Merge pull request 'lab 3 done' (#4) from lab3 into main

Reviewed-on: #4

lab_2/datasetlab2/Stores.csv

@@ -0,0 +1,897 @@
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lab_2/lab2.ipynb

@@ -0,0 +1,599 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Lab2 Pibd-31 Malafeev**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Загрузка трёх других датасетов(не своего варианта)*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(\".//datasetlab2//kc_house_data.csv\", sep=\",\")\n",
+    "df2 = pd.read_csv(\".//datasetlab2//Stores.csv\", sep=\",\")\n",
+    "df3 = pd.read_csv(\".//datasetlab2//Forbes Billionaires.csv\", sep=\",\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Далее будут выполнены в Markdown пукнты лабораторной 2-8 с пометкой каждого пункта."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2.Проблемной областью явлются: датасет stores.csv - магазины, kc_house_data.csv - датасет продажи домов и Forber Billionares.csv - датасет миллионеров."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3.Объектами наблюдениями явлются магазины, дома и миллионеры. Связи между объектами нет, единственная схожесть - магазин и дом являются зданиями."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4.Датасет миллионеров нужны например для сайта forbes - чтобы составить тир лист. В целом, другие датасеты тоже подходят для составления тир листа)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "5.Технический проект - тир лист, на входе датасет"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "6.Пункт будем выполнять в коде, оставлю к каждому комменты:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id               0\n",
+      "date             0\n",
+      "price            0\n",
+      "bedrooms         0\n",
+      "bathrooms        0\n",
+      "sqft_living      0\n",
+      "sqft_lot         0\n",
+      "floors           0\n",
+      "waterfront       0\n",
+      "view             0\n",
+      "condition        0\n",
+      "grade            0\n",
+      "sqft_above       0\n",
+      "sqft_basement    0\n",
+      "yr_built         0\n",
+      "yr_renovated     0\n",
+      "zipcode          0\n",
+      "lat              0\n",
+      "long             0\n",
+      "sqft_living15    0\n",
+      "sqft_lot15       0\n",
+      "dtype: int64\n",
+      "Store ID                0\n",
+      "Store_Area              0\n",
+      "Items_Available         0\n",
+      "Daily_Customer_Count    0\n",
+      "Store_Sales             0\n",
+      "dtype: int64\n",
+      "Rank        0\n",
+      "Name        0\n",
+      "Networth    0\n",
+      "Age         0\n",
+      "Country     0\n",
+      "Source      0\n",
+      "Industry    0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Проверка на пропущенные значения\n",
+    "print(df.isnull().sum())\n",
+    "print(df2.isnull().sum())\n",
+    "print(df3.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
+      "count  2.161300e+04  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
+      "mean   4.580302e+09  5.400881e+05      3.370842      2.114757   2079.899736   \n",
+      "std    2.876566e+09  3.671272e+05      0.930062      0.770163    918.440897   \n",
+      "min    1.000102e+06  7.500000e+04      0.000000      0.000000    290.000000   \n",
+      "25%    2.123049e+09  3.219500e+05      3.000000      1.750000   1427.000000   \n",
+      "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
+      "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
+      "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
+      "\n",
+      "           sqft_lot        floors    waterfront          view     condition  \\\n",
+      "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
+      "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
+      "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
+      "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
+      "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
+      "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
+      "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
+      "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
+      "\n",
+      "              grade    sqft_above  sqft_basement      yr_built  yr_renovated  \\\n",
+      "count  21613.000000  21613.000000   21613.000000  21613.000000  21613.000000   \n",
+      "mean       7.656873   1788.390691     291.509045   1971.005136     84.402258   \n",
+      "std        1.175459    828.090978     442.575043     29.373411    401.679240   \n",
+      "min        1.000000    290.000000       0.000000   1900.000000      0.000000   \n",
+      "25%        7.000000   1190.000000       0.000000   1951.000000      0.000000   \n",
+      "50%        7.000000   1560.000000       0.000000   1975.000000      0.000000   \n",
+      "75%        8.000000   2210.000000     560.000000   1997.000000      0.000000   \n",
+      "max       13.000000   9410.000000    4820.000000   2015.000000   2015.000000   \n",
+      "\n",
+      "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
