ex03_학생데이터_중퇴예측_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "145d6ce7-55d4-4abb-9509-cfd99cdf0811",
   "metadata": {},
   "source": [
    "#### 1. 학업에 중요한 영향을 미치는 요인 파악 -> 학업 중퇴 및 실패 줄이기\n",
    "#### 2. 개인별 학업 진행상황 파악 -> 성취도 높이기 위한 교육 및 활동 설계!\n",
    "#### 3. 인구통계정보를 기반으로 특정 지역이나 사회계층적으로 고등교육에 접근하기 어려운 사람들을 위한 방법 설계!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f4d5eee-fc82-412e-90d6-369cf1d15894",
   "metadata": {},
   "source": [
    "##### 가설1) 부모님의 학력에 따른 중퇴여부, 상관관계\n",
    "##### 가설2) 물가상승률과 실업률이 성적(1,2학기)에 영향을 줄까?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c0db8736-0101-4514-8986-cfbe782b1a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "a245dd52-79f2-404c-af9f-2aaa706ce48e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital status</th>\n",
       "      <th>Application mode</th>\n",
       "      <th>Application order</th>\n",
       "      <th>Course</th>\n",
       "      <th>Previous qualification</th>\n",
       "      <th>Previous qualification (grade)</th>\n",
       "      <th>Nacionality</th>\n",
       "      <th>Mother's qualification</th>\n",
       "      <th>Father's qualification</th>\n",
       "      <th>Mother's occupation</th>\n",
       "      <th>...</th>\n",
       "      <th>Curricular units 2nd sem (enrolled)</th>\n",
       "      <th>Curricular units 2nd sem (evaluations)</th>\n",
       "      <th>Curricular units 2nd sem (approved)</th>\n",
       "      <th>Curricular units 2nd sem (grade)</th>\n",
       "      <th>Curricular units 2nd sem (without evaluations)</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Inflation rate</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Target</th>\n",
       "      <th>Daytime/evening attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>Animation and Multimedia Design</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>Basic Education 3rd Cycle (9th/10th/11th Year)...</td>\n",
       "      <td>Other - 11th Year of Schooling</td>\n",
       "      <td>Personal Services, Security and Safety Workers...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.74</td>\n",
       "      <td>Dropout</td>\n",
       "      <td>daytime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>single</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Tourism</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>160.0</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>Secondary Education - 12th Year of Schooling o...</td>\n",
       "      <td>Higher Education - Degree</td>\n",
       "      <td>Intermediate Level Technicians and Professions</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>13.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.79</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>daytime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Communication Design</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>Basic education 1st cycle (4th/5th year) or eq...</td>\n",
       "      <td>Basic education 1st cycle (4th/5th year) or eq...</td>\n",
       "      <td>Unskilled Workers</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.74</td>\n",
       "      <td>Dropout</td>\n",
       "      <td>daytime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>Journalism and Communication</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>Basic Education 2nd Cycle (6th/7th/8th Year) o...</td>\n",
       "      <td>Basic education 1st cycle (4th/5th year) or eq...</td>\n",
       "      <td>Personal Services, Security and Safety Workers...</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>daytime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>married</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>Social Service (evening attendance)</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>Basic education 1st cycle (4th/5th year) or eq...</td>\n",
       "      <td>Basic Education 2nd Cycle (6th/7th/8th Year) o...</td>\n",
       "      <td>Unskilled Workers</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.79</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>evening</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4419</th>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Journalism and Communication</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>125.0</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>Secondary Education - 12th Year of Schooling o...</td>\n",
       "      <td>Secondary Education - 12th Year of Schooling o...</td>\n",
       "      <td>Personal Services, Security and Safety Workers...</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>daytime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4420</th>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Journalism and Communication</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Russian</td>\n",
       "      <td>Secondary Education - 12th Year of Schooling o...</td>\n",
       "      <td>Secondary Education - 12th Year of Schooling o...</td>\n",
       "      <td>Unskilled Workers</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.02</td>\n",
       "      <td>Dropout</td>\n",
       "      <td>daytime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4421</th>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nursing</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>154.0</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>Basic education 1st cycle (4th/5th year) or eq...</td>\n",
       "      <td>Basic education 1st cycle (4th/5th year) or eq...</td>\n",
