deploy

TP_4_FINAL.ipynb

@@ -0,0 +1,1416 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "El dataset contiene registros de sensores de smartphones de 4 actividades relacionadas con caídas y 9 actividades normales.\n",
+    "\n",
+    "Las que se corresponden con caídas son:  \n",
+    "* FOL: Caerse hacia adelante  \n",
+    "* FKL:  Caerse de rodillas  \n",
+    "* SDL:  Caerse de costado  \n",
+    "* BSC:  Caerse de una silla  \n",
+    "\n",
+    "Las actividades normales son:\n",
+    "* STD:  Estar parado  \n",
+    "* WAL:  Caminar  \n",
+    "* JOG:  Trotar  \n",
+    "* JUM:  Saltar   \n",
+    "* STU:  Subir escaleras  \n",
+    "* STN:  Bajar escaleras  \n",
+    "* SCH:  Sentarse  \n",
+    "* CSI:  Entrar a un automovil  \n",
+    "* CSO:  Salir de un automovil  \n",
+    "\n",
+    "Los registro del dataset fueron registrados por 11 individuos.\n",
+    "\n",
+    "Cada registro pertenece a una ventana temporal de 6 segundos, conteniendo \n",
+    "datos del acelerómetro y del giroscopio, dando lugar a las siguientes features:\n",
+    "\n",
+    "* acc_max:        dato de aceleración máxima del 4to segundo.  \n",
+    "* acc_kurtosis:   kurtosis de la aceleración durante los 6 segundos.  \n",
+    "* acc_skewness:   simetría de la aceleración durante los 6 segundos.  \n",
+    "* gyro_max:       dato máximo del giroscopio en el 4to segundo.  \n",
+    "* gyro_kurtosis:  kurtosis del giroscopio durante los 6 segundos.  \n",
+    "* gyro_skewness:  simetría del giroscopio durante los 6 segundos.  \n",
+    "* lin_max:        aceleración lineal máxima (excluyendo la gravedad) del 4to segundo.  \n",
+    "* post_lin_max:   aceleración lineal máxima en el 6to segundo.  \n",
+    "* post_gyro_max:  dato máximo del giroscopio en el 6to segundo.  \n",
+    "* fall:           1 si se corresponde con una caída, 0 si no.  \n",
+    "* label:          código de la actividad.  \n",
+    "\n",
+    "El dataset contiene 1784 registros, habiendo 1017 que se corresponden con actividades normales y 767 que se corresponden con caídas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.ensemble import IsolationForest\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "from imblearn import FunctionSampler\n",
+    "\n",
+    "from xgboost import XGBClassifier\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "from sklearn import set_config\n",
+    "set_config(display=\"diagram\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.read_csv('../tp_final_no_anda_la_clase_outlier/Train.csv')\n",
+    "df2 = pd.read_csv('../tp_final_no_anda_la_clase_outlier/Test.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1428, 12)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(356, 12)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df1, df2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1784, 12)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 0       0\n",
+       "acc_max          0\n",
+       "gyro_max         0\n",
+       "acc_kurtosis     0\n",
+       "gyro_kurtosis    0\n",
+       "label            0\n",
+       "lin_max          0\n",
+       "acc_skewness     0\n",
+       "gyro_skewness    0\n",
+       "post_gyro_max    0\n",
+       "post_lin_max     0\n",
+       "fall             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Unnamed: 0</th>\n",
+       "      <th>acc_max</th>\n",
+       "      <th>gyro_max</th>\n",
+       "      <th>acc_kurtosis</th>\n",
+       "      <th>gyro_kurtosis</th>\n",
+       "      <th>lin_max</th>\n",
+       "      <th>acc_skewness</th>\n",
+       "      <th>gyro_skewness</th>\n",
+       "      <th>post_gyro_max</th>\n",
+       "      <th>post_lin_max</th>\n",
+       "      <th>fall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "      <td>1784.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>891.500000</td>\n",
+       "      <td>21.768998</td>\n",
+       "      <td>5.028728</td>\n",
+       "      <td>10.031186</td>\n",
+       "      <td>3.916387</td>\n",
+       "      <td>7.976308</td>\n",
+       "      <td>1.732918</td>\n",
+       "      <td>1.629258</td>\n",
+       "      <td>3.191397</td>\n",
+       "      <td>5.228546</td>\n",
+       "      <td>0.429933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>515.140757</td>\n",
+       "      <td>5.479980</td>\n",
+       "      <td>2.943876</td>\n",
+       "      <td>11.836305</td>\n",
+       "      <td>5.489329</td>\n",
+       "      <td>4.258842</td>\n",
+       "      <td>1.529711</td>\n",
+       "      <td>0.999016</td>\n",
+       "      <td>3.429678</td>\n",
+       "      <td>5.004165</td>\n",
+       "      <td>0.495205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.787964</td>\n",
+       "      <td>0.026257</td>\n",
+       "      <td>-1.743347</td>\n",
+       "      <td>-1.532044</td>\n",
+       "      <td>0.043625</td>\n",
+       "      <td>-14.066208</td>\n",
+       "      <td>-0.460160</td>\n",
+       "      <td>-4.984168</td>\n",
+       "      <td>-5.382828</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>445.750000</td>\n",
+       "      <td>18.751488</td>\n",
+       "      <td>3.104216</td>\n",
+       "      <td>0.469997</td>\n",
+       "      <td>0.186524</td>\n",
+       "      <td>4.832765</td>\n",
+       "      <td>0.458187</td>\n",