+      "count  21613.000000  21613.000000  21613.000000   21613.000000   21613.000000  \n",
+      "mean   98077.939805     47.560053   -122.213896    1986.552492   12768.455652  \n",
+      "std       53.505026      0.138564      0.140828     685.391304   27304.179631  \n",
+      "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
+      "25%    98033.000000     47.471000   -122.328000    1490.000000    5100.000000  \n",
+      "50%    98065.000000     47.571800   -122.230000    1840.000000    7620.000000  \n",
+      "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
+      "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  \n",
+      "        Store ID    Store_Area  Items_Available  Daily_Customer_Count  \\\n",
+      "count  896.000000   896.000000       896.000000            896.000000   \n",
+      "mean   448.500000  1485.409598      1782.035714            786.350446   \n",
+      "std    258.797218   250.237011       299.872053            265.389281   \n",
+      "min      1.000000   775.000000       932.000000             10.000000   \n",
+      "25%    224.750000  1316.750000      1575.500000            600.000000   \n",
+      "50%    448.500000  1477.000000      1773.500000            780.000000   \n",
+      "75%    672.250000  1653.500000      1982.750000            970.000000   \n",
+      "max    896.000000  2229.000000      2667.000000           1560.000000   \n",
+      "\n",
+      "         Store_Sales  \n",
+      "count     896.000000  \n",
+      "mean    59351.305804  \n",
+      "std     17190.741895  \n",
+      "min     14920.000000  \n",
+      "25%     46530.000000  \n",
+      "50%     58605.000000  \n",
+      "75%     71872.500000  \n",
+      "max    116320.000000  \n",
+      "             Rank      Networth          Age\n",
+      "count  2600.000000  2600.000000  2600.000000\n",
+      "mean   1269.570769     4.860750    64.271923\n",
+      "std     728.146364    10.659671    13.220607\n",
+      "min       1.000000     1.000000    19.000000\n",
+      "25%     637.000000     1.500000    55.000000\n",
+      "50%    1292.000000     2.400000    64.000000\n",
+      "75%    1929.000000     4.500000    74.000000\n",
+      "max    2578.000000   219.000000   100.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Статистика по числовым данным для выявления аномальных распределений\n",
+    "print(df.describe())\n",
+    "print(df2.describe())\n",
+    "print(df3.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "7.По перым трём строкам кода, т.е после проверки на пропущенные значения выявлено, что их нет. А дальше я обнаружил аномалию: в датасете миллионеров есть столбец networth - чистое количество денег во всех формах ( в миллиардах ), в этом солбце минимальное значение является единицей, медиана в районе 2.4, а максимальное - 219. В ЭТОМ СТОЛБЦЕ АНОМАЛИЯ"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "8.Наши датасеты довольно информационные. Например у миллионер датасета можно посмотреть фио, сколько денег, что он сделал. Датасет по продаже домов гораздо информационнее, является лидером по наполненности и соответствует реальности. А вот датасет магазинов слабоват, можно например добавить: количество филлиалов, работников, прибыль"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "9.Возьмём датасет магазинов, будем удалять столбцы, где площадь ниже 1500 (по тз надо)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Store ID   Store_Area  Items_Available  Daily_Customer_Count  Store_Sales\n",
+      "0            1        1659             1961                   530        66490\n",
+      "4            5        1770             2111                   450        46620\n",
+      "6            7        1542             1858                  1030        72240\n",
+      "11          12        1751             2098                   720        57620\n",
+      "12          13        1746             2064                  1050        60470\n",
+      "..         ...         ...              ...                   ...          ...\n",
+      "882        883        1819             2187                   590        47920\n",
+      "886        887        1655             1986                  1150        77430\n",
+      "889        890        1539             1829                   650        46580\n",
+      "890        891        1549             1851                  1220        70620\n",
+      "891        892        1582             1910                  1080        66390\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df2_filtered = df2[df2['Store_Area'] >= 1500]\n",
+    "print(df2_filtered)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Теперь в датасете магазнов price заменим у всех на константное значение - 1 500 000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               id             date    price  bedrooms  bathrooms  sqft_living  \\\n",
+      "0      7129300520  20141013T000000  1500000         3       1.00         1180   \n",
+      "1      6414100192  20141209T000000  1500000         3       2.25         2570   \n",