       "      <td>Unskilled Workers</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.79</td>\n",
       "      <td>Dropout</td>\n",
       "      <td>daytime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4422</th>\n",
       "      <td>single</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Management</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Portuguese</td>\n",
       "      <td>Basic education 1st cycle (4th/5th year) or eq...</td>\n",
       "      <td>Basic education 1st cycle (4th/5th year) or eq...</td>\n",
       "      <td>Skilled Workers in Industry, Construction and ...</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>daytime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4423</th>\n",
       "      <td>single</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Journalism and Communication</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>152.0</td>\n",
       "      <td>Cape Verdean</td>\n",
       "      <td>Basic Education 2nd Cycle (6th/7th/8th Year) o...</td>\n",
       "      <td>Basic education 1st cycle (4th/5th year) or eq...</td>\n",
       "      <td>Personal Services, Security and Safety Workers...</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>daytime</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4424 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Marital status  Application mode  Application order  \\\n",
       "0            single                17                  5   \n",
       "1            single                15                  1   \n",
       "2            single                 1                  5   \n",
       "3            single                17                  2   \n",
       "4           married                39                  1   \n",
       "...             ...               ...                ...   \n",
       "4419         single                 1                  6   \n",
       "4420         single                 1                  2   \n",
       "4421         single                 1                  1   \n",
       "4422         single                 1                  1   \n",
       "4423         single                10                  1   \n",
       "\n",
       "                                   Course Previous qualification  \\\n",
       "0         Animation and Multimedia Design    Secondary education   \n",
       "1                                 Tourism    Secondary education   \n",
       "2                    Communication Design    Secondary education   \n",
       "3            Journalism and Communication    Secondary education   \n",
       "4     Social Service (evening attendance)    Secondary education   \n",
       "...                                   ...                    ...   \n",
       "4419         Journalism and Communication    Secondary education   \n",
       "4420         Journalism and Communication    Secondary education   \n",
       "4421                              Nursing    Secondary education   \n",
       "4422                           Management    Secondary education   \n",
       "4423         Journalism and Communication    Secondary education   \n",
       "\n",
       "      Previous qualification (grade)   Nacionality  \\\n",
       "0                              122.0    Portuguese   \n",
       "1                              160.0    Portuguese   \n",
       "2                              122.0    Portuguese   \n",
       "3                              122.0    Portuguese   \n",
       "4                              100.0    Portuguese   \n",
       "...                              ...           ...   \n",
       "4419                           125.0    Portuguese   \n",
       "4420                           120.0       Russian   \n",
       "4421                           154.0    Portuguese   \n",
       "4422                           180.0    Portuguese   \n",
       "4423                           152.0  Cape Verdean   \n",
       "\n",
       "                                 Mother's qualification  \\\n",
       "0     Basic Education 3rd Cycle (9th/10th/11th Year)...   \n",
       "1     Secondary Education - 12th Year of Schooling o...   \n",
       "2     Basic education 1st cycle (4th/5th year) or eq...   \n",
       "3     Basic Education 2nd Cycle (6th/7th/8th Year) o...   \n",
       "4     Basic education 1st cycle (4th/5th year) or eq...   \n",
       "...                                                 ...   \n",
       "4419  Secondary Education - 12th Year of Schooling o...   \n",
       "4420  Secondary Education - 12th Year of Schooling o...   \n",
       "4421  Basic education 1st cycle (4th/5th year) or eq...   \n",
       "4422  Basic education 1st cycle (4th/5th year) or eq...   \n",
       "4423  Basic Education 2nd Cycle (6th/7th/8th Year) o...   \n",
       "\n",
       "                                 Father's qualification  \\\n",
       "0                        Other - 11th Year of Schooling   \n",
       "1                             Higher Education - Degree   \n",
       "2     Basic education 1st cycle (4th/5th year) or eq...   \n",
       "3     Basic education 1st cycle (4th/5th year) or eq...   \n",
       "4     Basic Education 2nd Cycle (6th/7th/8th Year) o...   \n",
       "...                                                 ...   \n",
       "4419  Secondary Education - 12th Year of Schooling o...   \n",
       "4420  Secondary Education - 12th Year of Schooling o...   \n",
