G5_titanic-logistic-regression.ipynb

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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Los últimos 10 registros son:\n",
      "      Passengerid   Age      Fare  Sex  sibsp  zero  zero.1  zero.2  zero.3  \\\n",
      "1299         1300  28.0    7.7208    1      0     0       0       0       0   \n",
      "1300         1301   3.0   13.7750    1      1     0       0       0       0   \n",
      "1301         1302  28.0    7.7500    1      0     0       0       0       0   \n",
      "1302         1303  37.0   90.0000    1      1     0       0       0       0   \n",
      "1303         1304  28.0    7.7750    1      0     0       0       0       0   \n",
      "1304         1305  28.0    8.0500    0      0     0       0       0       0   \n",
      "1305         1306  39.0  108.9000    1      0     0       0       0       0   \n",
      "1306         1307  38.5    7.2500    0      0     0       0       0       0   \n",
      "1307         1308  28.0    8.0500    0      0     0       0       0       0   \n",
      "1308         1309  28.0   22.3583    0      1     0       0       0       0   \n",
      "\n",
      "      zero.4  ...  zero.12  zero.13  zero.14  Pclass  zero.15  zero.16  \\\n",
      "1299       0  ...        0        0        0       3        0        0   \n",
      "1300       0  ...        0        0        0       3        0        0   \n",
      "1301       0  ...        0        0        0       3        0        0   \n",
      "1302       0  ...        0        0        0       1        0        0   \n",
      "1303       0  ...        0        0        0       3        0        0   \n",
      "1304       0  ...        0        0        0       3        0        0   \n",
      "1305       0  ...        0        0        0       1        0        0   \n",
      "1306       0  ...        0        0        0       3        0        0   \n",
      "1307       0  ...        0        0        0       3        0        0   \n",
      "1308       0  ...        0        0        0       3        0        0   \n",
      "\n",
      "      Embarked  zero.17  zero.18  2urvived  \n",
      "1299       1.0        0        0         0  \n",
      "1300       2.0        0        0         0  \n",
      "1301       1.0        0        0         0  \n",
      "1302       1.0        0        0         0  \n",
      "1303       2.0        0        0         0  \n",
      "1304       2.0        0        0         0  \n",
      "1305       0.0        0        0         0  \n",
      "1306       2.0        0        0         0  \n",
      "1307       2.0        0        0         0  \n",
      "1308       0.0        0        0         0  \n",
      "\n",
      "[10 rows x 28 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"train_and_test2.csv\")\n",
    "total_registros= len(df)\n",
    "ultimos_10_registros=df.tail(10)\n",
    "print(\"Los últimos 10 registros son:\")\n",
    "print(ultimos_10_registros)\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "82bab229",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El data frame tiene un total de: 1309 registros\n"
     ]
    }
   ],
   "source": [
    "total_registros= len(df)\n",
    "print(\"El data frame tiene un total de:\", total_registros, \"registros\")"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "Passengerid    0\n",
       "Age            0\n",
       "Fare           0\n",
       "Sex            0\n",
       "sibsp          0\n",
       "zero           0\n",
       "zero.1         0\n",
       "zero.2         0\n",
       "zero.3         0\n",
       "zero.4         0\n",
       "zero.5         0\n",
       "zero.6         0\n",
       "Parch          0\n",
       "zero.7         0\n",
       "zero.8         0\n",
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       "zero.13        0\n",
       "zero.14        0\n",
       "Pclass         0\n",
       "zero.15        0\n",
       "zero.16        0\n",
       "Embarked       2\n",
       "zero.17        0\n",
       "zero.18        0\n",
       "2urvived       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
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   "source": [
    "df.fillna(method='ffill',inplace= True)"
   ]
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    {
     "data": {
