KNN train_preprocesado.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.legend import Legend\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "#from statsmodels.graphics.mosaicplot import mosaic\n",
    "import io\n",
    "import requests\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pricing, Delivery_Terms_Quote_Appr</th>\n",
       "      <th>Pricing, Delivery_Terms_Approved</th>\n",
       "      <th>Bureaucratic_Code_0_Approval</th>\n",
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       "      <th>Sales_Contract_No</th>\n",
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       "      <th>Bit_Total_Taxable_Amount_Currency_3</th>\n",
       "      <th>Bit_Prod_Category_A_0</th>\n",
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       "      <th>Bit_ASP_Currency_1</th>\n",
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       "      Pricing, Delivery_Terms_Quote_Appr  Pricing, Delivery_Terms_Approved  \\\n",
       "0                                      0                                 0   \n",
       "1                                      0                                 0   \n",
       "2                                      0                                 0   \n",
       "3                                      0                                 0   \n",
       "4                                      0                                 0   \n",
       "...                                  ...                               ...   \n",
       "9836                                   1                                 1   \n",
       "9837                                   1                                 1   \n",
       "9838                                   1                                 1   \n",
       "9839                                   1                                 1   \n",
       "9840                                   1                                 1   \n",
       "\n",
       "      Bureaucratic_Code_0_Approval  Bureaucratic_Code_0_Approved  \\\n",
       "0                                0                             0   \n",
       "1                                0                             0   \n",
       "2                                0                             0   \n",
       "3                                0                             0   \n",
       "4                                0                             0   \n",
       "...                            ...                           ...   \n",
       "9836                             1                             1   \n",
       "9837                             1                             1   \n",
       "9838                             1                             1   \n",
       "9839                             1                             1   \n",
       "9840                             1                             1   \n",
       "\n",
       "      Submitted_for_Approval  Account_Created_Date_Day  \\\n",
       "0                          0                         1   \n",
       "1                          0                         1   \n",
       "2                          0                         1   \n",
       "3                          0                         1   \n",
       "4                          0                        19   \n",
       "...                      ...                       ...   \n",
       "9836                       0                        24   \n",
       "9837                       0                        28   \n",
       "9838                       0                        28   \n",
       "9839                       0                         3   \n",
       "9840                       0                        13   \n",
       "\n",
       "      Account_Created_Date_Month  Account_Created_Date_Year  Opportunity_ID  \\\n",
       "0                              3                       2017             960   \n",
       "1                              3                       2017             959   \n",
       "2                              3                       2017             961   \n",
       "3                              3                       2017             956   \n",
       "4                              9                       2014            6985   \n",
       "...                          ...                        ...             ...   \n",
       "9836                           1                       2017             535   \n",
       "9837                          10                       2018            9968   \n",
       "9838                          10                       2018           10157   \n",
       "9839                           7                       2018            8740   \n",
       "9840                          10                       2016            3257   \n",
       "\n",
       "      Sales_Contract_No  ...  Bit_Total_Taxable_Amount_Currency_0  \\\n",
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       "0                                       0   \n",
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       "...                                   ...   \n",
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       "9838                                    0   \n",
       "9839                                    0   \n",
       "9840                                    0   \n",
       "\n",
       "      Bit_Total_Taxable_Amount_Currency_2  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "...                                   ...   \n",
       "9836                                    0   \n",
       "9837                                    0   \n",
       "9838                                    0   \n",
       "9839                                    0   \n",
       "9840                                    0   \n",
       "\n",
       "      Bit_Total_Taxable_Amount_Currency_3  Bit_Prod_Category_A_0  \\\n",
       "0                                       1                      1   \n",
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       "2                                       1                      1   \n",
       "3                                       1                      1   \n",
       "4                                       1                      1   \n",
       "...                                   ...                    ...   \n",
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       "9839                                    1                      1   \n",
