Uso del algoritmo de K-Means para la detección de transacciones bancarias fraudulentas

17. KMEANS - Detección de transacciones bancarias fraudulentas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelo: Algoritmo _KMEANS_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autor: _José Alamo Palomino_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caso Práctico: Detección de transacciones bancarias fraudulentas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contexto\n",
    "\n",
    "_Es importante que las compañías de tarjetas de crédito puedan reconocer transacciones fraudulentas de tarjetas de crédito para que a los clientes no se les cobre por los artículos que no compraron._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contenido\n",
    "\n",
    "* Los conjuntos de datos contienen transacciones realizadas con tarjetas de crédito en septiembre de 2013 por titulares de tarjetas europeas.\n",
    "\n",
    "\n",
    "* Este conjunto de datos presenta transacciones que ocurrieron en dos días, donde tenemos **492 fraudes de 284,807 transacciones**. El conjunto de datos está altamente desequilibrado, la clase positiva (fraudes) representa el 0.172% de todas las transacciones.\n",
    "\n",
    "\n",
    "* Contiene solo **variables de entrada numéricas que son el resultado de una transformación PCA**. Desafortunadamente, debido a problemas de confidencialidad, no podemos proporcionar las características originales y más información de fondo sobre los datos. \n",
    "\n",
    "\n",
    "* **Las características V1, V2, ... V28 son los componentes principales obtenidos con PCA**, las únicas características que no se han transformado con PCA son **'Tiempo' y 'Cantidad'**. \n",
    "\n",
    "\n",
    "* La característica **'Tiempo'** contiene los segundos transcurridos entre cada transacción y la primera transacción en el conjunto de datos. \n",
    "\n",
    "\n",
    "* La característica **'Cantidad'** es la Cantidad de dinero en la transacción, esta característica se puede utilizar para el aprendizaje sensible al costo dependiente del ejemplo. \n",
    "\n",
    "\n",
    "* La característica **'Clase'** es la variable de respuesta y toma el valor 1 en caso de fraude y 0 en caso contrario."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspiración\n",
    "\n",
    "* Identificar transacciones fraudulentas con tarjeta de crédito.\n",
    "\n",
    "* Dada la relación de desequilibrio de clase, recomendamos medir la precisión utilizando el área bajo la curva de precisión-recuperación (AUPRC). La precisión de la matriz de confusión no es significativa para la clasificación desequilibrada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <h2 style = \"color:darkorange\">1. Importando librerías necesarias</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.gridspec as gridspec\n",
    "from collections import Counter\n",
    "from sklearn import metrics\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <h2 style = \"color:darkorange\">2. Funciones auxiliares</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Función para representar gráficamente el algoritmo K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(X, y):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], 'k.', markersize=2)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], 'r.', markersize=2)\n",
    "\n",
    "def plot_centroids(centroids, weights=None, circle_color='w', cross_color='k'): # Centroides\n",
    "    if weights is not None:\n",
    "        centroids = centroids[weights > weights.max() / 10]\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "                marker='o', s=30, linewidths=8,\n",
    "                color=circle_color, zorder=10, alpha=0.9)\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "                marker='x', s=50, linewidths=50,\n",
    "                color=cross_color, zorder=11, alpha=1)\n",
    "\n",
    "def plot_decision_boundaries(clusterer, X, y, resolution=1000, show_centroids=True): # Límite de decisión\n",
    "    mins = X.min(axis=0) - 0.1\n",
    "    maxs = X.max(axis=0) + 0.1\n",
    "    xx, yy = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\n",
    "                         np.linspace(mins[1], maxs[1], resolution))\n",
    "    Z = clusterer.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    plt.contourf(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\n",
    "                cmap=\"Pastel2\")\n",
    "    plt.contour(Z, extent=(mins[0], maxs[0], mins[1], maxs[1]),\n",
    "                linewidths=1, colors='k')\n",
    "    plot_data(X, y)\n",
    "    if show_centroids:\n",
    "        plot_centroids(clusterer.cluster_centers_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Función para calcular la pureza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def purity_score(y_true, y_pred):\n",
    "    # compute contingency matrix (also called confusion matrix)\n",
    "    contingency_matrix = metrics.cluster.contingency_matrix(y_true, y_pred)\n",
    "    # return purity\n",