+       "      <td>0.811557</td>\n",
+       "      <td>0.286294</td>\n",
+       "      <td>0.907965</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>891.500000</td>\n",
+       "      <td>22.924268</td>\n",
+       "      <td>4.568088</td>\n",
+       "      <td>8.423476</td>\n",
+       "      <td>2.028413</td>\n",
+       "      <td>8.282902</td>\n",
+       "      <td>1.520431</td>\n",
+       "      <td>1.542694</td>\n",
+       "      <td>2.452813</td>\n",
+       "      <td>3.727967</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1337.250000</td>\n",
+       "      <td>25.865634</td>\n",
+       "      <td>6.428771</td>\n",
+       "      <td>15.717815</td>\n",
+       "      <td>5.582912</td>\n",
+       "      <td>11.100896</td>\n",
+       "      <td>2.912764</td>\n",
+       "      <td>2.291739</td>\n",
+       "      <td>5.226240</td>\n",
+       "      <td>9.629489</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1783.000000</td>\n",
+       "      <td>32.885551</td>\n",
+       "      <td>17.288546</td>\n",
+       "      <td>231.134385</td>\n",
+       "      <td>34.163811</td>\n",
+       "      <td>25.382307</td>\n",
+       "      <td>6.782592</td>\n",
+       "      <td>5.174101</td>\n",
+       "      <td>16.204944</td>\n",
+       "      <td>23.972115</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Unnamed: 0      acc_max     gyro_max  acc_kurtosis  gyro_kurtosis  \\\n",
+       "count  1784.000000  1784.000000  1784.000000   1784.000000    1784.000000   \n",
+       "mean    891.500000    21.768998     5.028728     10.031186       3.916387   \n",
+       "std     515.140757     5.479980     2.943876     11.836305       5.489329   \n",
+       "min       0.000000     9.787964     0.026257     -1.743347      -1.532044   \n",
+       "25%     445.750000    18.751488     3.104216      0.469997       0.186524   \n",
+       "50%     891.500000    22.924268     4.568088      8.423476       2.028413   \n",
+       "75%    1337.250000    25.865634     6.428771     15.717815       5.582912   \n",
+       "max    1783.000000    32.885551    17.288546    231.134385      34.163811   \n",
+       "\n",
+       "           lin_max  acc_skewness  gyro_skewness  post_gyro_max  post_lin_max  \\\n",
+       "count  1784.000000   1784.000000    1784.000000    1784.000000   1784.000000   \n",
+       "mean      7.976308      1.732918       1.629258       3.191397      5.228546   \n",
+       "std       4.258842      1.529711       0.999016       3.429678      5.004165   \n",
+       "min       0.043625    -14.066208      -0.460160      -4.984168     -5.382828   \n",
+       "25%       4.832765      0.458187       0.811557       0.286294      0.907965   \n",
+       "50%       8.282902      1.520431       1.542694       2.452813      3.727967   \n",
+       "75%      11.100896      2.912764       2.291739       5.226240      9.629489   \n",
+       "max      25.382307      6.782592       5.174101      16.204944     23.972115   \n",
+       "\n",
+       "              fall  \n",
+       "count  1784.000000  \n",
+       "mean      0.429933  \n",
+       "std       0.495205  \n",
+       "min       0.000000  \n",
+       "25%       0.000000  \n",
+       "50%       0.000000  \n",
+       "75%       1.000000  \n",
+       "max       1.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 1784 entries, 0 to 355\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   Unnamed: 0     1784 non-null   int64  \n",
+      " 1   acc_max        1784 non-null   float64\n",
+      " 2   gyro_max       1784 non-null   float64\n",
+      " 3   acc_kurtosis   1784 non-null   float64\n",
+      " 4   gyro_kurtosis  1784 non-null   float64\n",
+      " 5   label          1784 non-null   object \n",
+      " 6   lin_max        1784 non-null   float64\n",
+      " 7   acc_skewness   1784 non-null   float64\n",
+      " 8   gyro_skewness  1784 non-null   float64\n",
+      " 9   post_gyro_max  1784 non-null   float64\n",
+      " 10  post_lin_max   1784 non-null   float64\n",
+      " 11  fall           1784 non-null   int64  \n",
+      "dtypes: float64(9), int64(2), object(1)\n",
+      "memory usage: 181.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>Unnamed: 0</th>\n",
+       "      <th>acc_max</th>\n",
+       "      <th>gyro_max</th>\n",
+       "      <th>acc_kurtosis</th>\n",
+       "      <th>gyro_kurtosis</th>\n",
+       "      <th>label</th>\n",
+       "      <th>lin_max</th>\n",
+       "      <th>acc_skewness</th>\n",
+       "      <th>gyro_skewness</th>\n",
+       "      <th>post_gyro_max</th>\n",
+       "      <th>post_lin_max</th>\n",
+       "      <th>fall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1044</th>\n",
+       "      <td>879</td>\n",
+       "      <td>22.960623</td>\n",
+       "      <td>6.481883</td>\n",
+       "      <td>4.701671</td>\n",
+       "      <td>2.504065</td>\n",
+       "      <td>CSI</td>\n",
+       "      <td>12.424865</td>\n",
+       "      <td>1.209656</td>\n",
+       "      <td>1.738483</td>\n",
+       "      <td>4.721564</td>\n",
+       "      <td>10.974288</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Unnamed: 0    acc_max  gyro_max  acc_kurtosis  gyro_kurtosis label  \\\n",
+       "1044         879  22.960623  6.481883      4.701671       2.504065   CSI   \n",