       "9840                                    1                      1   \n",
       "\n",
       "      Bit_ASP_Currency_0  Bit_ASP_Currency_1  Bit_ASP_Currency_2  \\\n",
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       "\n",
       "     Bit_ASP_Currency_3  Bit_ASP_(converted)_Currency_0  \n",
       "0                     1                               1  \n",
       "1                     1                               1  \n",
       "2                     1                               1  \n",
       "3                     1                               1  \n",
       "4                     1                               1  \n",
       "...                 ...                             ...  \n",
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       "\n",
       "[9841 rows x 315 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_train=\"https://raw.githubusercontent.com/joacols/TP_orga_de_datos/main/Train_Preprocesado_TP2_Datos_2020-2C-encodeado.csv\"\n",
    "s=requests.get(url_train).content\n",
    "df_train=pd.read_csv(io.StringIO(s.decode('utf-8')))\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pricing, Delivery_Terms_Quote_Appr</th>\n",
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       "      <th>Bit_Total_Taxable_Amount_Currency_2</th>\n",
       "      <th>Bit_Total_Taxable_Amount_Currency_3</th>\n",
       "      <th>Bit_Prod_Category_A_0</th>\n",
       "      <th>Bit_ASP_Currency_0</th>\n",
       "      <th>Bit_ASP_Currency_1</th>\n",
       "      <th>Bit_ASP_Currency_2</th>\n",
       "      <th>Bit_ASP_Currency_3</th>\n",
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       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "      <td>12172</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "      <td>11408</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>12232</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>12233</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1562</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>2017</td>\n",
       "      <td>11758</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>10721</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>11757</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>10851</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>10880</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1567 rows × 315 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pricing, Delivery_Terms_Quote_Appr  Pricing, Delivery_Terms_Approved  \\\n",
       "0                                      0                                 0   \n",
       "1                                      0                                 0   \n",
       "2                                      0                                 0   \n",
       "3                                      0                                 0   \n",
       "4                                      0                                 0   \n",
       "...                                  ...                               ...   \n",
       "1562                                   1                                 1   \n",
       "1563                                   1                                 1   \n",
       "1564                                   1                                 1   \n",
       "1565                                   1                                 1   \n",
       "1566                                   1                                 1   \n",
       "\n",
       "      Bureaucratic_Code_0_Approval  Bureaucratic_Code_0_Approved  \\\n",
       "0                                0                             0   \n",
       "1                                0                             0   \n",
       "2                                0                             0   \n",
       "3                                0                             0   \n",
       "4                                0                             0   \n",
       "...                            ...                           ...   \n",
       "1562                             1                             1   \n",
       "1563                             0                             0   \n",
       "1564                             1                             1   \n",
       "1565                             1                             1   \n",
       "1566                             1                             1   \n",
       "\n",
       "      Submitted_for_Approval  Account_Created_Date_Day  \\\n",
       "0                          0                        20   \n",
       "1                          0                        22   \n",
       "2                          0                        22   \n",
       "3                          0                         4   \n",
       "4                          0                         4   \n",
       "...                      ...                       ...   \n",
       "1562                       0                        21   \n",
       "1563                       0                        17   \n",
       "1564                       0                        15   \n",
       "1565                       0                        28   \n",
       "1566                       0                        28   \n",
       "\n",
       "      Account_Created_Date_Month  Account_Created_Date_Year  Opportunity_ID  \\\n",
       "0                              2                       2017           12173   \n",
       "1                              8                       2018           12172   \n",
       "2                              8                       2018           11408   \n",
       "3                              4                       2019           12232   \n",
       "4                              4                       2019           12233   \n",
       "...                          ...                        ...             ...   \n",
       "1562                           8                       2017           11758   \n",
       "1563                          10                       2018           10721   \n",
       "1564                          10                       2018           11757   \n",
       "1565                          12                       2017           10851   \n",
       "1566                          12                       2017           10880   \n",
       "\n",