      "text/plain": [
       "Passengerid    0\n",
       "Age            0\n",
       "Fare           0\n",
       "Sex            0\n",
       "sibsp          0\n",
       "zero           0\n",
       "zero.1         0\n",
       "zero.2         0\n",
       "zero.3         0\n",
       "zero.4         0\n",
       "zero.5         0\n",
       "zero.6         0\n",
       "Parch          0\n",
       "zero.7         0\n",
       "zero.8         0\n",
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       "zero.10        0\n",
       "zero.11        0\n",
       "zero.12        0\n",
       "zero.13        0\n",
       "zero.14        0\n",
       "Pclass         0\n",
       "zero.15        0\n",
       "zero.16        0\n",
       "Embarked       0\n",
       "zero.17        0\n",
       "zero.18        0\n",
       "2urvived       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
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   "source": [
    "def forward_selection(X_train, X_test, y_train, y_test, model):\n",
    "    selected_features = []\n",
    "    remaining_features = list(X_train.columns)\n",
    "    \n",
    "    while remaining_features:\n",
    "        best_feature = None\n",
    "        best_score = 0\n",
    "        \n",
    "        for feature in remaining_features:\n",
    "            temp_features = selected_features + [feature]\n",
    "            model.fit(X_train[temp_features], y_train)\n",
    "            score = model.score(X_test[temp_features], y_test)\n",
    "            \n",
    "            if score > best_score:\n",
    "                best_score = score\n",
    "                best_feature = feature\n",
    "        \n",
    "        if best_feature:\n",
    "            selected_features.append(best_feature)\n",
    "            remaining_features.remove(best_feature)\n",
    "        else:\n",
    "            break\n",
    "    #print(selected_features)\n",
    "    return selected_features"
   ]
  },
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   "execution_count": 12,
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   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd415efd",
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   "outputs": [],
   "source": [
    "#X = df[['Age','Fare','Sex','sibsp','Pclass','Embarked']]\n",
    "X = df.drop('2urvived',axis=1)\n",
    "y = df['2urvived']\n",
    "scaler = MinMaxScaler()\n",
    "scaled_x = scaler.fit_transform(X)\n",
    "scaled_df = pd.DataFrame(scaled_x, columns = X.columns)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_df, y, test_size=0.2, random_state=42)\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "selected_features_forward = forward_selection(X_train, X_test, y_train, y_test, model)"
   ]
  },
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   "outputs": [
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       "      <th>Pclass</th>\n",
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       "      <th>Embarked</th>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Passengerid       Age      Fare  Sex  sibsp  zero  zero.1  zero.2  \\\n",
       "772     0.590214  0.711888  0.020495  1.0  0.000   0.0     0.0     0.0   \n",
       "543     0.415138  0.398722  0.050749  0.0  0.125   0.0     0.0     0.0   \n",
       "289     0.220948  0.273456  0.015127  1.0  0.000   0.0     0.0     0.0   \n",
       "10      0.007645  0.047977  0.032596  1.0  0.125   0.0     0.0     0.0   \n",
       "147     0.112385  0.110610  0.067096  1.0  0.250   0.0     0.0     0.0   \n",
       "\n",
       "     zero.3  zero.4  ...  zero.11  zero.12  zero.13  zero.14  Pclass  zero.15  \\\n",
       "772     0.0     0.0  ...      0.0      0.0      0.0      0.0     0.5      0.0   \n",
       "543     0.0     0.0  ...      0.0      0.0      0.0      0.0     0.5      0.0   \n",
       "289     0.0     0.0  ...      0.0      0.0      0.0      0.0     1.0      0.0   \n",
       "10      0.0     0.0  ...      0.0      0.0      0.0      0.0     1.0      0.0   \n",
       "147     0.0     0.0  ...      0.0      0.0      0.0      0.0     1.0      0.0   \n",
       "\n",
       "     zero.16  Embarked  zero.17  zero.18  \n",
       "772      0.0       1.0      0.0      0.0  \n",
       "543      0.0       1.0      0.0      0.0  \n",
       "289      0.0       0.5      0.0      0.0  \n",