       "4421  Basic education 1st cycle (4th/5th year) or eq...   \n",
       "4422  Basic education 1st cycle (4th/5th year) or eq...   \n",
       "4423  Basic education 1st cycle (4th/5th year) or eq...   \n",
       "\n",
       "                                    Mother's occupation  ...  \\\n",
       "0     Personal Services, Security and Safety Workers...  ...   \n",
       "1        Intermediate Level Technicians and Professions  ...   \n",
       "2                                     Unskilled Workers  ...   \n",
       "3     Personal Services, Security and Safety Workers...  ...   \n",
       "4                                     Unskilled Workers  ...   \n",
       "...                                                 ...  ...   \n",
       "4419  Personal Services, Security and Safety Workers...  ...   \n",
       "4420                                  Unskilled Workers  ...   \n",
       "4421                                  Unskilled Workers  ...   \n",
       "4422  Skilled Workers in Industry, Construction and ...  ...   \n",
       "4423  Personal Services, Security and Safety Workers...  ...   \n",
       "\n",
       "     Curricular units 2nd sem (enrolled)  \\\n",
       "0                                      0   \n",
       "1                                      6   \n",
       "2                                      6   \n",
       "3                                      6   \n",
       "4                                      6   \n",
       "...                                  ...   \n",
       "4419                                   6   \n",
       "4420                                   6   \n",
       "4421                                   8   \n",
       "4422                                   5   \n",
       "4423                                   6   \n",
       "\n",
       "      Curricular units 2nd sem (evaluations)  \\\n",
       "0                                          0   \n",
       "1                                          6   \n",
       "2                                          0   \n",
       "3                                         10   \n",
       "4                                          6   \n",
       "...                                      ...   \n",
       "4419                                       8   \n",
       "4420                                       6   \n",
       "4421                                       9   \n",
       "4422                                       6   \n",
       "4423                                       6   \n",
       "\n",
       "     Curricular units 2nd sem (approved) Curricular units 2nd sem (grade)  \\\n",
       "0                                      0                         0.000000   \n",
       "1                                      6                        13.666667   \n",
       "2                                      0                         0.000000   \n",
       "3                                      5                        12.400000   \n",
       "4                                      6                        13.000000   \n",
       "...                                  ...                              ...   \n",
       "4419                                   5                        12.666667   \n",
       "4420                                   2                        11.000000   \n",
       "4421                                   1                        13.500000   \n",
       "4422                                   5                        12.000000   \n",
       "4423                                   6                        13.000000   \n",
       "\n",
       "     Curricular units 2nd sem (without evaluations) Unemployment rate  \\\n",
       "0                                                 0              10.8   \n",
       "1                                                 0              13.9   \n",
       "2                                                 0              10.8   \n",
       "3                                                 0               9.4   \n",
       "4                                                 0              13.9   \n",
       "...                                             ...               ...   \n",
       "4419                                              0              15.5   \n",
       "4420                                              0              11.1   \n",
       "4421                                              0              13.9   \n",
       "4422                                              0               9.4   \n",
       "4423                                              0              12.7   \n",
       "\n",
       "     Inflation rate   GDP    Target Daytime/evening attendance  \n",
       "0               1.4  1.74   Dropout                    daytime  \n",
       "1              -0.3  0.79  Graduate                    daytime  \n",
       "2               1.4  1.74   Dropout                    daytime  \n",
       "3              -0.8 -3.12  Graduate                    daytime  \n",
       "4              -0.3  0.79  Graduate                    evening  \n",
       "...             ...   ...       ...                        ...  \n",
       "4419            2.8 -4.06  Graduate                    daytime  \n",
       "4420            0.6  2.02   Dropout                    daytime  \n",
       "4421           -0.3  0.79   Dropout                    daytime  \n",
       "4422           -0.8 -3.12  Graduate                    daytime  \n",
       "4423            3.7 -1.70  Graduate                    daytime  \n",