       "      Sales_Contract_No  ...  Bit_Total_Taxable_Amount_Currency_0  \\\n",
       "0                     0  ...                                    0   \n",
       "1                     0  ...                                    0   \n",
       "2                     0  ...                                    0   \n",
       "3                     0  ...                                    0   \n",
       "4                     0  ...                                    0   \n",
       "...                 ...  ...                                  ...   \n",
       "1562                  0  ...                                    0   \n",
       "1563                  0  ...                                    0   \n",
       "1564                  0  ...                                    0   \n",
       "1565                  0  ...                                    0   \n",
       "1566                  0  ...                                    0   \n",
       "\n",
       "      Bit_Total_Taxable_Amount_Currency_1  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "...                                   ...   \n",
       "1562                                    0   \n",
       "1563                                    0   \n",
       "1564                                    0   \n",
       "1565                                    0   \n",
       "1566                                    0   \n",
       "\n",
       "      Bit_Total_Taxable_Amount_Currency_2  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "...                                   ...   \n",
       "1562                                    0   \n",
       "1563                                    0   \n",
       "1564                                    0   \n",
       "1565                                    0   \n",
       "1566                                    0   \n",
       "\n",
       "      Bit_Total_Taxable_Amount_Currency_3  Bit_Prod_Category_A_0  \\\n",
       "0                                       1                      1   \n",
       "1                                       1                      1   \n",
       "2                                       1                      1   \n",
       "3                                       1                      1   \n",
       "4                                       1                      1   \n",
       "...                                   ...                    ...   \n",
       "1562                                    1                      1   \n",
       "1563                                    1                      1   \n",
       "1564                                    1                      1   \n",
       "1565                                    1                      1   \n",
       "1566                                    1                      1   \n",
       "\n",
       "      Bit_ASP_Currency_0  Bit_ASP_Currency_1  Bit_ASP_Currency_2  \\\n",
       "0                      0                   0                   0   \n",
       "1                      0                   0                   0   \n",
       "2                      0                   0                   0   \n",
       "3                      0                   0                   0   \n",
       "4                      0                   0                   0   \n",
       "...                  ...                 ...                 ...   \n",
       "1562                   0                   0                   0   \n",
       "1563                   0                   0                   0   \n",
       "1564                   0                   0                   0   \n",
       "1565                   0                   0                   0   \n",
       "1566                   0                   0                   0   \n",
       "\n",
       "      Bit_ASP_Currency_3  Bit_ASP_(converted)_Currency_0  \n",
       "0                      1                               1  \n",
       "1                      1                               1  \n",
       "2                      1                               1  \n",
       "3                      1                               1  \n",
       "4                      1                               1  \n",
       "...                  ...                             ...  \n",
       "1562                   1                               1  \n",
       "1563                   1                               1  \n",
       "1564                   1                               1  \n",
       "1565                   1                               1  \n",
       "1566                   1                               1  \n",
       "\n",
       "[1567 rows x 315 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_test=\"https://raw.githubusercontent.com/joacols/TP_orga_de_datos/main/Test_Preprocesado_TP2_Datos_2020-2C-encodeado.csv\"\n",
    "s=requests.get(url_test).content\n",
    "df_test=pd.read_csv(io.StringIO(s.decode('utf-8')))\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pricing, Delivery_Terms_Quote_Appr    int64\n",
       "Pricing, Delivery_Terms_Approved      int64\n",
       "Bureaucratic_Code_0_Approval          int64\n",
       "Bureaucratic_Code_0_Approved          int64\n",
       "Submitted_for_Approval                int64\n",
       "                                      ...  \n",
       "Bit_ASP_Currency_0                    int64\n",
       "Bit_ASP_Currency_1                    int64\n",
       "Bit_ASP_Currency_2                    int64\n",
       "Bit_ASP_Currency_3                    int64\n",
       "Bit_ASP_(converted)_Currency_0        int64\n",
       "Length: 315, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pricing, Delivery_Terms_Quote_Appr    int64\n",
       "Pricing, Delivery_Terms_Approved      int64\n",
       "Bureaucratic_Code_0_Approval          int64\n",
       "Bureaucratic_Code_0_Approved          int64\n",
       "Submitted_for_Approval                int64\n",
       "                                      ...  \n",
       "Bit_ASP_Currency_0                    int64\n",
       "Bit_ASP_Currency_1                    int64\n",
       "Bit_ASP_Currency_2                    int64\n",
       "Bit_ASP_Currency_3                    int64\n",
       "Bit_ASP_(converted)_Currency_0        int64\n",
       "Length: 315, dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=df_train.drop(['Stage','Sales_Contract_No'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['Is_Closed_Won']=df_train.Stage.str.contains('Closed Won')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     5072\n",