+       "\n",
+       "        lin_max  acc_skewness  gyro_skewness  post_gyro_max  post_lin_max  \\\n",
+       "1044  12.424865      1.209656       1.738483       4.721564     10.974288   \n",
+       "\n",
+       "      fall  \n",
+       "1044     0  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1784, 12)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Unnamed: 0'].value_counts().mean() #acá vemos que cada valor de esta columna aparece una sola vez, por lo que es un índice. \n",
+    "#será dropeada"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FOL    192\n",
+       "SDL    192\n",
+       "FKL    192\n",
+       "BSC    191\n",
+       "CSO    113\n",
+       "STD    113\n",
+       "SCH    113\n",
+       "STU    113\n",
+       "CSI    113\n",
+       "STN    113\n",
+       "JUM    113\n",
+       "WAL    113\n",
+       "JOG    113\n",
+       "Name: label, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['label'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1017\n",
+       "1     767\n",
+       "Name: fall, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['fall'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>fall</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>label</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BSC</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CSI</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CSO</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FKL</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FOL</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>JOG</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>JUM</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SCH</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SDL</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STN</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STU</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WAL</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       fall\n",
+       "label      \n",
+       "BSC       1\n",
+       "CSI       0\n",
+       "CSO       0\n",
+       "FKL       1\n",
+       "FOL       1\n",
+       "JOG       0\n",
+       "JUM       0\n",
+       "SCH       0\n",
+       "SDL       1\n",
+       "STD       0\n",
+       "STN       0\n",
+       "STU       0\n",
+       "WAL       0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# acá vemos que las categorías 'BSC', 'FKL', 'FOL', y 'STD' se corresponden al valor '1' de la columna 'fall' por lo que representan caídas, \n",
+    "# mientras que el resto de las categorias se corresponden con el valor '0' por lo que representan movimientos que no son caídas\n",
+    "grouped = df.groupby('label').agg({'fall': 'mean'}) \n",
+    "grouped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#acá confirmamos que los que no corresponden a caídas coinciden en cantidad con los \"0\" de la categoría a predecir\n",
+    "#df.loc[df['label'].isin(grupo[grupo < 120].index.tolist())]['label'].value_counts().sum() == df['fall'].value_counts()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_matrix = df.corr() #vamos a ver como correlacionan entre si las features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "#sns.heatmap(corr_matrix, cmap=\"Blues\", annot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "fall             1.000000\n",
+       "post_lin_max     0.864964\n",
+       "post_gyro_max    0.765410\n",
+       "acc_skewness     0.713811\n",
+       "gyro_skewness    0.685179\n",
+       "acc_max          0.609653\n",
+       "lin_max          0.581044\n",
+       "gyro_kurtosis    0.550182\n",
+       "acc_kurtosis     0.547179\n",
+       "gyro_max         0.468947\n",
+       "Unnamed: 0      -0.857480\n",
+       "Name: fall, dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix['fall'].sort_values(ascending = False) #ordenamos de mayor a menor las correlaciones con 'fall'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 0   -0.857480\n",
+       "gyro_max      0.468947\n",
+       "Name: fall, dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix['fall'][corr_matrix['fall'] < 0.5] # aca vemos que 'gyro_max' correlaciona poco con 'fall'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Unnamed: 0', 'acc_max', 'gyro_max', 'acc_kurtosis', 'gyro_kurtosis',\n",
+       "       'label', 'lin_max', 'acc_skewness', 'gyro_skewness', 'post_gyro_max',\n",
+       "       'post_lin_max', 'fall'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Entocnes como las columnas \"Unnamed: 0', 'gyro_max', y 'label' son innecesarioas, usamos una clase para preprocesar los datos que elimine estas columnas del dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FeatureSelection(BaseEstimator, TransformerMixin):\n",
+    "\n",
+    "    def __init__(self,selected_features):\n",
+    "        self.selected_features=selected_features\n",
+    "    \n",
+    "    def fit(self,X,y=None):\n",
+    "        return self\n",
+    "\n",
+    "    def transform(self, X, y=None):\n",
+    "        return X[self.selected_features]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class OutlierRemover(BaseEstimator, TransformerMixin):\n",
+    "    \n",
+    "    def __init__(self, n_std=3):\n",