    "    return np.sum(np.amax(contingency_matrix, axis=0)) / np.sum(contingency_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style = \"color:darkorange\">3. Lectura del conjunto de datos </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observamos que los datos apracecen como que en la misma escala (pero no están escalados) al aplicarse el algoritmo de \n",
    "# reducción de la dimensión PCA.\n",
    "df = pd.read_csv(\"Datasets/creditcard.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style = \"color:darkorange\">4. Visualización del conjunto de datos</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.229658</td>\n",
       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
       "      <td>0.191881</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167716</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.644269</td>\n",
       "      <td>1.417964</td>\n",
       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.428118</td>\n",
       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.943465</td>\n",
       "      <td>-1.015455</td>\n",
       "      <td>0.057504</td>\n",
       "      <td>-0.649709</td>\n",
       "      <td>-0.415267</td>\n",
       "      <td>-0.051634</td>\n",
       "      <td>-1.206921</td>\n",
       "      <td>-1.085339</td>\n",
       "      <td>40.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.894286</td>\n",
       "      <td>0.286157</td>\n",
       "      <td>-0.113192</td>\n",
       "      <td>-0.271526</td>\n",
       "      <td>2.669599</td>\n",
       "      <td>3.721818</td>\n",
       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073425</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>93.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.338262</td>\n",
       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de características: 31\n",
      "Longitud del conjunto de datos: 284807\n"
     ]
    }
   ],
   "source": [
    "print(\"Número de características:\", len(df.columns))\n",
    "print(\"Longitud del conjunto de datos:\", len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 492 transacciones fraudulentas, 284315 transacciones legitimas\n",
    "# El conjunto de datos se encuntra desequilabrado\n",
    "df[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "# Visualizamos los tipos de cada uno de los atributos\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      False\n",
       "V1        False\n",
       "V2        False\n",
       "V3        False\n",
       "V4        False\n",
       "V5        False\n",
       "V6        False\n",
       "V7        False\n",
       "V8        False\n",
       "V9        False\n",
       "V10       False\n",
       "V11       False\n",
       "V12       False\n",
       "V13       False\n",
       "V14       False\n",
       "V15       False\n",
       "V16       False\n",
       "V17       False\n",
       "V18       False\n",
       "V19       False\n",
       "V20       False\n",
       "V21       False\n",
       "V22       False\n",
       "V23       False\n",
       "V24       False\n",
       "V25       False\n",
       "V26       False\n",
       "V27       False\n",
       "V28       False\n",
       "Amount    False\n",
       "Class     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comprobamos si alguna columna tiene valores nulos\n",
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x2304 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representamos gráficamente las características\n",
    "features = df.drop(\"Class\", axis=1)\n",
    "\n",
    "plt.figure(figsize=(12,32))\n",
    "gs = gridspec.GridSpec(8, 4)\n",
    "gs.update(hspace=0.8)\n",
    "\n",
    "for i, f in enumerate(features):\n",
    "    ax = plt.subplot(gs[i])\n",
    "    sns.distplot(df[f][df[\"Class\"] == 1])\n",
    "    sns.distplot(df[f][df[\"Class\"] == 0])\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_title('feature: ' + str(f))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Observamos a cada uno de los atributos de entrada, de tal manera que se pueda visualizar en cada uno de ellos cada una\n",
    "# de las clases de la variable de salida. Los legítimos en azul y fraudulentos en naranja.\n",
    "\n",
    "# Esta gráfica nos sirve para ver que características diferencian bastante bien los elementos positivos de los negativos\n",
    "# (categorías de la variable respuesta).\n",
    "\n",
    "# Para la variable Time, podemos observar que los elementos positivos y negativos se encuentran en el mismo rango y con \n",
    "# la misma densidad.\n",
    "\n",
    "# La característica V4 si que va a diferenciar bastante los ejemplos positivos de los negativos, es decir, si que\n",
    "# diferencia a las clases legítimas y fraudulentas. Entonces puede ser una característica bastante útil para utilizarla\n",
    "# en diferenciar entre transacciones legítimas y fraudulentas.\n",
    "\n",
    "# Similar comportamiento vemos en las variables V10, V11, V12, V14, V16 y V17."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Representación gráfica de dos características:\n",
