Seleccion de hiperparámetros y predicciones usando arboles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
    "\n",
    "#import xgboost as xgb\n",
    "#from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\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": "markdown",
   "metadata": {},
   "source": [
    "### Lectura de datos del CSV y seteo del tipo para cada columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "  <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>Sales_Contract_No</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",
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       "  <tbody>\n",
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       "      <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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>959</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>961</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>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>956</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>19</td>\n",
       "      <td>9</td>\n",
       "      <td>2014</td>\n",
       "      <td>6985</td>\n",
       "      <td>3681</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>5 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",
       "   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",
       "   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",
       "   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",
       "   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               3681  ...                                    0   \n",
       "\n",
       "   Bit_Total_Taxable_Amount_Currency_1  Bit_Total_Taxable_Amount_Currency_2  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    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",
       "   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",
       "  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",
       "[5 rows x 315 columns]"
      ]
     },
     "execution_count": 23,
     "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_train=requests.get(url_train).content\n",
    "df_train=pd.read_csv(io.StringIO(s_train.decode('utf-8')),\n",
    "\n",
    "#df = pd.read_csv('Entrenamieto_ECI_2020.csv',\n",
    "dtype={\n",
    "'ID': np.int64,\n",
    "'Pricing, Delivery_Terms_Quote_Approval': np.int64,\n",
    "'Pricing, Delivery_Terms_Approved': np.int64,\n",
    "'Bureaucratic_Code_0_Approval': np.int64,\n",
    "'Bureaucratic_Code_0_Approved': np.int64,\n",
    "'Submitted_for_Approval': np.int64,\n",
    "'Opportunity_ID': np.int64,\n",
    "#'Sales_Contract_No': np.int64,\n",
    "'Delivery_Year': np.int64,\n",
    "'Total_Power': np.int64,\n",
    "#'Price':np.float64,\n",
    "'ASP':np.float64,\n",
    "'ASP_(converted)':np.float64,\n",
    "'Total_Amount':np.float64},\n",
    "#parse_dates=[\n",
    "#'Account_Created_Date',\n",
    "#'Planned_Delivery_Start_Date',\n",
    "#'Planned_Delivery_End_Date',\n",
    "#'Opportunity_Created_Date',\n",
    "#'Last_Activity',\n",
    "#'Quote_Expiry_Date',\n",
    "#'Actual_Delivery_Date',\n",
    "#'Last_Modified_Date'\n",
    "#]\n",
    ")\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>Sales_Contract_No</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "<p>5 rows × 315 columns</p>\n",
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      ],
      "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",
       "   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",
       "   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",
       "   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",
       "   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",
       "   Bit_Total_Taxable_Amount_Currency_1  Bit_Total_Taxable_Amount_Currency_2  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    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",
       "   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",
       "   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",
       "[5 rows x 315 columns]"
      ]
     },
     "execution_count": 24,
     "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_test=requests.get(url_test).content\n",
    "df_test=pd.read_csv(io.StringIO(s_test.decode('utf-8')),\n",
    "\n",
    "#df = pd.read_csv('Entrenamieto_ECI_2020.csv',\n",
    "dtype={\n",
    "'ID': np.int64,\n",
    "'Pricing, Delivery_Terms_Quote_Approval': np.int64,\n",
    "'Pricing, Delivery_Terms_Approved': np.int64,\n",
    "'Bureaucratic_Code_0_Approval': np.int64,\n",
    "'Bureaucratic_Code_0_Approved': np.int64,\n",
    "'Submitted_for_Approval': np.int64,\n",
    "'Opportunity_ID': np.int64,\n",
    "#'Sales_Contract_No': np.int64,\n",
    "'Delivery_Year': np.int64,\n",
    "'Total_Power': np.int64,\n",
    "#'Price':np.float64,\n",
    "'ASP':np.float64,\n",
    "'ASP_(converted)':np.float64,\n",
    "'Total_Amount':np.float64},\n",
    "#parse_dates=[\n",
    "#'Account_Created_Date',\n",
    "#'Planned_Delivery_Start_Date',\n",
    "#'Planned_Delivery_End_Date',\n",
    "#'Opportunity_Created_Date',\n",
    "#'Last_Activity',\n",
    "#'Quote_Expiry_Date',\n",
    "#'Actual_Delivery_Date',\n",
    "#'Last_Modified_Date'\n",
    "#]\n",
    ")\n",
    "\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9841, 315)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1567, 315)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "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>nulls</th>\n",