       "False    4769\n",
       "Name: Is_Closed_Won, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Is_Closed_Won'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=df_train['Is_Closed_Won']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=df_test.drop(['Stage','Sales_Contract_No'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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",
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       "\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>Pricing, Delivery_Terms_Quote_Appr</th>\n",
       "      <th>Pricing, Delivery_Terms_Approved</th>\n",
       "      <th>Bureaucratic_Code_0_Approval</th>\n",
       "      <th>Bureaucratic_Code_0_Approved</th>\n",
       "      <th>Submitted_for_Approval</th>\n",
       "      <th>Account_Created_Date_Day</th>\n",
       "      <th>Account_Created_Date_Month</th>\n",
       "      <th>Account_Created_Date_Year</th>\n",
       "      <th>Opportunity_ID</th>\n",
       "      <th>Opportunity_Created_Date_Day</th>\n",
       "      <th>...</th>\n",
       "      <th>Bit_Total_Taxable_Amount_Currency_0</th>\n",
       "      <th>Bit_Total_Taxable_Amount_Currency_1</th>\n",
       "      <th>Bit_Total_Taxable_Amount_Currency_2</th>\n",
       "      <th>Bit_Total_Taxable_Amount_Currency_3</th>\n",
       "      <th>Bit_Prod_Category_A_0</th>\n",
       "      <th>Bit_ASP_Currency_0</th>\n",
       "      <th>Bit_ASP_Currency_1</th>\n",
       "      <th>Bit_ASP_Currency_2</th>\n",
       "      <th>Bit_ASP_Currency_3</th>\n",
       "      <th>Bit_ASP_(converted)_Currency_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>12173</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "      <td>12172</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "      <td>11408</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>12232</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>12233</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1562</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>2017</td>\n",
       "      <td>11758</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>10721</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>11757</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>10851</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>10880</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1567 rows × 313 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pricing, Delivery_Terms_Quote_Appr  Pricing, Delivery_Terms_Approved  \\\n",
       "0                                      0                                 0   \n",
       "1                                      0                                 0   \n",
       "2                                      0                                 0   \n",
       "3                                      0                                 0   \n",
       "4                                      0                                 0   \n",
       "...                                  ...                               ...   \n",
       "1562                                   1                                 1   \n",
       "1563                                   1                                 1   \n",
       "1564                                   1                                 1   \n",
       "1565                                   1                                 1   \n",
       "1566                                   1                                 1   \n",
       "\n",
       "      Bureaucratic_Code_0_Approval  Bureaucratic_Code_0_Approved  \\\n",
       "0                                0                             0   \n",
       "1                                0                             0   \n",
       "2                                0                             0   \n",
       "3                                0                             0   \n",
       "4                                0                             0   \n",
       "...                            ...                           ...   \n",
       "1562                             1                             1   \n",
       "1563                             0                             0   \n",
       "1564                             1                             1   \n",
       "1565                             1                             1   \n",
       "1566                             1                             1   \n",
       "\n",
       "      Submitted_for_Approval  Account_Created_Date_Day  \\\n",
       "0                          0                        20   \n",
       "1                          0                        22   \n",
       "2                          0                        22   \n",
       "3                          0                         4   \n",
       "4                          0                         4   \n",
       "...                      ...                       ...   \n",
       "1562                       0                        21   \n",
       "1563                       0                        17   \n",
       "1564                       0                        15   \n",
       "1565                       0                        28   \n",
       "1566                       0                        28   \n",
       "\n",
       "      Account_Created_Date_Month  Account_Created_Date_Year  Opportunity_ID  \\\n",
       "0                              2                       2017           12173   \n",
       "1                              8                       2018           12172   \n",
       "2                              8                       2018           11408   \n",
       "3                              4                       2019           12232   \n",
       "4                              4                       2019           12233   \n",
       "...                          ...                        ...             ...   \n",
       "1562                           8                       2017           11758   \n",
       "1563                          10                       2018           10721   \n",
       "1564                          10                       2018           11757   \n",
       "1565                          12                       2017           10851   \n",