+    "        self.n_std = n_std\n",
+    "    \n",
+    "    def fit(self, X, y = None):\n",
+    "        self.mean_ = np.mean(X, axis=0)\n",
+    "        self.std_ = np.std(X, axis=0)\n",
+    "        return self\n",
+    "    \n",
+    "    def transform(self, X, y):\n",
+    "        print(y)\n",
+    "       \n",
+    "            # Filtrar las filas que no contienen valores atípicos\n",
+    "        limite_inferior = self.mean_ - self.n_std * self.std_\n",
+    "        limite_superior = self.mean_ + self.n_std * self.std_\n",
+    "        mask = np.all((X > limite_inferior) & (X < limite_superior), axis=1)\n",
+    "        \n",
+    "        X_filtrado = X[mask]\n",
+    "        y = y[mask]\n",
+    "        return X_filtrado, y\n",
+    "    \n",
+    "    def fit_transform(self, X, y=None, **fit_params):\n",
+    "        return self.fit(X, y).transform(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1427, 11)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(357, 11)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(1427,)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(357,)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Separamos las variables independientes de la target\n",
+    "X=df.drop(columns=['fall'])\n",
+    "y=df['fall']\n",
+    "\n",
+    "# Dividimos los datos en el set de train y el de test: \n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=100, stratify=y)\n",
+    "display(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
+    "display(type(X_train), type(X_test), type(y_train), type(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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>Unnamed: 0</th>\n",
+       "      <th>acc_max</th>\n",
+       "      <th>gyro_max</th>\n",
+       "      <th>acc_kurtosis</th>\n",
+       "      <th>gyro_kurtosis</th>\n",
+       "      <th>label</th>\n",
+       "      <th>lin_max</th>\n",
+       "      <th>acc_skewness</th>\n",
+       "      <th>gyro_skewness</th>\n",
+       "      <th>post_gyro_max</th>\n",
+       "      <th>post_lin_max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>765</th>\n",
+       "      <td>931</td>\n",
+       "      <td>17.310921</td>\n",
+       "      <td>5.78264</td>\n",
+       "      <td>5.979438</td>\n",
+       "      <td>-0.16566</td>\n",
+       "      <td>CSO</td>\n",
+       "      <td>4.717529</td>\n",
+       "      <td>1.367272</td>\n",
+       "      <td>0.811601</td>\n",
+       "      <td>5.699724</td>\n",
+       "      <td>4.569499</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0    acc_max  gyro_max  acc_kurtosis  gyro_kurtosis label  \\\n",
+       "765         931  17.310921   5.78264      5.979438       -0.16566   CSO   \n",
+       "\n",
+       "      lin_max  acc_skewness  gyro_skewness  post_gyro_max  post_lin_max  \n",
+       "765  4.717529      1.367272       0.811601       5.699724      4.569499  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.sample()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=0) #preparo el cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#le pongo estos pasos x defecto al pipeline\n",
+    "pipeline = Pipeline([('FeatureSelection', FeatureSelection(['acc_max', 'acc_kurtosis', 'gyro_kurtosis',\n",
+    "       'lin_max', 'acc_skewness', 'gyro_skewness', 'post_gyro_max', 'post_lin_max'])), \n",
+    "#      ('OutlierRemover', OutlierRemover()),\n",
+    "       ('scaler', StandardScaler()), \n",
+    "       ('model', LogisticRegression())], verbose = False) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pipeline.steps[0][1].fit_transform(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# en esta lista de diccionarios pongo las cosas que quiero que pruebe el CV\n",
+    "# en el pipe vamos a probar 4 modelos con varios hiperparámetros\n",
+    "param_grid = [ {'model': [KNeighborsClassifier()], \"model__n_neighbors\": [2, 3, 4, 5, 6, 7, 8], 'model__weights' : ['uniform', 'distance'], 'scaler' : [StandardScaler(), MinMaxScaler(), None]}, \n",
+    "               {'model': [LogisticRegression()], 'model__C': [0.01, 0.1, 1, 10, 100, 1000], 'model__penalty': ['l2', None], 'scaler' : [StandardScaler(), MinMaxScaler(), None]} ,\n",
+    "               {'model': [RandomForestClassifier()], 'model__criterion': ['gini', 'entropy'], 'scaler' : [StandardScaler(), MinMaxScaler(), None]},\n",
+    "               {'model': [XGBClassifier(objective='binary:logistic', eval_metric='logloss')], 'model__learning_rate': [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2], 'scaler' : [StandardScaler(), MinMaxScaler(), None] }\n",
+    "               ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid = GridSearchCV(pipeline, param_grid, cv=folds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"484012e9-2376-4ea2-ae1f-88c9569a5cd9\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"484012e9-2376-4ea2-ae1f-88c9569a5cd9\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=0, shuffle=True),\n",
+       "             estimator=Pipeline(steps=[('FeatureSelection',\n",
+       "                                        FeatureSelection(selected_features=['acc_max',\n",
+       "                                                                            'acc_kurtosis',\n",