       "\n",
       "[4424 rows x 37 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/data_students.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "60fc5c15-74d3-4b0f-bc42-6205526985b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4424 entries, 0 to 4423\n",
      "Data columns (total 37 columns):\n",
      " #   Column                                          Non-Null Count  Dtype  \n",
      "---  ------                                          --------------  -----  \n",
      " 0   Marital status                                  4424 non-null   object \n",
      " 1   Application mode                                4424 non-null   int64  \n",
      " 2   Application order                               4424 non-null   int64  \n",
      " 3   Course                                          4424 non-null   object \n",
      " 4   Previous qualification                          4424 non-null   object \n",
      " 5   Previous qualification (grade)                  4424 non-null   float64\n",
      " 6   Nacionality                                     4424 non-null   object \n",
      " 7   Mother's qualification                          4424 non-null   object \n",
      " 8   Father's qualification                          4424 non-null   object \n",
      " 9   Mother's occupation                             4424 non-null   object \n",
      " 10  Father's occupation                             4424 non-null   object \n",
      " 11  Admission grade                                 4424 non-null   float64\n",
      " 12  Displaced                                       4424 non-null   object \n",
      " 13  Educational special needs                       4424 non-null   object \n",
      " 14  Debtor                                          4424 non-null   object \n",
      " 15  Tuition fees up to date                         4424 non-null   object \n",
      " 16  Gender                                          4424 non-null   object \n",
      " 17  Scholarship holder                              4424 non-null   object \n",
      " 18  Age at enrollment                               4424 non-null   int64  \n",
      " 19  International                                   4424 non-null   object \n",
      " 20  Curricular units 1st sem (credited)             4424 non-null   int64  \n",
      " 21  Curricular units 1st sem (enrolled)             4424 non-null   int64  \n",
      " 22  Curricular units 1st sem (evaluations)          4424 non-null   int64  \n",
      " 23  Curricular units 1st sem (approved)             4424 non-null   int64  \n",
      " 24  Curricular units 1st sem (grade)                4424 non-null   float64\n",
      " 25  Curricular units 1st sem (without evaluations)  4424 non-null   int64  \n",
      " 26  Curricular units 2nd sem (credited)             4424 non-null   int64  \n",
      " 27  Curricular units 2nd sem (enrolled)             4424 non-null   int64  \n",
      " 28  Curricular units 2nd sem (evaluations)          4424 non-null   int64  \n",
      " 29  Curricular units 2nd sem (approved)             4424 non-null   int64  \n",
      " 30  Curricular units 2nd sem (grade)                4424 non-null   float64\n",
      " 31  Curricular units 2nd sem (without evaluations)  4424 non-null   int64  \n",
      " 32  Unemployment rate                               4424 non-null   float64\n",
      " 33  Inflation rate                                  4424 non-null   float64\n",
      " 34  GDP                                             4424 non-null   float64\n",
      " 35  Target                                          4424 non-null   object \n",
      " 36  Daytime/evening attendance                      4424 non-null   object \n",
      "dtypes: float64(7), int64(13), object(17)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "b0edc1f1-e145-4f75-97b5-cfc040689c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['grade']=(data[\"Curricular units 1st sem (grade)\"]+data[\"Curricular units 2nd sem (grade)\"])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "11b42abc-c725-42b1-99c9-15b0b809fc4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.000000\n",
       "1       14.000000\n",
       "2        0.000000\n",
       "3       13.428571\n",
       "4       12.333333\n",
       "          ...    \n",
       "4419    13.600000\n",
       "4420    12.000000\n",
       "4421    14.912500\n",
       "4422    13.800000\n",
       "4423    11.666667\n",
       "Name: Curricular units 1st sem (grade), Length: 4424, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Curricular units 1st sem (grade)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "d298a824-9a06-4d21-bbf1-4da0b9d35da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 경고창 warnings 제거 코드\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "969bce3a-1c86-4950-8045-9550d4adbd76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Unskilled Workers',\n",
       "       'Intermediate Level Technicians and Professions',\n",
       "       'Skilled Workers in Industry, Construction and Craftsmen',\n",
       "       'Armed Forces Professions',\n",
       "       'Personal Services, Security and Safety Workers and Sellers',\n",
       "       'Installation and Machine Operators and Assembly Workers',\n",
       "       'Administrative staff',\n",
       "       ' Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers',\n",
       "       'Specialists in Intellectual and Scientific Activities ',\n",
