبی از مدل ها "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# وقتی دادمون خیلی زیاد باشه برای خطر اورفیتینگ از این روش ها استفاده میکنیم\n",
    "# در واقع مدلمونو به صورت موازی پیش میبریم بار اجرایی رو روی یک الگوریتم کم میکنیم"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Yalda\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\ensemble\\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(base_estimator=DecisionTreeClassifier(criterion=&#x27;entropy&#x27;,\n",
       "                                                        max_depth=15,\n",
       "                                                        min_samples_split=3,\n",
       "                                                        random_state=42),\n",
       "                  max_samples=0.4, n_estimators=50, random_state=42)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=DecisionTreeClassifier(criterion=&#x27;entropy&#x27;,\n",
       "                                                        max_depth=15,\n",
       "                                                        min_samples_split=3,\n",
       "                                                        random_state=42),\n",
       "                  max_samples=0.4, n_estimators=50, random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=15, min_samples_split=3,\n",
       "                       random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=15, min_samples_split=3,\n",
       "                       random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(criterion='entropy',\n",
       "                                                        max_depth=15,\n",
       "                                                        min_samples_split=3,\n",
       "                                                        random_state=42),\n",
       "                  max_samples=0.4, n_estimators=50, random_state=42)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "baggdt = BaggingClassifier(base_estimator= dt ,n_estimators=50 ,bootstrap=True , \n",
    "                            max_samples= 0.4 , max_features=1.0 ,random_state=42 )\n",
    "\n",
    "\n",
    "baggdt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.832"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ybagg = baggdt.predict(x_test)\n",
    "accuracy_score(y_test,ybagg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Yalda\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\ensemble\\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(algorithm=&#x27;SAMME&#x27;,\n",
       "                   base_estimator=DecisionTreeClassifier(criterion=&#x27;entropy&#x27;,\n",
       "                                                         max_depth=15,\n",
       "                                                         min_samples_split=3,\n",
       "                                                         random_state=42),\n",
       "                   learning_rate=1, random_state=42)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(algorithm=&#x27;SAMME&#x27;,\n",
       "                   base_estimator=DecisionTreeClassifier(criterion=&#x27;entropy&#x27;,\n",
       "                                                         max_depth=15,\n",
       "                                                         min_samples_split=3,\n",
       "                                                         random_state=42),\n",
       "                   learning_rate=1, random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=15, min_samples_split=3,\n",
       "                       random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=15, min_samples_split=3,\n",
       "                       random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME',\n",
       "                   base_estimator=DecisionTreeClassifier(criterion='entropy',\n",
       "                                                         max_depth=15,\n",
       "                                                         min_samples_split=3,\n",
       "                                                         random_state=42),\n",
       "                   learning_rate=1, random_state=42)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "adaboost = AdaBoostClassifier(base_estimator= dt , n_estimators=50 , learning_rate=1 , random_state=42 , algorithm='SAMME' )\n",
    "adaboost.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yadaboost = adaboost.predict(x_test)\n",
    "accuracy_score(y_test,yadaboost)"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}