+       "                                                                            'gyro_kurtosis',\n",
+       "                                                                            'lin_max',\n",
+       "                                                                            'acc_skewness',\n",
+       "                                                                            'gyro_skewness',\n",
+       "                                                                            'post_gyro_max',\n",
+       "                                                                            'post_lin_max'])),\n",
+       "                                       ('scaler', StandardScaler()),\n",
+       "                                       ('model', LogisticRegression())]),\n",
+       "             param_grid=[{'model...\n",
+       "                                                  missing=nan,\n",
+       "                                                  monotone_constraints=None,\n",
+       "                                                  n_estimators=100, n_jobs=None,\n",
+       "                                                  num_parallel_tree=None,\n",
+       "                                                  random_state=None,\n",
+       "                                                  reg_alpha=None,\n",
+       "                                                  reg_lambda=None,\n",
+       "                                                  scale_pos_weight=None,\n",
+       "                                                  subsample=None,\n",
+       "                                                  tree_method=None,\n",
+       "                                                  validate_parameters=None,\n",
+       "                                                  verbosity=None)],\n",
+       "                          'model__learning_rate': [0.2, 0.3, 0.4, 0.5, 0.6, 0.7,\n",
+       "                                                   0.8, 0.9, 1, 1.1, 1.2],\n",
+       "                          'scaler': [StandardScaler(), MinMaxScaler(), None]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a5d56b68-499d-4387-b7cd-4b1c5fa5176e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a5d56b68-499d-4387-b7cd-4b1c5fa5176e\">FeatureSelection</label><div class=\"sk-toggleable__content\"><pre>FeatureSelection(selected_features=['acc_max', 'acc_kurtosis', 'gyro_kurtosis',\n",
+       "                                    'lin_max', 'acc_skewness', 'gyro_skewness',\n",
+       "                                    'post_gyro_max', 'post_lin_max'])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c03eadc0-04c3-4a2f-b996-b40a444633f1\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c03eadc0-04c3-4a2f-b996-b40a444633f1\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"fdb7ff8f-c74e-4082-831e-cf7ce9689a15\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"fdb7ff8f-c74e-4082-831e-cf7ce9689a15\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=0, shuffle=True),\n",
+       "             estimator=Pipeline(steps=[('FeatureSelection',\n",
+       "                                        FeatureSelection(selected_features=['acc_max',\n",
+       "                                                                            'acc_kurtosis',\n",
+       "                                                                            'gyro_kurtosis',\n",
+       "                                                                            'lin_max',\n",
+       "                                                                            'acc_skewness',\n",
+       "                                                                            'gyro_skewness',\n",
+       "                                                                            'post_gyro_max',\n",
+       "                                                                            'post_lin_max'])),\n",
+       "                                       ('scaler', StandardScaler()),\n",
+       "                                       ('model', LogisticRegression())]),\n",
+       "             param_grid=[{'model...\n",
+       "                                                  missing=nan,\n",
+       "                                                  monotone_constraints=None,\n",
+       "                                                  n_estimators=100, n_jobs=None,\n",
+       "                                                  num_parallel_tree=None,\n",
+       "                                                  random_state=None,\n",
+       "                                                  reg_alpha=None,\n",
+       "                                                  reg_lambda=None,\n",
+       "                                                  scale_pos_weight=None,\n",
+       "                                                  subsample=None,\n",
+       "                                                  tree_method=None,\n",
+       "                                                  validate_parameters=None,\n",
+       "                                                  verbosity=None)],\n",
+       "                          'model__learning_rate': [0.2, 0.3, 0.4, 0.5, 0.6, 0.7,\n",
+       "                                                   0.8, 0.9, 1, 1.1, 1.2],\n",