       "       'Specialists in information and communication technologies (ICT)',\n",
       "       'Farmers and Skilled Workers in Agriculture, Fisheries and Forestry',\n",
       "       'Student', 'Other Situation',\n",
       "       'Workers in food processing, woodworking, clothing and other industries and crafts',\n",
       "       'Specialists in the physical sciences, mathematics, engineering and related techniques',\n",
       "       '(blank)', 'Other administrative support staff',\n",
       "       'Street vendors (except food) and street service providers',\n",
       "       'Unskilled workers in agriculture, animal production, fisheries and forestry',\n",
       "       'Market-oriented farmers and skilled agricultural and animal production workers',\n",
       "       'Unskilled workers in extractive industry, construction, manufacturing and transport',\n",
       "       'personal service workers', 'assembly workers.',\n",
       "       'Technicians and professionals, of intermediate level of health',\n",
       "       'Intermediate level science and engineering technicians and professions',\n",
       "       'Meal preparation assistants',\n",
       "       'Farmers, livestock keepers, fishermen, hunters and gatherers, subsistence',\n",
       "       'Information and communication technology technicians',\n",
       "       'Data, accounting, statistical, financial services and registry-related operators',\n",
       "       'Skilled construction workers and the like, except electricians',\n",
       "       'Other Armed Forces personnel',\n",
       "       'Skilled workers in metallurgy, metalworking and similar',\n",
       "       'sellers', 'Vehicle drivers and mobile equipment operators',\n",
       "       'Health professionals', 'Armed Forces Sergeants',\n",
       "       'Fixed plant and machine operators',\n",
       "       'Intermediate level technicians from legal, social, sports, cultural and similar services',\n",
       "       'teachers', 'Directors of administrative and commercial services',\n",
       "       'Personal care workers and the like',\n",
       "       'Skilled workers in electricity and electronics',\n",
       "       'Office workers, secretaries in general and data processing operators',\n",
       "       'Hotel, catering, trade and other services directors',\n",
       "       'Armed Forces Officers',\n",
       "       'Protection and security services personnel'], dtype=object)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Father's occupation\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "0ed5b75a-dade-468e-9106-4e0c743d3563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       0\n",
       "       ..\n",
       "4419    1\n",
       "4420    1\n",
       "4421    1\n",
       "4422    1\n",
       "4423    1\n",
       "Name: Daytime/evening attendance, Length: 4424, dtype: int32"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data['Daytime/evening attendance']=np.where(data['Daytime/evening attendance']==\"daytime\",1,0)\n",
    "data['Daytime/evening attendance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "40246f40-1b5c-474e-8f47-a47cb59c242c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Daytime/evening attendance\n",
       "0     9.617258\n",
       "1    10.535797\n",
       "Name: grade, dtype: float64"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Daytime/evening attendance')['grade'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "ad96610b-bc09-4943-9d71-842d63c41f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Daytime/evening attendance\n",
       "0    0.428571\n",
       "1    0.308044\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Target']=np.where(data['Target']=='Dropout',1,0)\n",
    "data.groupby('Daytime/evening attendance')['Target'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "17b08dc0-8ecd-4df0-97f4-b2102f1e620e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Admission grade\n",
       "95.0      7.136050\n",
       "95.1      0.000000\n",
       "95.5      6.500000\n",
       "95.8      0.000000\n",
       "96.0      5.144099\n",
       "           ...    \n",
       "180.4    12.733333\n",
       "183.5     6.000000\n",
       "184.0    16.793889\n",
       "184.4    14.666667\n",
       "190.0    13.200820\n",
       "Name: grade, Length: 620, dtype: float64"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"Admission grade\")['grade'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "44833c1e-651f-416d-bcfe-25778437139e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Course\n",
       "Advertising and Marketing Management    11.007110\n",
       "Agronomy                                 9.926741\n",
       "Animation and Multimedia Design          2.076352\n",
       "Basic Education                         11.018245\n",
       "Biofuel Production Technologies          9.317172\n",
       "Communication Design                    11.638760\n",
       "Equinculture                             9.884412\n",
       "Informatics Engineering                  8.676531\n",
       "Journalism and Communication            11.365190\n",
       "Management                               9.912321\n",
       "Management (evening attendance)          9.163434\n",
       "Nursing                                 12.409757\n",
       "Oral Hygiene                            10.050185\n",
       "Social Service                          11.176406\n",
       "Social Service (evening attendance)     10.182955\n",