    "#### Para graficar usamos las variables V10 y V14, las cuales diferencian bien a los ejemplos fraudulentos de los legítimos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(df[\"V10\"][df['Class'] == 0], df[\"V14\"][df['Class'] == 0], c=\"g\", marker=\".\")\n",
    "plt.scatter(df[\"V10\"][df['Class'] == 1], df[\"V14\"][df['Class'] == 1], c=\"r\", marker=\".\")\n",
    "plt.xlabel(\"V10\", fontsize=14)\n",
    "plt.ylabel(\"V14\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ¿Que pasaría si graficamos 2 variables que no diferencian bien a las transacciones fraudulentas de las legítimas?\n",
    "Observamos que usando las variables V24 y V26 sería muy complicado diferenciar entre ambas categorías, es decir las transacciones legítimas como fraudulentas se aprecian en el mismo rango."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(df[\"V24\"][df['Class'] == 0], df[\"V26\"][df['Class'] == 0], c=\"g\", marker=\".\")\n",
    "plt.scatter(df[\"V24\"][df['Class'] == 1], df[\"V26\"][df['Class'] == 1], c=\"r\", marker=\".\")\n",
    "plt.xlabel(\"V24\", fontsize=14)\n",
    "plt.ylabel(\"V26\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Luego de ganar estas instuiciones sobre nuestras características de manera gráfica, ya podríamos saber que variables pueden ser o no útiles para nuestro algoritmo.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style = \"color:darkorange\">5. Preparación del conjunto de datos</h2> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para este tipo de algoritmos es importante que **todos los datos se encuentren en un rango similar, por lo tanto, podemos aplicar una función de escalado o normalización**. \n",
    "\n",
    "Otra opción, es eliminar las características que no se encuentran en un rango similar siempre y cuando no sean muy influyentes para la predicción."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>4.356170</td>\n",
       "      <td>...</td>\n",
       "      <td>1.475829</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>-0.975926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059616</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>-0.484782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>-0.399126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127434</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>-0.915427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.382948</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               V1         V2        V3        V4        V5        V6  \\\n",
       "0       -1.359807  -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
       "1        1.191857   0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
       "2       -1.358354  -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
       "3       -0.966272  -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
       "4       -1.158233   0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
       "...           ...        ...       ...       ...       ...       ...   \n",
       "284802 -11.881118  10.071785 -9.834783 -2.066656 -5.364473 -2.606837   \n",
       "284803  -0.732789  -0.055080  2.035030 -0.738589  0.868229  1.058415   \n",
       "284804   1.919565  -0.301254 -3.249640 -0.557828  2.630515  3.031260   \n",
       "284805  -0.240440   0.530483  0.702510  0.689799 -0.377961  0.623708   \n",
       "284806  -0.533413  -0.189733  0.703337 -0.506271 -0.012546 -0.649617   \n",
       "\n",
       "              V7        V8        V9       V10  ...       V20       V21  \\\n",
       "0       0.239599  0.098698  0.363787  0.090794  ...  0.251412 -0.018307   \n",
       "1      -0.078803  0.085102 -0.255425 -0.166974  ... -0.069083 -0.225775   \n",
       "2       0.791461  0.247676 -1.514654  0.207643  ...  0.524980  0.247998   \n",
       "3       0.237609  0.377436 -1.387024 -0.054952  ... -0.208038 -0.108300   \n",
       "4       0.592941 -0.270533  0.817739  0.753074  ...  0.408542 -0.009431   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -4.918215  7.305334  1.914428  4.356170  ...  1.475829  0.213454   \n",
       "284803  0.024330  0.294869  0.584800 -0.975926  ...  0.059616  0.214205   \n",
       "284804 -0.296827  0.708417  0.432454 -0.484782  ...  0.001396  0.232045   \n",
       "284805 -0.686180  0.679145  0.392087 -0.399126  ...  0.127434  0.265245   \n",
       "284806  1.577006 -0.414650  0.486180 -0.915427  ...  0.382948  0.261057   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0       0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1      -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2       0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3       0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4       0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "284802  0.111864  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731   \n",
       "284803  0.924384  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   \n",
       "284804  0.578229 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   \n",
       "284805  0.800049 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   \n",