+       "                          'scaler': [StandardScaler(), MinMaxScaler(), None]}])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.fit(X_train, y_train) #muestra los pasos x defecto"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e6e964df-0baa-49a5-8a1e-77b6f069b78c\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e6e964df-0baa-49a5-8a1e-77b6f069b78c\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('FeatureSelection',\n",
+       "                 FeatureSelection(selected_features=['acc_max', 'acc_kurtosis',\n",
+       "                                                     'gyro_kurtosis', 'lin_max',\n",
+       "                                                     'acc_skewness',\n",
+       "                                                     'gyro_skewness',\n",
+       "                                                     'post_gyro_max',\n",
+       "                                                     'post_lin_max'])),\n",
+       "                ('scaler', StandardScaler()),\n",
+       "                ('model',\n",
+       "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
+       "                               colsample_bylevel=1, colsample_bynode=1,\n",
+       "                               colsample_bytree=1, eval_metric='logloss',\n",
+       "                               gamma=0, gpu_id=-1, importance_type='gain',\n",
+       "                               interaction_constraints='', learning_rate=0.5,\n",
+       "                               max_delta_step=0, max_depth=6,\n",
+       "                               min_child_weight=1, missing=nan,\n",
+       "                               monotone_constraints='()', n_estimators=100,\n",
+       "                               n_jobs=12, num_parallel_tree=1, random_state=0,\n",
+       "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
+       "                               subsample=1, tree_method='exact',\n",
+       "                               validate_parameters=1, verbosity=None))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6d5f5ea5-673e-4db1-b258-041feb8a3482\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"6d5f5ea5-673e-4db1-b258-041feb8a3482\">FeatureSelection</label><div class=\"sk-toggleable__content\"><pre>FeatureSelection(selected_features=['acc_max', 'acc_kurtosis', 'gyro_kurtosis',\n",
+       "                                    'lin_max', 'acc_skewness', 'gyro_skewness',\n",
+       "                                    'post_gyro_max', 'post_lin_max'])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4cd06878-e1f5-4ebf-a2e7-f966b2c7ce03\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4cd06878-e1f5-4ebf-a2e7-f966b2c7ce03\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"289c49df-e6a2-4e71-97d2-f53cf749144e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"289c49df-e6a2-4e71-97d2-f53cf749144e\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
+       "              colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',\n",
+       "              gamma=0, gpu_id=-1, importance_type='gain',\n",
+       "              interaction_constraints='', learning_rate=0.5, max_delta_step=0,\n",
+       "              max_depth=6, min_child_weight=1, missing=nan,\n",
+       "              monotone_constraints='()', n_estimators=100, n_jobs=12,\n",
+       "              num_parallel_tree=1, random_state=0, reg_alpha=0, reg_lambda=1,\n",
+       "              scale_pos_weight=1, subsample=1, tree_method='exact',\n",
+       "              validate_parameters=1, verbosity=None)</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('FeatureSelection',\n",
+       "                 FeatureSelection(selected_features=['acc_max', 'acc_kurtosis',\n",
+       "                                                     'gyro_kurtosis', 'lin_max',\n",
+       "                                                     'acc_skewness',\n",
+       "                                                     'gyro_skewness',\n",
+       "                                                     'post_gyro_max',\n",
+       "                                                     'post_lin_max'])),\n",
+       "                ('scaler', StandardScaler()),\n",
+       "                ('model',\n",
+       "                 XGBClassifier(base_score=0.5, booster='gbtree',\n",
+       "                               colsample_bylevel=1, colsample_bynode=1,\n",
+       "                               colsample_bytree=1, eval_metric='logloss',\n",
+       "                               gamma=0, gpu_id=-1, importance_type='gain',\n",
+       "                               interaction_constraints='', learning_rate=0.5,\n",
+       "                               max_delta_step=0, max_depth=6,\n",
+       "                               min_child_weight=1, missing=nan,\n",
+       "                               monotone_constraints='()', n_estimators=100,\n",
+       "                               n_jobs=12, num_parallel_tree=1, random_state=0,\n",
+       "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
+       "                               subsample=1, tree_method='exact',\n",