       "1566                          12                       2017           10880   \n",
       "\n",
       "      Opportunity_Created_Date_Day  ...  Bit_Total_Taxable_Amount_Currency_0  \\\n",
       "0                                4  ...                                    0   \n",
       "1                                4  ...                                    0   \n",
       "2                               11  ...                                    0   \n",
       "3                                9  ...                                    0   \n",
       "4                                9  ...                                    0   \n",
       "...                            ...  ...                                  ...   \n",
       "1562                             5  ...                                    0   \n",
       "1563                            14  ...                                    0   \n",
       "1564                             5  ...                                    0   \n",
       "1565                            21  ...                                    0   \n",
       "1566                            23  ...                                    0   \n",
       "\n",
       "      Bit_Total_Taxable_Amount_Currency_1  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "...                                   ...   \n",
       "1562                                    0   \n",
       "1563                                    0   \n",
       "1564                                    0   \n",
       "1565                                    0   \n",
       "1566                                    0   \n",
       "\n",
       "      Bit_Total_Taxable_Amount_Currency_2  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "...                                   ...   \n",
       "1562                                    0   \n",
       "1563                                    0   \n",
       "1564                                    0   \n",
       "1565                                    0   \n",
       "1566                                    0   \n",
       "\n",
       "      Bit_Total_Taxable_Amount_Currency_3  Bit_Prod_Category_A_0  \\\n",
       "0                                       1                      1   \n",
       "1                                       1                      1   \n",
       "2                                       1                      1   \n",
       "3                                       1                      1   \n",
       "4                                       1                      1   \n",
       "...                                   ...                    ...   \n",
       "1562                                    1                      1   \n",
       "1563                                    1                      1   \n",
       "1564                                    1                      1   \n",
       "1565                                    1                      1   \n",
       "1566                                    1                      1   \n",
       "\n",
       "      Bit_ASP_Currency_0  Bit_ASP_Currency_1  Bit_ASP_Currency_2  \\\n",
       "0                      0                   0                   0   \n",
       "1                      0                   0                   0   \n",
       "2                      0                   0                   0   \n",
       "3                      0                   0                   0   \n",
       "4                      0                   0                   0   \n",
       "...                  ...                 ...                 ...   \n",
       "1562                   0                   0                   0   \n",
       "1563                   0                   0                   0   \n",
       "1564                   0                   0                   0   \n",
       "1565                   0                   0                   0   \n",
       "1566                   0                   0                   0   \n",
       "\n",
       "      Bit_ASP_Currency_3  Bit_ASP_(converted)_Currency_0  \n",
       "0                      1                               1  \n",
       "1                      1                               1  \n",
       "2                      1                               1  \n",
       "3                      1                               1  \n",
       "4                      1                               1  \n",
       "...                  ...                             ...  \n",
       "1562                   1                               1  \n",
       "1563                   1                               1  \n",
       "1564                   1                               1  \n",
       "1565                   1                               1  \n",
       "1566                   1                               1  \n",
       "\n",
       "[1567 rows x 313 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = neigh.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=500)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_500 = neigh.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs_500 = neigh.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=10)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_10 = neigh.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=100)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_100 = neigh.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ..., False, False, False])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "tupla1 = pd.DataFrame(columns=('Opportunity_ID', 'Target'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla1['Opportunity_ID']=df_test['Opportunity_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla1['Target']=pd.Series(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla1['Target']=(tupla1['Target']==True).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Opportunity_ID</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12173</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12172</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11408</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1562</th>\n",
       "      <td>11758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>10721</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>11757</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>10851</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>10880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1567 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Opportunity_ID  Target\n",