       "284806  0.643078  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 29 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observando de analizar la representación gráfica de las características de entrada, vemos que las variables \"Time\" y \n",
    "# \"Amount\" no ayuda a diferenciar las transacciones legítimas de las fraudulentas, manteniendo un rango y densidad similar.\n",
    "# Entonces eliminamos ambas variables.\n",
    "\n",
    "df = df.drop([\"Time\", \"Amount\"], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style = \"color:darkorange\">6. Kmeans con un conjunto de datos de dos dimensiones</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Antes de comenzar con el entrenamiento de KMEANS para todos los atributos del conjunto de datos, se realiza una prueba para dos atributos con el objetivo de comprender como construye el límite de decisión.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V10</th>\n",
       "      <th>V14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.311169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.166974</td>\n",
       "      <td>-0.143772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.207643</td>\n",
       "      <td>-0.165946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.054952</td>\n",
       "      <td>-0.287924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.753074</td>\n",
       "      <td>-1.119670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>4.356170</td>\n",
       "      <td>4.626942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>-0.975926</td>\n",
       "      <td>-0.675143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>-0.484782</td>\n",
       "      <td>-0.510602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>-0.399126</td>\n",
       "      <td>0.449624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>-0.915427</td>\n",
       "      <td>-0.084316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             V10       V14\n",
       "0       0.090794 -0.311169\n",
       "1      -0.166974 -0.143772\n",
       "2       0.207643 -0.165946\n",
       "3      -0.054952 -0.287924\n",
       "4       0.753074 -1.119670\n",
       "...          ...       ...\n",
       "284802  4.356170  4.626942\n",
       "284803 -0.975926 -0.675143\n",
       "284804 -0.484782 -0.510602\n",
       "284805 -0.399126  0.449624\n",
       "284806 -0.915427 -0.084316\n",
       "\n",
       "[284807 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[[\"V10\", \"V14\"]].copy()\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generamos los clusters para nuestro conjunto de datos sin etiquetar\n",
    "\n",
    "# Para entrenar nuestro algoritmo de clustering, importamos nuestro estimador KMeans de sklearn.cluster.\n",
    "# Instanciamos la clase KMeans en el objeto \"kmeans\" definiendo los parámetros n_clusters=5 (numero de clusters) y \n",
    "# random_state=42 (semilla aleatoria)\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=42)\n",
    "\n",
    "# Entrenamos nuestro modelo\n",
    "clusters = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizando gráficamente los clusters y la distribución de los datos en categorías\n",
    "plt.figure(figsize=(12, 6))\n",
    "plot_decision_boundaries(kmeans, X.values, df[\"Class\"].values)\n",
    "plt.xlabel(\"V10\", fontsize=14)\n",
    "plt.ylabel(\"V14\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Representación de cada uno de los clústers\n",
    "Podemos observar la cantidad de elementos que tiene cada cluster y la cantidad de transacciones fraudulentas en cada uno de los clústers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 0 has 134343 samples - 15 are malicious samples\n",
      "Label 1 has 71527 samples - 16 are malicious samples\n",
      "Label 2 has 2484 samples - 1 are malicious samples\n",
      "Label 3 has 389 samples - 317 are malicious samples\n",
      "Label 4 has 76064 samples - 143 are malicious samples\n"
     ]
    }
   ],
   "source": [
    "counter = Counter(clusters.tolist())\n",
    "bad_counter = Counter(clusters[df['Class'] == 1].tolist())\n",
    "\n",
    "for key in sorted(counter.keys()):\n",
    "    print(\"Label {0} has {1} samples - {2} are malicious samples\".format(\n",
    "        key, counter[key], bad_counter[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style = \"color:darkorange\">7. Kmeans con un conjunto de datos multidimensional</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Al tratarse de un algoritmo basado en aprendizaje no supervisado, no se requieren las etiquetas para entrenar o predecir. Hay que tener en cuenta que en este tipo de problemas se presupone que no se dispone de las etiquetas.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separamos las características de entrada de la de salida.\n",
    "X = df.drop(\"Class\", axis=1)\n",
    "y = df[\"Class\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Entrenamos nuestro algoritmo de KMeans con nuestra data completa\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=42)\n",
    "clusters = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 0 has 124575 samples - 145 are malicious samples\n",
      "Label 1 has 107374 samples - 129 are malicious samples\n",