       "10       0.0       1.0      0.0      0.0  \n",
       "147      0.0       1.0      0.0      0.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b6b7a50d",
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    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:41.191014Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0e90df0",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
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       "      dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6647b54f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:41.339704Z",
     "iopub.status.busy": "2024-01-26T15:19:41.339281Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8a58da32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:41.369640Z",
     "iopub.status.busy": "2024-01-26T15:19:41.369179Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b19f42ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:41.401734Z",
     "iopub.status.busy": "2024-01-26T15:19:41.401335Z",
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    },
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     "end_time": "2024-01-26T15:19:41.737811",
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     "start_time": "2024-01-26T15:19:41.389271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b31e83a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:41.761622Z",
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     "status": "completed"
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d', cbar=False)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c771305",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:42.018822Z",
     "iopub.status.busy": "2024-01-26T15:19:42.017992Z",
     "iopub.status.idle": "2024-01-26T15:19:42.024011Z",
     "shell.execute_reply": "2024-01-26T15:19:42.022659Z"
    },
    "papermill": {
     "duration": 0.021281,
     "end_time": "2024-01-26T15:19:42.026733",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.005452",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1aef8e3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:42.050875Z",
     "iopub.status.busy": "2024-01-26T15:19:42.050402Z",
     "iopub.status.idle": "2024-01-26T15:19:42.066042Z",
     "shell.execute_reply": "2024-01-26T15:19:42.064851Z"
    },
    "papermill": {
     "duration": 0.030962,
     "end_time": "2024-01-26T15:19:42.068606",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.037644",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score : 0.8392857142857143\n",
      "Recall Score : 0.8392857142857143\n",
      "F1 Score : 0.8392857142857143\n"
     ]
    }
   ],
   "source": [
    "print(f'Precision Score : {precision_score(y_test,y_pred)}')\n",
    "print(f'Recall Score : {precision_score(y_test,y_pred)}')\n",
    "print(f'F1 Score : {precision_score(y_test,y_pred)}')      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f141f13d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:42.092668Z",
     "iopub.status.busy": "2024-01-26T15:19:42.092236Z",
     "iopub.status.idle": "2024-01-26T15:19:42.097558Z",
     "shell.execute_reply": "2024-01-26T15:19:42.096363Z"
    },
    "papermill": {
     "duration": 0.020448,
     "end_time": "2024-01-26T15:19:42.099937",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.079489",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f77b1cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:42.124725Z",
     "iopub.status.busy": "2024-01-26T15:19:42.123815Z",
     "iopub.status.idle": "2024-01-26T15:19:42.130469Z",
     "shell.execute_reply": "2024-01-26T15:19:42.129628Z"
    },
    "papermill": {
     "duration": 0.021722,
     "end_time": "2024-01-26T15:19:42.132605",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.110883",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7941107a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:42.156769Z",
     "iopub.status.busy": "2024-01-26T15:19:42.156312Z",
     "iopub.status.idle": "2024-01-26T15:19:42.166904Z",
     "shell.execute_reply": "2024-01-26T15:19:42.165671Z"
    },
    "papermill": {
     "duration": 0.025984,
     "end_time": "2024-01-26T15:19:42.169400",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.143416",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "auroc = roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2d52166c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-26T15:19:42.192821Z",