       "      <th>porcentaje</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [nulls, porcentaje]\n",
       "Index: []"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faltantes=pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False),columns=['nulls'])\n",
    "filas=len(df_train)\n",
    "faltantes['porcentaje']=round(100*faltantes['nulls']/filas,2)\n",
    "faltantes[faltantes['nulls']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>nulls</th>\n",
       "      <th>porcentaje</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [nulls, porcentaje]\n",
       "Index: []"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faltantes=pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False),columns=['nulls'])\n",
    "filas=len(df_test)\n",
    "faltantes['porcentaje']=round(100*faltantes['nulls']/filas,2)\n",
    "faltantes[faltantes['nulls']!=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proceso de preparación de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separamos la variable a predecir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df4_train['Is_closed_won']=(df4_train['Stage']=='Closed Won')\n",
    "df_train['Is_closed_won']=df_train['Stage'].apply(lambda x: 1 if x=='Closed Won' else 0)\n",
    "df_train=df_train.drop(['Stage'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_train.drop(['Is_closed_won','Sales_Contract_No'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_train['Is_closed_won']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y.value_counts()#.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creamos set de entrenamiento y test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X, y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7872, 313)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proceso de selección de hiperparámetros y predicción usando Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selección de hiperparámetros, entrenamiento y validación usando Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\licha\\Miniconda3\\envs\\orgadatos\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=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, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'class_weight': ['balanced', None], 'min_samples_split': [2, 3, 4], 'min_samples_leaf': [1, 2, 3], 'max_depth': [5, 10, 15, None]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV,RandomizedSearchCV\n",
    "from sklearn import tree\n",
    "parametros = {\n",
    "    'class_weight': [\"balanced\",None],\n",
    "    'min_samples_split':[2,3,4],\n",
    "    'min_samples_leaf':[1,2,3],\n",
    "    'max_depth': [5,10,15,None]\n",
    "}\n",
    "decisionTree = tree.DecisionTreeClassifier()\n",
    "#seleccion de hiperparametros\n",
    "clf = GridSearchCV(decisionTree, parametros,scoring='neg_log_loss')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_validation =clf.predict(X_test)\n",
    "y_validation_probs = clf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado:\n",
      " accurancy: 0.7775520568816658\n",
      " log loss: 0.5259030600831148\n"
     ]
    }
   ],
   "source": [
    "print( \"Resultado:\")\n",
    "print( \" accurancy:\" , accuracy_score(y_test, y_validation) )\n",
    "print( \" log loss:\" , log_loss(y_test, y_validation_probs) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': None,\n",
       " 'max_depth': 5,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediccion usando Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs =clf.predict_proba( df_test.drop(['Sales_Contract_No','Stage'], axis=1)  )[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12172</td>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11408</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12232</td>\n",
       "      <td>0.819608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12233</td>\n",
       "      <td>0.819608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Opportunity_ID    Target\n",
       "0           12173  0.000000\n",
       "1           12172  0.363636\n",
       "2           11408  0.714286\n",
       "3           12232  0.819608\n",
       "4           12233  0.819608"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creo un dataframe con el formato ( id , precio (El precio predecido ))\n",
    "prediccion = df_test['Opportunity_ID'].to_frame()\n",
    "prediccion.insert(1,'Target', y_pred_probs , True) \n",
    "#Exportamos la prediccion en formato csv sin labels\n",
    "prediccion.to_csv(r'./DecisionTree_max_depth_5_class_weight_none_min_samples_leaf_1_min_samples_split_3.csv',index=False , header=True)\n",
    "prediccion.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proceso de selección de hiperparámetros y predicción usando Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selección de hiperparámetros,entrenamiento y validación usando Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\licha\\Miniconda3\\envs\\orgadatos\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=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='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'class_weight': ['balanced', None], 'min_samples_split': [2, 3, 4], 'min_samples_leaf': [1, 2, 3], 'max_depth': [5, 10, 15, None], 'n_estimators': [50, 60, 75, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parametros = {\n",
    "    'class_weight': [\"balanced\",None],\n",