      "Label 2 has 3562 samples - 12 are malicious samples\n",
      "Label 3 has 43256 samples - 4 are malicious samples\n",
      "Label 4 has 6040 samples - 202 are malicious samples\n"
     ]
    }
   ],
   "source": [
    "# Evaluamos los clusters y el contenido que se han formado\n",
    "counter = Counter(clusters.tolist())\n",
    "bad_counter = Counter(clusters[y == 1].tolist())\n",
    "\n",
    "for key in sorted(counter.keys()):\n",
    "    print(\"Label {0} has {1} samples - {2} are malicious samples\".format(\n",
    "        key, counter[key], bad_counter[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De los resultados anteriores, podemos ver que sin aplicar ningún tipo de métrica de evaluación, este algoritmo se ha comportado peor para nuestro conjunto de datos con todas las características de entrada que para nuestra data con 2 dimensiones.\n",
    "\n",
    "**KMeans es un algoritmo que se comporta mejor con menos características de entrada.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style = \"color:darkorange\">8. Reducción del número de características</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En muchas ocasiones Kmeans funciona mejor con un número de características bajo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplicamos selección de características con _Random Forest_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=-1,\n",
       "                       oob_score=False, random_state=42, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Utilizamos Random Forest para realizar selección de características\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Entrenamos con 50 árboles aleatorios.\n",
    "clf_rnd = RandomForestClassifier(n_estimators=50, random_state=42, n_jobs=-1)\n",
    "clf_rnd.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'V1': 0.015389404971033027,\n",
       " 'V2': 0.016407150056988305,\n",
       " 'V3': 0.01722312061737392,\n",
       " 'V4': 0.02512550413945264,\n",
       " 'V5': 0.01401712859810283,\n",
       " 'V6': 0.011296141845450042,\n",
       " 'V7': 0.02449037051612812,\n",
       " 'V8': 0.018059564597952896,\n",
       " 'V9': 0.024722916398008103,\n",
       " 'V10': 0.06811641427499372,\n",
       " 'V11': 0.053469614948197276,\n",
       " 'V12': 0.06888896739850175,\n",
       " 'V13': 0.010954897237912396,\n",
       " 'V14': 0.1358320181722068,\n",
       " 'V15': 0.010922894832491871,\n",
       " 'V16': 0.08117386226595893,\n",
       " 'V17': 0.2276567580877801,\n",
       " 'V18': 0.0421579852578569,\n",
       " 'V19': 0.013452683786719781,\n",
       " 'V20': 0.015014336892266172,\n",
       " 'V21': 0.018718499960898357,\n",
       " 'V22': 0.009675453221364604,\n",
       " 'V23': 0.007502442365373689,\n",
       " 'V24': 0.011850060925530433,\n",
       " 'V25': 0.011343766053549316,\n",
       " 'V26': 0.02018126531570017,\n",
       " 'V27': 0.014577789128218357,\n",
       " 'V28': 0.011778988133989525}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionamos las características más importantes\n",
    "feature_importances = {name: score for name, score in zip(list(df), clf_rnd.feature_importances_)}\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V17    0.227657\n",
       "V14    0.135832\n",
       "V16    0.081174\n",
       "V12    0.068889\n",
       "V10    0.068116\n",
       "V11    0.053470\n",
       "V18    0.042158\n",
       "V4     0.025126\n",
       "V9     0.024723\n",
       "V7     0.024490\n",
       "V26    0.020181\n",
       "V21    0.018718\n",
       "V8     0.018060\n",
       "V3     0.017223\n",
       "V2     0.016407\n",
       "V1     0.015389\n",
       "V20    0.015014\n",
       "V27    0.014578\n",
       "V5     0.014017\n",
       "V19    0.013453\n",
       "V24    0.011850\n",
       "V28    0.011779\n",
       "V25    0.011344\n",
       "V6     0.011296\n",
       "V13    0.010955\n",
       "V15    0.010923\n",
       "V22    0.009675\n",
       "V23    0.007502\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances_sorted = pd.Series(feature_importances).sort_values(ascending=False)\n",
    "feature_importances_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>V17</th>\n",
       "      <th>V14</th>\n",
       "      <th>V16</th>\n",
       "      <th>V12</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.207971</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.551600</td>\n",
       "      <td>0.025791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>-0.183361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>-0.121359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.684093</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>1.965775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>-0.038195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>1.991691</td>\n",
       "      <td>4.626942</td>\n",
       "      <td>1.107641</td>\n",
       "      <td>2.711941</td>\n",
       "      <td>4.356170</td>\n",
       "      <td>-1.593105</td>\n",
       "      <td>0.510632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>-0.025693</td>\n",
       "      <td>-0.675143</td>\n",