     "iopub.status.busy": "2024-01-26T15:19:42.192398Z",
     "iopub.status.idle": "2024-01-26T15:19:42.522212Z",
     "shell.execute_reply": "2024-01-26T15:19:42.521097Z"
    },
    "papermill": {
     "duration": 0.34516,
     "end_time": "2024-01-26T15:19:42.525295",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.180135",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr, tpr, color='blue', label='ROC curve (AUROC = %0.2f)' % auroc)\n",
    "plt.plot([0, 1], [0, 1], color='red', linestyle='--', label='Random Guess')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e5c79874",
   "metadata": {
    "papermill": {
     "duration": 0.010986,
     "end_time": "2024-01-26T15:19:42.547924",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.536938",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Métricas anteriores:\n",
      "Precision Score : 0.8392857142857143\n",
      "Recall Score : 0.6438356164383562\n",
      "F1 Score : 0.7286821705426357\n",
      "\n",
      "Métricas nuevas:\n",
      "Precision Score : 0.7591240875912408\n",
      "Recall Score : 0.5875706214689266\n",
      "F1 Score : 0.6624203821656052\n"
     ]
    }
   ],
   "source": [
    "#Luego de ejecutar todo el algoritmo, divida los datos de entrenamiento y prueba en 50% y 50%, \n",
    "#e indique si las métricas precision_score, recall_score, y  f1_score mejoraron. \n",
    "#Nota: En el código entregado debe estar como lo hizo o lo calculó.\n",
    "# Importar librerías\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# Dividir los datos en 50% para entrenamiento y 50% para prueba\n",
    "X_train_new, X_test_new, y_train_new, y_test_new = train_test_split(scaled_df, y, test_size=0.5, random_state=42)\n",
    "\n",
    "# Entrenar el modelo con los nuevos datos de entrenamiento\n",
    "model.fit(X_train_new, y_train_new)\n",
    "\n",
    "# Realizar predicciones en los nuevos datos de prueba\n",
    "y_pred_new = model.predict(X_test_new)\n",
    "\n",
    "# Calcular métricas con los nuevos datos\n",
    "precision_new = precision_score(y_test_new, y_pred_new)\n",
    "recall_new = recall_score(y_test_new, y_pred_new)\n",
    "f1_new = f1_score(y_test_new, y_pred_new)\n",
    "\n",
    "# Comparar métricas anteriores y nuevas\n",
    "print(\"Métricas anteriores:\")\n",
    "print(f'Precision Score : {precision_score(y_test,y_pred)}')\n",
    "print(f'Recall Score : {recall_score(y_test,y_pred)}')\n",
    "print(f'F1 Score : {f1_score(y_test,y_pred)}')\n",
    "\n",
    "print(\"\\nMétricas nuevas:\")\n",
    "print(f'Precision Score : {precision_new}')\n",
    "print(f'Recall Score : {recall_new}')\n",
    "print(f'F1 Score : {f1_new}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "911a3939",
   "metadata": {
    "papermill": {
     "duration": 0.011367,
     "end_time": "2024-01-26T15:19:42.570600",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.559233",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Métricas anteriores:\n",
      "Precision Score : 0.8392857142857143\n",
      "Recall Score : 0.6438356164383562\n",
      "F1 Score : 0.7286821705426357\n",
      "\n",
      "Métricas nuevas:\n",
      "Precision Score : 0.9259259259259259\n",
      "Recall Score : 0.5952380952380952\n",
      "F1 Score : 0.7246376811594204\n"
     ]
    }
   ],
   "source": [
    "#Luego de ejecutar todo el algoritmo, divida los datos de entrenamiento y prueba en 90% y 10% respectivamente, \n",
    "#e indique si las métricas precision_score, recall_score, y  f1_score mejoraron. Nota: En el código entregado \n",
    "#debe estar como lo hizo o lo calculó.\n",
    "\n",
    "X_train_new, X_test_new, y_train_new, y_test_new = train_test_split(scaled_df, y, test_size=0.1, random_state=42)\n",
    "\n",
    "# Entrenar el modelo con los nuevos datos de entrenamiento\n",
    "model.fit(X_train_new, y_train_new)\n",
    "\n",
    "# Realizar predicciones en los nuevos datos de prueba\n",
    "y_pred_new = model.predict(X_test_new)\n",
    "\n",
    "# Calcular métricas con los nuevos datos\n",
    "precision_new = precision_score(y_test_new, y_pred_new)\n",
    "recall_new = recall_score(y_test_new, y_pred_new)\n",
    "f1_new = f1_score(y_test_new, y_pred_new)\n",
    "\n",