       "Tourism                                 10.194371\n",
       "Veterinary Nursing                      11.510099\n",
       "Name: grade, dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"Course\")['grade'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "389aeef3-5986-44a7-9a66-03186b2d1cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x000001F7C16689D0>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Target')['Course']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "39848916-5ab6-4ef4-b102-53e22b6bba2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Basic Education 3rd Cycle (9th/10th/11th Year) or Equiv.',\n",
       "       'Secondary Education - 12th Year of Schooling or Eq.',\n",
       "       'Basic education 1st cycle (4th/5th year) or equiv.',\n",
       "       'Basic Education 2nd Cycle (6th/7th/8th Year) or Equiv.',\n",
       "       'Higher Education - Degree', \"Higher Education - Master's\",\n",
       "       'Professional higher technical course',\n",
       "       \"Higher Education - Bachelor's Degree\", 'Unknown',\n",
       "       'Other - 11th Year of Schooling',\n",
       "       'Higher education - degree (1st cycle)',\n",
       "       '12th Year of Schooling - Not Completed',\n",
       "       'Higher Education - Doctorate',\n",
       "       'Technological specialization course', '7th Year (Old)',\n",
       "       'Specialized higher studies course', '8th year of schooling',\n",
       "       '10th Year of Schooling', \"Can't read or write\",\n",
       "       'Can read without having a 4th year of schooling',\n",
       "       'Frequency of Higher Education',\n",
       "       '11th Year of Schooling - Not Completed',\n",
       "       '9th Year of Schooling - Not Completed',\n",
       "       'Higher Education - Master (2nd cycle)', 'General commerce course',\n",
       "       'Technical-professional course',\n",
       "       '2nd cycle of the general high school course',\n",
       "       '7th year of schooling',\n",
       "       'Higher Education - Doctorate (3rd cycle)'], dtype=object)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Mother's qualification\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "e72cc0e9-5736-40ee-8fba-3cad2a7c0212",
   "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>Target</th>\n",
       "      <th>grade</th>\n",
       "      <th>Scholarship holder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.550759</td>\n",
       "      <td>-0.245354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>-0.550759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scholarship holder</th>\n",
       "      <td>-0.245354</td>\n",
       "      <td>0.182591</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Target     grade  Scholarship holder\n",
       "Target              1.000000 -0.550759           -0.245354\n",
       "grade              -0.550759  1.000000            0.182591\n",
       "Scholarship holder -0.245354  0.182591            1.000000"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"Target\", 'grade','Scholarship holder']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "2eed1df6-fc91-42d3-b78f-263857767045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "4419    1\n",
       "4420    0\n",
       "4421    1\n",
       "4422    1\n",
       "4423    0\n",
       "Name: International, Length: 4424, dtype: int32"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['International']=np.where(data['International']=='yes',1,0)\n",
    "data['International']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "10a5551a-f136-4cf6-98fe-2c5f0033b8d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "4419    0\n",
       "4420    1\n",
       "4421    0\n",
       "4422    0\n",
       "4423    0\n",
       "Name: Debtor, Length: 4424, dtype: int32"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Debtor']=np.where(data['Debtor']=='yes',1,0)\n",
    "data['Debtor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "a437cf6e-b306-472e-bc78-0abd778753ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "4419    0\n",
       "4420    0\n",
       "4421    1\n",
       "4422    1\n",
       "4423    0\n",
       "Name: Scholarship holder, Length: 4424, dtype: int32"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Scholarship holder']=np.where(data['Scholarship holder']=='yes',1,0)\n",
    "data['Scholarship holder']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "459e663a-950b-42ee-a921-03d4f17006dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Admission grade\n",
       "95.0     0.636364\n",
       "95.1     1.000000\n",
       "95.5     0.500000\n",
       "95.8     1.000000\n",
       "96.0     0.714286\n",
       "           ...   \n",
       "180.4    1.000000\n",
       "183.5    1.000000\n",
       "184.0    0.000000\n",
       "184.4    0.000000\n",
       "190.0    0.333333\n",
       "Name: Target, Length: 620, dtype: float64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"Admission grade\")['Target'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "cd10846b-1968-4ce5-b068-99dad87f8542",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Previous qualification\n",
       "10th year of schooling                                      1.000000\n",
       "10th year of schooling - not completed                      0.500000\n",
       "11th year of schooling - not completed                      0.750000\n",