+      "2      5631500400  20150225T000000  1500000         2       1.00          770   \n",
+      "3      2487200875  20141209T000000  1500000         4       3.00         1960   \n",
+      "4      1954400510  20150218T000000  1500000         3       2.00         1680   \n",
+      "...           ...              ...      ...       ...        ...          ...   \n",
+      "21608   263000018  20140521T000000  1500000         3       2.50         1530   \n",
+      "21609  6600060120  20150223T000000  1500000         4       2.50         2310   \n",
+      "21610  1523300141  20140623T000000  1500000         2       0.75         1020   \n",
+      "21611   291310100  20150116T000000  1500000         3       2.50         1600   \n",
+      "21612  1523300157  20141015T000000  1500000         2       0.75         1020   \n",
+      "\n",
+      "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
+      "0          5650     1.0           0     0  ...      7        1180   \n",
+      "1          7242     2.0           0     0  ...      7        2170   \n",
+      "2         10000     1.0           0     0  ...      6         770   \n",
+      "3          5000     1.0           0     0  ...      7        1050   \n",
+      "4          8080     1.0           0     0  ...      8        1680   \n",
+      "...         ...     ...         ...   ...  ...    ...         ...   \n",
+      "21608      1131     3.0           0     0  ...      8        1530   \n",
+      "21609      5813     2.0           0     0  ...      8        2310   \n",
+      "21610      1350     2.0           0     0  ...      7        1020   \n",
+      "21611      2388     2.0           0     0  ...      8        1600   \n",
+      "21612      1076     2.0           0     0  ...      7        1020   \n",
+      "\n",
+      "       sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+      "0                  0      1955             0    98178  47.5112 -122.257   \n",
+      "1                400      1951          1991    98125  47.7210 -122.319   \n",
+      "2                  0      1933             0    98028  47.7379 -122.233   \n",
+      "3                910      1965             0    98136  47.5208 -122.393   \n",
+      "4                  0      1987             0    98074  47.6168 -122.045   \n",
+      "...              ...       ...           ...      ...      ...      ...   \n",
+      "21608              0      2009             0    98103  47.6993 -122.346   \n",
+      "21609              0      2014             0    98146  47.5107 -122.362   \n",
+      "21610              0      2009             0    98144  47.5944 -122.299   \n",
+      "21611              0      2004             0    98027  47.5345 -122.069   \n",
+      "21612              0      2008             0    98144  47.5941 -122.299   \n",
+      "\n",
+      "       sqft_living15  sqft_lot15  \n",
+      "0               1340        5650  \n",
+      "1               1690        7639  \n",
+      "2               2720        8062  \n",
+      "3               1360        5000  \n",
+      "4               1800        7503  \n",
+      "...              ...         ...  \n",
+      "21608           1530        1509  \n",
+      "21609           1830        7200  \n",
+      "21610           1020        2007  \n",
+      "21611           1410        1287  \n",
+      "21612           1020        1357  \n",
+      "\n",
+      "[21613 rows x 21 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['price'] = 1500000\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Теперь у миллионеров в networth подставим среднее по  столбцу:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Networth\n",
+      "0      4.86075\n",
+      "1      4.86075\n",
+      "2      4.86075\n",
+      "3      4.86075\n",
+      "4      4.86075\n",
+      "...        ...\n",
+      "2595   4.86075\n",
+      "2596   4.86075\n",
+      "2597   4.86075\n",
+      "2598   4.86075\n",
+      "2599   4.86075\n",
+      "\n",
+      "[2600 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "networth_mean = df3['Networth'].mean()\n",
+    "df3['Networth'] = networth_mean\n",
+    "print(df3[['Networth']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "10.КОД"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train df: (15129, 21), Validation df: (3242, 21), Test df: (3242, 21)\n",
+      "Train df2: (627, 5), Validation df2: (134, 5), Test df2: (135, 5)\n",
+      "Train df3: (1820, 7), Validation df3: (390, 7), Test df3: (390, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "train_df, temp_df = train_test_split(df, test_size=0.3, random_state=42)\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42) \n",
+    "\n",
+    "train_df2, temp_df2 = train_test_split(df2, test_size=0.3, random_state=42)\n",
+    "val_df2, test_df2 = train_test_split(temp_df2, test_size=0.5, random_state=42)\n",
+    "\n",
+    "train_df3, temp_df3 = train_test_split(df3, test_size=0.3, random_state=42)\n",
+    "val_df3, test_df3 = train_test_split(temp_df3, test_size=0.5, random_state=42)\n",