+       "                               validate_parameters=1, verbosity=None))])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_estimator_ #el mejor modelo "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "El modelo arrojó un accuracy score en el conjunto de entrenamiento de:  0.9824880382775121\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"El modelo arrojó un accuracy score en el conjunto de entrenamiento de: \", grid.best_score_) #vemos el accuracy del mejor modelo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'model': XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,\n",
+       "               colsample_bynode=None, colsample_bytree=None,\n",
+       "               eval_metric='logloss', gamma=None, gpu_id=None,\n",
+       "               importance_type='gain', interaction_constraints=None,\n",
+       "               learning_rate=0.5, max_delta_step=None, max_depth=None,\n",
+       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "               n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
+       "               random_state=None, reg_alpha=None, reg_lambda=None,\n",
+       "               scale_pos_weight=None, subsample=None, tree_method=None,\n",
+       "               validate_parameters=None, verbosity=None),\n",
+       " 'model__learning_rate': 0.5,\n",
+       " 'scaler': StandardScaler()}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_params_ #vemos los mejores hiperparámetros del mejor modelo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "El modelo tiene un accuracy score de:  0.9803921568627451\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"El modelo tiene un accuracy score de: \", accuracy_score(grid.best_estimator_.predict(X_test),y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = grid.best_estimator_.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.98      0.99      0.98       204\n",
+      "           1       0.99      0.97      0.98       153\n",
+      "\n",
+      "    accuracy                           0.98       357\n",
+      "   macro avg       0.98      0.98      0.98       357\n",
+      "weighted avg       0.98      0.98      0.98       357\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "import seaborn as sns\n",
+    "import itertools\n",
+    "y_pred_ = list(itertools.chain(y_pred))\n",
+    "y_test_ = list(itertools.chain(y_test))\n",
+    "\n",
+    "print(classification_report(y_test_, y_pred_))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_model = grid.best_estimator_\n",
+    "\n",
+    "\n",
+    "with open('mejor_modelo_tp4.pkl', 'wb') as f:\n",
+    "    pickle.dump(best_model, f)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dhdsblend2021",
+   "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": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "052e7fd3051fb62256c874c1940dfbcd26c7f9302251177c1c2130ce8acd18fb"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Test.csv

@@ -0,0 +1,357 @@
+,acc_max,gyro_max,acc_kurtosis,gyro_kurtosis,label,lin_max,acc_skewness,gyro_skewness,post_gyro_max,post_lin_max,fall
+9,28.05519859535977,10.794617323010852,21.334536447601472,34.16381128227045,FOL,13.880577944682654,3.2834044377683167,4.577282542553694,10.755338641709578,13.762561204533556,1
+11,26.639680553929008,8.785023804201199,13.518670772298544,12.812893552287926,FOL,15.789371872403208,3.301849452968188,3.464729238136188,8.27771420011614,15.341656211635405,1
+19,25.045218505920648,5.307413288701565,21.60306032276784,4.754181686390407,FOL,11.592445108437284,3.1247144793250032,2.2686758039686903,4.976133776145394,11.30382329642677,1
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+41,24.120890588912825,4.82297262209205,25.28494104943809,5.373700918777202,FOL,9.68498816989189,3.94663214869771,2.40115959447104,4.758358638905193,9.581053127060589,1
+42,26.09969906685643,10.7519124233488,12.484428454348857,16.478316373746846,FOL,10.134481512014686,2.444529286618209,3.489323502920626,10.695903209006188,9.969141418679769,1
+43,28.25568601927073,5.5649846715010085,14.78561539490733,4.064957793988343,FOL,8.468906272060678,3.203897963486894,2.145354235724656,4.700211009368287,7.363725283954484,1
+47,27.16508320914863,7.994755618388835,16.395911474276335,8.117632190903231,FOL,12.63057210027421,3.4340486197109343,2.768507302368722,7.940154035797493,12.538196828860068,1
+53,26.520924461992315,4.615356438779256,12.813780709669786,6.148091496822191,FOL,11.901719337442712,2.4349214273955813,2.492567082359076,4.510459118641143,11.720810783861277,1
+57,26.669630771920826,6.432186507829393,19.00300970826891,10.423145021373443,FOL,8.77849702687983,3.7490579055184208,3.1861761370076334,6.09362412299623,8.111725717307966,1
+58,30.539622152317552,7.007042588384992,21.385483968891748,9.820452926189162,FOL,15.40041070729741,4.231585520394664,3.10587624169102,6.952354793382767,15.303016591184177,1
+70,26.87072175030601,12.88523832301778,11.4655886900107,9.008338142902726,FOL,12.933568586385608,2.62442788896165,2.9128471378196967,12.652602706249606,12.563903550117214,1
+72,28.558023875692506,4.182922027922354,19.637086348188262,4.952926927612339,FOL,15.99893496624578,3.5214803988561982,2.178141781004954,3.9288399032277113,15.676040686881937,1
+80,24.402452552822385,7.328835002159143,19.319826683355068,19.79163198263485,FOL,8.361839005891609,3.745745442309167,3.706123966640385,7.1194719143120935,8.150851024555074,1