       "0              12173       1\n",
       "1              12172       0\n",
       "2              11408       1\n",
       "3              12232       0\n",
       "4              12233       1\n",
       "...              ...     ...\n",
       "1562           11758       0\n",
       "1563           10721       1\n",
       "1564           11757       0\n",
       "1565           10851       0\n",
       "1566           10880       0\n",
       "\n",
       "[1567 rows x 2 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tupla1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla1.to_csv(r'./KNN_3neighbours.csv',index=False , header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "tupla2 = pd.DataFrame(columns=('Opportunity_ID', 'Target'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla2['Opportunity_ID']=df_test['Opportunity_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla2['Target']=pd.Series(y_pred_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla2['Target']=(tupla2['Target']==True).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla2.to_csv(r'./KNN_10neighbours.csv',index=False , header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "tupla3 = pd.DataFrame(columns=('Opportunity_ID', 'Target'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla3['Opportunity_ID']=df_test['Opportunity_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla3['Target']=pd.Series(y_pred_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla3['Target']=(tupla3['Target']==True).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla3.to_csv(r'./KNN_100neighbours.csv',index=False , header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_train.drop(['Stage','Sales_Contract_No'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_train['Stage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_entrenamiento, x_prueba, y_entrenamiento, y_prueba = \\\n",
    "    train_test_split(X, y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=500)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_500 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6764855256475368"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_prueba, y_pred_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1000)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_1000 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6729304215337735"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_prueba, y_pred_1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.659849024250576"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "tupla4 = pd.DataFrame(columns=('Opportunity_ID', 'Target'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla4['Opportunity_ID']=df_test['Opportunity_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Data must be 1-dimensional",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-135-ad559eb39ce0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtupla4\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Target'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred_probs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\patricio\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    325\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 327\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_cast_failure\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    328\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    329\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\patricio\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\construction.py\u001b[0m in \u001b[0;36msanitize_array\u001b[1;34m(data, index, dtype, copy, raise_cast_failure)\u001b[0m\n\u001b[0;32m    494\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0msubarr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Data must be 1-dimensional\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    497\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m             \u001b[0msubarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray_tuplesafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mException\u001b[0m: Data must be 1-dimensional"
     ]
    }
   ],
   "source": [
    "tupla4['Target']=pd.Series(y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla4['Target']=(tupla4['Target']==True).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla4.to_csv(r'./KNN_1000n.csv',index=False , header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_50 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_40 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_100 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_70 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_2000 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla5['Opportunity_ID']=df_test['Opportunity_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla5['Target']=pd.Series(y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla5['Target']=(tupla5['Target']==True).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla5.to_csv(r'./KNN_2000n.csv',index=False , header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_60 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_5000 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_45 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_55 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_57 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=53)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_53 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_53)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=54)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh.fit(x_entrenamiento, y_entrenamiento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_54 = neigh.predict(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_score(y_prueba, y_pred_54)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla3['Opportunity_ID']=df_test['Opportunity_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla3['Target']=pd.Series(y_pred_55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "tupla3['Target']=(tupla3['Target']==True).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "tupla3.to_csv(r'./KNN_55neighbours.csv',index=False , header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs = neigh.predict_proba(x_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss(y_prueba, y_pred_probs)"
   ]
  }
 ],
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