+    "print(f\"Train df: {train_df.shape}, Validation df: {val_df.shape}, Test df: {test_df.shape}\")\n",
+    "print(f\"Train df2: {train_df2.shape}, Validation df2: {val_df2.shape}, Test df2: {test_df2.shape}\")\n",
+    "print(f\"Train df3: {train_df3.shape}, Validation df3: {val_df3.shape}, Test df3: {test_df3.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Было сделаное разбиение на три выборки: 70%, 15% и 15%. Подключена была библиотека scikit-learn и функция train_test_split , как сказано в пункте 15. Вполне сбалансированные"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "12.Качаем библиотеку imbalanced-learn, достаём нужные функции и погнали"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution after oversampling (df):\n",
+      "price_category\n",
+      "Low       10787\n",
+      "Medium    10787\n",
+      "High      10787\n",
+      "Luxury    10787\n",
+      "Name: count, dtype: int64\n",
+      "Class distribution after undersampling (df):\n",
+      "price_category\n",
+      "Low       1465\n",
+      "Medium    1465\n",
+      "High      1465\n",
+      "Luxury    1465\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "df = pd.read_csv(\".//datasetlab2//kc_house_data.csv\", sep=\",\")\n",
+    "df['price_category'] = pd.cut(df['price'], bins=[0, 300000, 600000, 1000000, float('inf')],\n",
+    "                              labels=['Low', 'Medium', 'High', 'Luxury'])\n",
+    "\n",
+    "y = df['price_category']\n",
+    "X = df.drop(columns=['price', 'price_category'])\n",
+    "\n",
+    "oversampler = RandomOverSampler(random_state=42)\n",
+    "X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
+    "\n",
+    "undersampler = RandomUnderSampler(random_state=42)\n",
+    "X_resampled_under, y_resampled_under = undersampler.fit_resample(X, y)\n",
+    "\n",
+    "print(\"Class distribution after oversampling (df):\")\n",
+    "print(pd.Series(y_resampled).value_counts())\n",
+    "\n",
+    "print(\"Class distribution after undersampling (df):\")\n",
+    "print(pd.Series(y_resampled_under).value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution after oversampling (df3):\n",
+      "AGE_category\n",
+      "Young          1401\n",
+      "Middle-aged    1401\n",
+      "Senior         1401\n",
+      "Elderly        1401\n",
+      "Name: count, dtype: int64\n",
+      "Class distribution after undersampling (df3):\n",
+      "AGE_category\n",
+      "Young          15\n",
+      "Middle-aged    15\n",
+      "Senior         15\n",
+      "Elderly        15\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df3 = pd.read_csv(\".//datasetlab2//Forbes Billionaires.csv\", sep=\",\")\n",
+    "\n",
+    "df3['AGE_category'] = pd.cut(df3['Age'], bins=[0, 30, 50, 70, float('inf')],\n",
+    "                              labels=['Young', 'Middle-aged', 'Senior', 'Elderly'])\n",
+    "\n",
+    "y3 = df3['AGE_category']\n",
+    "X3 = df3.drop(columns=['Age', 'AGE_category'])\n",
+    "\n",
+    "oversampler3 = RandomOverSampler(random_state=42)\n",
+    "X_resampled_3, y_resampled_3 = oversampler3.fit_resample(X3, y3)\n",
+    "\n",
+    "undersampler3 = RandomUnderSampler(random_state=42)\n",
+    "X_resampled_3_under, y_resampled_3_under = undersampler3.fit_resample(X3, y3)\n",
+    "\n",
+    "print(\"Class distribution after oversampling (df3):\")\n",
+    "print(pd.Series(y_resampled_3).value_counts())\n",
+    "\n",
+    "print(\"Class distribution after undersampling (df3):\")\n",
+    "print(pd.Series(y_resampled_3_under).value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution after oversampling (df2):\n",
+      "Sales_category\n",
+      "Low       598\n",
+      "Medium    598\n",
+      "High      598\n",
+      "Luxury      0\n",
+      "Name: count, dtype: int64\n",
+      "Class distribution after undersampling (df2):\n",
+      "Sales_category\n",
+      "Low       7\n",
+      "Medium    7\n",
+      "High      7\n",
+      "Luxury    0\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df2 = pd.read_csv(\".//datasetlab2//Stores.csv\", sep=\",\")\n",
+    "\n",
+    "df2['Sales_category'] = pd.cut(df2['Store_Sales'], bins=[0, 50000, 100000, 200000, float('inf')],\n",
+    "                                labels=['Low', 'Medium', 'High', 'Luxury'])\n",
+    "\n",
+    "y2 = df2['Sales_category']\n",
+    "X2 = df2.drop(columns=['Store_Sales', 'Sales_category'])\n",
+    "\n",
+    "oversampler2 = RandomOverSampler(random_state=42)\n",
+    "X_resampled_2, y_resampled_2 = oversampler2.fit_resample(X2, y2)\n",
+    "\n",
+    "undersampler2 = RandomUnderSampler(random_state=42)\n",
+    "X_resampled_2_under, y_resampled_2_under = undersampler2.fit_resample(X2, y2)\n",
+    "\n",
+    "print(\"Class distribution after oversampling (df2):\")\n",
+    "print(pd.Series(y_resampled_2).value_counts())\n",
+    "\n",
+    "print(\"Class distribution after undersampling (df2):\")\n",
+    "print(pd.Series(y_resampled_2_under).value_counts())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "miivenv",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}