EjercicioKNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from numpy import mean,abs,sqrt\n",
    "from seaborn import lineplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfAds = pd.read_csv('Advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfAds.shape[0]\n",
    "#dfAds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfAds[[\"TV\",\"radio\",\"newspaper\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = dfAds[\"sales\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.279974999999997"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "knn = KNeighborsRegressor(n_neighbors=200)\n",
    "knn = knn.fit(X, Y)\n",
    "valoresPre = knn.predict(X)\n",
    "absoluto = abs(valoresPre - Y)\n",
    "mean(absoluto)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "errorDF = pd.DataFrame(error,columns={\"Error\"})\n",
    "errorDF[\"MaxDepth\"] = range(1,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Error</th>\n",
       "      <th>MaxDepth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.043774</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.851052</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.296229</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.820382</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.520068</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.313894</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.168117</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.068383</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.021767</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.005000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Error  MaxDepth\n",
       "0   3.043774         1\n",
       "1   1.851052         2\n",
       "2   1.296229         3\n",
       "3   0.820382         4\n",
       "4   0.520068         5\n",
       "5   0.313894         6\n",
       "6   0.168117         7\n",
       "7   0.068383         8\n",
       "8   0.021767         9\n",
       "9   0.005000        10\n",
       "10  0.001000        11\n",
       "11  0.000500        12\n",
       "12  0.000000        13\n",
       "13  0.000000        14"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errorDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MaxDepth', ylabel='Error'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lineplot(data=errorDF, x=\"MaxDepth\", y=\"Error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17.03504274])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[230.1,37.8,69.2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_80=0.80\n",
    "\n",
    "errorDF['t_train'] = np.random.uniform(0, 1, len(errorDF)) <=df_80\n",
    "train, test = errorDF[errorDF['t_train']==True], errorDF[errorDF['t_train']==False]\n",
    "df_2 = errorDF.drop('t_train', 1)\n",
    "\n",
    "columns=['TV','radio','newspaper']\n",
    "df_sales_train=train['sales']\n",
    "df_sales_test=test['sales']\n",
    "\n",
    "X1=train[columns]\n",
    "X2=test[columns]\n",
    "y1=df_sales_train\n",
    "y2=df_sales_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rango = []\n",
    "Errores=[]\n",
    "e=range(1,20)\n",
    "for i in e:\n",
    "  neigh_model = KNeighborsRegressor(n_neighbors=i)\n",
    "  neigh_model = neigh_model.fit(X1, y1)\n",
    "  #train fit\n",
    "  #test predict\n",
    "  predicciones = neigh_model.predict(X2)\n",
    "  Errores.append(np.mean(abs(predicciones-y2)))\n",
    "rango.append(i)\n",
    "\n",
    "\n",
    "dtf=pd.DataFrame(Errores, columns=['Error'])\n",
    "\n",
    "\n",
    "dtf['Rango']=e\n",
    "dtf"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "fb698bdeab99cfbf9e31cab566d99a06e057f2dec3dcc8097ebabb329e860e16"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit ('base': conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}