+82,25.183593909424424,11.50561153560719,12.60519583569215,9.353825681292246,FOL,12.26174307893678,2.266115054691958,2.5906282109473286,11.315924841619529,12.095609133007247,1
+84,21.28777202057265,2.7621960561865486,6.1931385222203925,6.2147658963635255,FOL,4.845818787660597,1.8231253670985967,2.443142005419769,2.6177596910786023,4.686363085273009,1
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app.py

@@ -0,0 +1,147 @@
+import streamlit as st
+import pandas as pd
+import pickle
+
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from sklearn.pipeline import Pipeline
+from sklearn.pipeline import make_pipeline
+
+from sklearn.model_selection import GridSearchCV
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import GridSearchCV
+from sklearn.model_selection import StratifiedKFold
+from sklearn.model_selection import cross_val_score
+from sklearn.metrics import classification_report
+from sklearn.metrics import accuracy_score
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import IsolationForest
+from sklearn.tree import DecisionTreeClassifier
+
+from fs import FeatureSelection
+
+from xgboost import XGBClassifier
+
+import warnings
+warnings.filterwarnings('ignore')
+from sklearn import set_config
+set_config(display="diagram")
+
+
+    
+
+with open('mejor_modelo_tp4.pkl', 'rb') as f:
+    modelo = pickle.load(f)
+
+
+# Define una función para hacer predicciones con el modelo
+def predecir(features):
+    # Procesa los valores de features y hace predicciones con el modelo
+    predicciones = modelo.predict(features)
+    return predicciones
+
+# 'Unnamed: 0', 'acc_max', 'gyro_max', 'acc_kurtosis', 'gyro_kurtosis',
+#        'label', 'lin_max', 'acc_skewness', 'gyro_skewness', 'post_gyro_max',
+#        'post_lin_max', 'fall']
+
+# Crea la aplicación Streamlit
+def app():
+    
+    st.title('Ingrese los valores de las features')
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+            feature1 = st.text_input('Unnamed: 0', value = 61)
+
+    with col2:
+            feature2 = st.text_input('acc_max', value = 26.310655)
+
+    with col3:
+            feature3 = st.text_input('gyro_max', value = 5.192876)
+
+    col4, col5, col6 = st.columns(3)
+
+    with col4:
+            feature4 = st.text_input('acc_kurtosis', value = 17.569042)
+
+    with col5:
+            feature5 = st.text_input('gyro_kurtosis', value = 9.776727)
+
+    with col6:
+            feature6 = st.text_input('label', value = 'FOL')
+    
+    col7, col8, col9 = st.columns(3)
+
+    with col7:
+            feature7 = st.text_input('lin_max', value = 11.584056)
+
+    with col8:
+            feature8 = st.text_input('acc_skewness', value = 3.587634)
+
+    with col9:
+            feature9 = st.text_input('gyro_skewness', value = 2.848477)
+
+    col10, col11, col12 = st.columns(3)
+
+    with col10:
+            feature10 = st.text_input('post_gyro_max', value = 4.691588)
+
+    with col11:
+            feature11 = st.text_input('post_lin_max', value = 10.684285)
+
+    # with col12:
+    #         feature12 =  st.text_input('3')
+    # # # Crea los campos de entrada de texto para las features
+    # feature1 = st.text_input('Unnamed: 0')
+    # feature2 = st.text_input('acc_max')
+    # feature3 = st.text_input('gyro_max')
+    # feature4 = st.text_input('acc_kurtosis')
+    # feature5 = st.text_input('gyro_kurtosis')
+    # feature6 = st.text_input('label')
+    # feature7 = st.text_input('lin_max')
+    # feature8 = st.text_input('acc_skewness')
+    # feature9 = st.text_input('gyro_skewness')
+    # feature10 = st.text_input('post_gyro_max')
+    # feature11 = st.text_input('post_lin_max')
+
+    # Crea un botón para hacer predicciones con el modelo
+    if st.button('Predecir'):
+        # Convierte los valores de features en un DataFrame de Pandas
+        features = pd.DataFrame({
+            'Unnamed: 0': [feature1],
+            'acc_max': [feature2],
+            'gyro_max': [feature3],
+            'acc_kurtosis': [feature4],
+            'gyro_kurtosis': [feature5],
+            'label': [feature6],
+            'lin_max': [feature7],
+            'acc_skewness': [feature8],
+            'gyro_skewness': [feature9],
+            'post_gyro_max': [feature10],
+            'post_lin_max': [feature11],          
+        })
+        
+        # if predecir(features) == 0:
+        #     predicciones = 'NO '
+        # else:
+        #     predicciones = ''
+        if predecir(features) == [0]:
+               respuesta = ' NO  '
+        else:
+               respuesta = ' SI  '
+        st.write(f'Estos datos {respuesta}son compatibles con una caída')
+
+    if st.button('Modelo'):
+               
+        st.write(modelo.named_steps)
+
+# Ejecuta la aplicación Streamlit
+if __name__ == '__main__':
+    app()

fs.py

@@ -0,0 +1,12 @@
+
+from sklearn.base import BaseEstimator, TransformerMixin
+class FeatureSelection(BaseEstimator, TransformerMixin):
+
+    def __init__(self,selected_features):
+        self.selected_features=selected_features
+    
+    def fit(self,X,y=None):
+        return self
+
+    def transform(self, X, y=None):
+        return X[self.selected_features]

mejor_modelo_tp4.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2bba03af283884d9abcd9ad006e28c12722d0bcc531f14736ef71d1907555903
+size 97073