    "# Comparar métricas anteriores y nuevas\n",
    "print(\"Métricas anteriores:\")\n",
    "print(f'Precision Score : {precision_score(y_test,y_pred)}')\n",
    "print(f'Recall Score : {recall_score(y_test,y_pred)}')\n",
    "print(f'F1 Score : {f1_score(y_test,y_pred)}')\n",
    "\n",
    "print(\"\\nMétricas nuevas:\")\n",
    "print(f'Precision Score : {precision_new}')\n",
    "print(f'Recall Score : {recall_new}')\n",
    "print(f'F1 Score : {f1_new}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ead82b47",
   "metadata": {
    "papermill": {
     "duration": 0.011005,
     "end_time": "2024-01-26T15:19:42.592839",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.581834",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Métricas anteriores:\n",
      "Precision Score : 0.8392857142857143\n",
      "Recall Score : 0.6438356164383562\n",
      "F1 Score : 0.7286821705426357\n",
      "\n",
      "Métricas nuevas:\n",
      "Precision Score : 0.8378378378378378\n",
      "Recall Score : 0.5740740740740741\n",
      "F1 Score : 0.6813186813186813\n"
     ]
    }
   ],
   "source": [
    "#Luego de ejecutar todo el algoritmo, divida los datos de entrenamiento y prueba en 70% y 30% respectivamente, \n",
    "#e indique si las métricas precision_score, recall_score, y  f1_score mejoraron. Nota: En el código entregado debe \n",
    "#estar como lo hizo o lo calculó.\n",
    "\n",
    "X_train_new, X_test_new, y_train_new, y_test_new = train_test_split(scaled_df, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Entrenar el modelo con los nuevos datos de entrenamiento\n",
    "model.fit(X_train_new, y_train_new)\n",
    "\n",
    "# Realizar predicciones en los nuevos datos de prueba\n",
    "y_pred_new = model.predict(X_test_new)\n",
    "\n",
    "# Calcular métricas con los nuevos datos\n",
    "precision_new = precision_score(y_test_new, y_pred_new)\n",
    "recall_new = recall_score(y_test_new, y_pred_new)\n",
    "f1_new = f1_score(y_test_new, y_pred_new)\n",
    "\n",
    "# Comparar métricas anteriores y nuevas\n",
    "print(\"Métricas anteriores:\")\n",
    "print(f'Precision Score : {precision_score(y_test,y_pred)}')\n",
    "print(f'Recall Score : {recall_score(y_test,y_pred)}')\n",
    "print(f'F1 Score : {f1_score(y_test,y_pred)}')\n",
    "\n",
    "print(\"\\nMétricas nuevas:\")\n",
    "print(f'Precision Score : {precision_new}')\n",
    "print(f'Recall Score : {recall_new}')\n",
    "print(f'F1 Score : {f1_new}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "255f5acc",
   "metadata": {
    "papermill": {
     "duration": 0.011098,
     "end_time": "2024-01-26T15:19:42.615156",
     "exception": false,
     "start_time": "2024-01-26T15:19:42.604058",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Métricas anteriores:\n",
      "Precision Score : 0.8392857142857143\n",
      "Recall Score : 0.6438356164383562\n",
      "F1 Score : 0.7286821705426357\n",
      "\n",
      "Métricas nuevas:\n",
      "Precision Score : 0.8378378378378378\n",
      "Recall Score : 0.5740740740740741\n",
      "F1 Score : 0.6813186813186813\n"
     ]
    }
   ],
   "source": [
    "# Crear un nuevo modelo de regresión logística con 100 iteraciones\n",
    "model = LogisticRegression(max_iter=100)\n",
    "\n",
    "# Entrenar el modelo con los nuevos datos de entrenamiento\n",
    "model.fit(X_train_new, y_train_new)\n",
    "\n",
    "# Realizar predicciones en los nuevos datos de prueba\n",
    "y_pred_new = model.predict(X_test_new)\n",
    "\n",
    "# Calcular métricas con los nuevos datos\n",
    "precision_new = precision_score(y_test_new, y_pred_new)\n",
    "recall_new = recall_score(y_test_new, y_pred_new)\n",
    "f1_new = f1_score(y_test_new, y_pred_new)\n",
    "\n",
    "# Comparar métricas anteriores y nuevas\n",
    "print(\"Métricas anteriores:\")\n",
    "print(f'Precision Score : {precision_score(y_test,y_pred)}')\n",
    "print(f'Recall Score : {recall_score(y_test,y_pred)}')\n",
    "print(f'F1 Score : {f1_score(y_test,y_pred)}')\n",
    "\n",
    "print(\"\\nMétricas nuevas:\")\n",
    "print(f'Precision Score : {precision_new}')\n",
    "print(f'Recall Score : {recall_new}')\n",
    "print(f'F1 Score : {f1_new}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07c656e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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