    "    'min_samples_split':[2,3,4],\n",
    "    'min_samples_leaf':[1,2,3],\n",
    "    'max_depth': [5,10,15,None],\n",
    "    'n_estimators': [50,60,75,100]\n",
    "}\n",
    "randomForest = RandomForestClassifier()\n",
    "#seleccion de hiperparametros\n",
    "clf = GridSearchCV(randomForest, parametros,scoring='neg_log_loss')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_validation =clf.predict(X_test)\n",
    "y_validation_probs = clf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado:\n",
      " accurancy: 0.8466226510919248\n",
      " log loss: 0.3724183793669345\n"
     ]
    }
   ],
   "source": [
    "print( \"Resultado:\")\n",
    "print( \" accurancy:\" , accuracy_score(y_test, y_validation) )\n",
    "print( \" log loss:\" , log_loss(y_test, y_validation_probs) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': None,\n",
       " 'max_depth': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediccion usando Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs =clf.predict_proba( df_test.drop(['Sales_Contract_No','Stage'], axis=1)  )[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12172</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11408</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12232</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12233</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Opportunity_ID  Target\n",
       "0           12173    0.52\n",
       "1           12172    0.46\n",
       "2           11408    0.68\n",
       "3           12232    0.60\n",
       "4           12233    0.61"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creo un dataframe con el formato ( id , precio (El precio predecido ))\n",
    "prediccion = df_test['Opportunity_ID'].to_frame()\n",
    "prediccion.insert(1,'Target', y_pred_probs , True) \n",
    "#Exportamos la prediccion en formato csv sin labels\n",
    "prediccion.to_csv(r'./RandomForest_max_depth_none_class_weight_balanced_min_samples_leaf_2_min_samples_split_2_n_estimators_100.csv',index=False , header=True)\n",
    "prediccion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proceso de selección de hiperparámetros y predicción usando Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selección de hiperparámetros,entrenamiento y validación usando Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\licha\\Miniconda3\\envs\\orgadatos\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='log2', max_leaf_nodes=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='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'class_weight': ['balanced', None], 'min_samples_split': [2, 3, 4], 'min_samples_leaf': [1, 2, 3], 'max_depth': [5, 10, 15, None], 'n_estimators': [50, 60, 75, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parametros = {\n",
    "    'class_weight': [\"balanced\",None],\n",
    "    'min_samples_split':[2,3,4],\n",
    "    'min_samples_leaf':[1,2,3],\n",
    "    'max_depth': [5,10,15,None],\n",
    "    'n_estimators': [50,60,75,100],\n",
    "    #'max_features':['log2',None]\n",
    "    #'max_features':['sqrt', 'log2',None]\n",
    "}\n",
    "randomForest = RandomForestClassifier(max_features=\"log2\")\n",
    "#seleccion de hiperparametros\n",
    "clf = GridSearchCV(randomForest, parametros,scoring='neg_log_loss')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_validation =clf.predict(X_test)\n",
    "y_validation_probs = clf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado:\n",
      " accurancy: 0.8532249873031996\n",
      " log loss: 0.38403349918046614\n"
     ]
    }
   ],
   "source": [
    "print( \"Resultado:\")\n",
    "print( \" accurancy:\" , accuracy_score(y_test, y_validation) )\n",
    "print( \" log loss:\" , log_loss(y_test, y_validation_probs) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': 'balanced',\n",
       " 'max_depth': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 4,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediccion usando Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_probs =clf.predict_proba( df_test.drop(['Sales_Contract_No','Stage'], axis=1)  )[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>0.467776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12172</td>\n",
       "      <td>0.433618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11408</td>\n",
       "      <td>0.568561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12232</td>\n",
       "      <td>0.446158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12233</td>\n",
       "      <td>0.478548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Opportunity_ID    Target\n",
       "0           12173  0.467776\n",
       "1           12172  0.433618\n",
       "2           11408  0.568561\n",
       "3           12232  0.446158\n",
       "4           12233  0.478548"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creo un dataframe con el formato ( id , precio (El precio predecido ))\n",
    "prediccion = df_test['Opportunity_ID'].to_frame()\n",
    "prediccion.insert(1,'Target', y_pred_probs , True) \n",
    "#Exportamos la prediccion en formato csv sin labels\n",
    "prediccion.to_csv(r'./RandomForest_max_depth_none_class_weight_none_min_samples_leaf_1_min_samples_split_2_n_estimators_100_max_features_log2.csv',index=False , header=True)\n",
    "prediccion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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