       "      <td>-0.711757</td>\n",
       "      <td>0.915802</td>\n",
       "      <td>-0.975926</td>\n",
       "      <td>-0.150189</td>\n",
       "      <td>-1.221179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>0.313502</td>\n",
       "      <td>-0.510602</td>\n",
       "      <td>0.140716</td>\n",
       "      <td>0.063119</td>\n",
       "      <td>-0.484782</td>\n",
       "      <td>0.411614</td>\n",
       "      <td>0.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>0.509928</td>\n",
       "      <td>0.449624</td>\n",
       "      <td>-0.608577</td>\n",
       "      <td>-0.962886</td>\n",
       "      <td>-0.399126</td>\n",
       "      <td>-1.933849</td>\n",
       "      <td>1.113981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>-0.660377</td>\n",
       "      <td>-0.084316</td>\n",
       "      <td>-0.302620</td>\n",
       "      <td>-0.031513</td>\n",
       "      <td>-0.915427</td>\n",
       "      <td>-1.040458</td>\n",
       "      <td>0.167430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284807 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             V17       V14       V16       V12       V10       V11       V18\n",
       "0       0.207971 -0.311169 -0.470401 -0.617801  0.090794 -0.551600  0.025791\n",
       "1      -0.114805 -0.143772  0.463917  1.065235 -0.166974  1.612727 -0.183361\n",
       "2       1.109969 -0.165946 -2.890083  0.066084  0.207643  0.624501 -0.121359\n",
       "3      -0.684093 -0.287924 -1.059647  0.178228 -0.054952 -0.226487  1.965775\n",
       "4      -0.237033 -1.119670 -0.451449  0.538196  0.753074 -0.822843 -0.038195\n",
       "...          ...       ...       ...       ...       ...       ...       ...\n",
       "284802  1.991691  4.626942  1.107641  2.711941  4.356170 -1.593105  0.510632\n",
       "284803 -0.025693 -0.675143 -0.711757  0.915802 -0.975926 -0.150189 -1.221179\n",
       "284804  0.313502 -0.510602  0.140716  0.063119 -0.484782  0.411614  0.395652\n",
       "284805  0.509928  0.449624 -0.608577 -0.962886 -0.399126 -1.933849  1.113981\n",
       "284806 -0.660377 -0.084316 -0.302620 -0.031513 -0.915427 -1.040458  0.167430\n",
       "\n",
       "[284807 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reducimos el conjunto de datos a las 7 características más importantes\n",
    "X_reduced = X[list(feature_importances_sorted.head(7).index)].copy()\n",
    "X_reduced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrenamiento de KMEANS con el conjunto de datos reducido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=5, random_state=42)\n",
    "clusters = kmeans.fit_predict(X_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 0 has 20162 samples - 45 are malicious samples\n",
      "Label 1 has 100980 samples - 90 are malicious samples\n",
      "Label 2 has 311 samples - 268 are malicious samples\n",
      "Label 3 has 116590 samples - 69 are malicious samples\n",
      "Label 4 has 46764 samples - 20 are malicious samples\n"
     ]
    }
   ],
   "source": [
    "# Evaluamos los clusters y el contenido que se han formado\n",
    "counter = Counter(clusters.tolist())\n",
    "bad_counter = Counter(clusters[y == 1].tolist())\n",
    "\n",
    "for key in sorted(counter.keys()):\n",
    "    print(\"Label {0} has {1} samples - {2} are malicious samples\".format(\n",
    "        key, counter[key], bad_counter[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Vemos que ha mejorado la capacidad o el rendimiento de predicción cuando se ha hecho la selección de características disminuyendo las características de entrada y tomando las más relevantes para el algoritmo.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style = \"color:darkorange\">9. Evaluación de los resultados</h2> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay que tener cuidado con los **conjuntos de datos desequilibrados** cuando se utilizan métricas para medir la pureza de los clusters.\n",
    "\n",
    "**Una posible solución es utilizar técnicas de equilibrado del conjunto de datos, como la generación de más ejemplos de transacciones fraudulentas o la disminución de ejemplos de transacciones legítimas.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Purity Score: 0.9990625230419197\n"
     ]
    }
   ],
   "source": [
    "# Calculamos el purity score, es importante darse cuenta de que recibe las etiquetas\n",
    "print(\"Purity Score:\", purity_score(y, clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shiloutte:  0.17631960054515786\n"
     ]
    }
   ],
   "source": [
    "# Calculamos el coeficiente de Shiloutte, es importante darse cuenta de que no le pasamos las etiquetas\n",
    "print(\"Shiloutte: \", metrics.silhouette_score(X_reduced, clusters, sample_size=10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calinski harabasz:  40537.024698689966\n"
     ]
    }
   ],
   "source": [
    "# Calculamos el Calinski harabasz score, es importante darse cuenta de que no le pasamos las etiquetas\n",
    "print(\"Calinski harabasz: \", metrics.calinski_harabasz_score(X_reduced, clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}