       "12th year of schooling - not completed                      1.000000\n",
       "Basic education 2nd cycle (6th/7th/8th year) or equiv.      0.428571\n",
       "Basic education 3rd cycle (9th/10th/11th year) or equiv.    0.641975\n",
       "Frequency of higher education                               0.437500\n",
       "Higher education - bachelor's degree                        0.695652\n",
       "Higher education - degree                                   0.595238\n",
       "Higher education - degree (1st cycle)                       0.350000\n",
       "Higher education - doctorate                                1.000000\n",
       "Higher education - master (2nd cycle)                       0.333333\n",
       "Higher education - master's                                 0.500000\n",
       "Other - 11th year of schooling                              0.577778\n",
       "Professional higher technical course                        0.166667\n",
       "Secondary education                                         0.290019\n",
       "Technological specialization course                         0.315068\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"Previous qualification\")['Target'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "ea81ece7-f8f2-4eb0-9936-22e5b4bae453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tuition fees up to date\n",
       "no     0.865530\n",
       "yes    0.247433\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"Tuition fees up to date\")['Target'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "8ff00b18-d637-48d1-9d1c-abaa9939bc68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nacionality\n",
       "Portuguese      505\n",
       "Brazilian         9\n",
       "Santomean         4\n",
       "Cape Verdean      4\n",
       "Spanish           2\n",
       "Guinean           2\n",
       "Lithuanian        1\n",
       "Russian           1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data[\"Tuition fees up to date\"]=='no']['Nacionality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "10225bf6-986e-4eba-b6cb-3261d7638135",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Previous qualification\n",
       "Secondary education                                         3717\n",
       "Technological specialization course                          219\n",
       "Basic education 3rd cycle (9th/10th/11th year) or equiv.     162\n",
       "Higher education - degree                                    126\n",
       "Other - 11th year of schooling                                45\n",
       "Higher education - degree (1st cycle)                         40\n",
       "Professional higher technical course                          36\n",
       "Higher education - bachelor's degree                          23\n",
       "Frequency of higher education                                 16\n",
       "12th year of schooling - not completed                        11\n",
       "Higher education - master's                                    8\n",
       "Basic education 2nd cycle (6th/7th/8th year) or equiv.         7\n",
       "Higher education - master (2nd cycle)                          6\n",
       "11th year of schooling - not completed                         4\n",
       "10th year of schooling - not completed                         2\n",
       "Higher education - doctorate                                   1\n",
       "10th year of schooling                                         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Previous qualification'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "6a9f198c-523d-459f-aa33-06fca7056bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.boxplot(x='Target', y='Nacionality', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "da48cc0d-1f9b-4e3d-8191-0d1358911bb4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 학비 납부 상태와 타겟 간의 히트맵\n",
    "heatmap_data = data[['Inflation rate', 'Unemployment rate', 'Target']].corr()\n",
    "\n",
    "sns.heatmap(heatmap_data, annot=True)\n",
    "plt.title('Heatmap of Tuition Fees Up-to-date vs Target')\n",
    "plt.ylabel('Tuition Fees Up-to-date')\n",
    "plt.xlabel('Target')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "e063bcc6-2cf1-48ec-873d-cee9d34318fc",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "heatmap() takes 1 positional argument but 3 positional arguments (and 1 keyword-only argument) were given",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[311], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# 성적과 타겟 간의 히트맵\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgrade\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTarget\u001b[39m\u001b[38;5;124m'\u001b[39m, data, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoolwarm\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGrade vs Target\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgrade\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mTypeError\u001b[0m: heatmap() takes 1 positional argument but 3 positional arguments (and 1 keyword-only argument) were given"
     ]
    }
   ],
   "source": [
    "# 성적과 타겟 간의 히트맵\n",
    "sns.heatmap('grade','Target', data, cmap='coolwarm')\n",
    "plt.title('Grade vs Target')\n",
    "plt.ylabel('grade')\n",
    "plt.xlabel('Target')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c691df6d-f8e9-4767-98e3-7de38ff4dcec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9abb92db-e969-43a3-86b7-07a660ff58a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}