Give_Me_Some_Credit_Test_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Give me some credit: predict one input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.180982Z",
     "start_time": "2019-11-15T14:52:10.170530Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.199847Z",
     "start_time": "2019-11-15T14:52:10.191606Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier() # this class contains the implementation of the Random Forest learning technique for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.223774Z",
     "start_time": "2019-11-15T14:52:10.210823Z"
    }
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "model = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.241784Z",
     "start_time": "2019-11-15T14:52:10.233258Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib\n",
    "from joblib import dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.507441Z",
     "start_time": "2019-11-15T14:52:10.248289Z"
    }
   },
   "outputs": [],
   "source": [
    "val_prepared = pd.read_csv('csv/val_sk.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.552843Z",
     "start_time": "2019-11-15T14:52:10.524182Z"
    }
   },
   "outputs": [],
   "source": [
    "val_prepared.head()\n",
    "val_prepared = val_prepared.iloc[0:0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.585093Z",
     "start_time": "2019-11-15T14:52:10.558056Z"
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   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
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       "      <th>NumberOfDependents</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>Senior</th>\n",
       "      <th>IncomePerPerson</th>\n",
       "      <th>CombinedDefaulted</th>\n",
       "      <th>CombinedCreditLoans</th>\n",
       "      <th>MonthlyDebt</th>\n",
       "      <th>MonthlyBalance</th>\n",
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       "Empty DataFrame\n",
       "Columns: [RevolvingUtilizationOfUnsecuredLines, age, NumberOfTime30-59DaysPastDueNotWorse, DebtRatio, MonthlyIncome, NumberOfOpenCreditLinesAndLoans, NumberOfTimes90DaysLate, NumberRealEstateLoansOrLines, NumberOfTime60-89DaysPastDueNotWorse, NumberOfDependents, SeriousDlqin2yrs, Senior, IncomePerPerson, CombinedDefaulted, CombinedCreditLoans, MonthlyDebt, MonthlyBalance]\n",
       "Index: []"
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     "execution_count": 235,
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    "val_prepared.head()"
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   "cell_type": "code",
   "execution_count": 236,
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   "outputs": [],
   "source": [
    "data = {\n",
    "    \"RevolvingUtilizationOfUnsecuredLines\": 0.01703559,\n",
    "    \"NumberOfDependents\": 1,\n",
    "    \"DebtRatio\": 0,\n",
    "    \"MonthlyIncome\": 3000,\n",
    "    \"age\": 42,\n",
    "    \"NumberOfOpenCreditLinesAndLoans\": 6,\n",
    "    \"NumberRealEstateLoansOrLines\": 1,\n",
    "    \"NumberOfTime30-59DaysPastDueNotWorse\": 1,\n",
    "    \"NumberOfTime60-89DaysPastDueNotWorse\": 0,\n",
    "    \"NumberOfTimes90DaysLate\": 0\n",
    "}\n",
    "\n",
    "\n",
    "val_prepared = val_prepared.append([data])"
   ]
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       "      <th>MonthlyIncome</th>\n",
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       "      <th>NumberRealEstateLoansOrLines</th>\n",
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       "      <th>NumberOfDependents</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>Senior</th>\n",
       "      <th>IncomePerPerson</th>\n",
       "      <th>CombinedDefaulted</th>\n",
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       "   RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "0                              0.017036  42.0   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     1        0.0         3000.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                6                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  SeriousDlqin2yrs  Senior  IncomePerPerson  \\\n",
       "0                   1               NaN     NaN              NaN   \n",
       "\n",
       "   CombinedDefaulted  CombinedCreditLoans  MonthlyDebt  MonthlyBalance  \n",
       "0                NaN                  NaN          NaN             NaN  "
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     "execution_count": 237,
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   ],
   "source": [
    "val_prepared.head()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traitement des features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
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     "end_time": "2019-11-15T14:52:10.788191Z",
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   "outputs": [],
   "source": [
    "    # Age\n",
    "    val_prepared.loc[val_prepared.age > 65, 'Senior'] = '1' \n",
    "    val_prepared.loc[val_prepared.age <= 65, 'Senior'] = '0'\n",
    "    age_working = val_prepared.loc[(val_prepared[\"age\"] >= 18) & (val_prepared[\"age\"] < 65)]\n",
    "    age_senior = val_prepared.loc[(val_prepared[\"age\"] >= 65)]\n",
    "    age_working_impute = age_working.MonthlyIncome.mean()\n",
    "    age_senior_impute = age_senior.MonthlyIncome.mean()\n",
    "    \n",
    "    # MonthlyIncome\n",
    "    val_prepared[\"MonthlyIncome\"] = val_prepared[\"MonthlyIncome\"].fillna(99999)\n",
    "    val_prepared.loc[((df[\"age\"] >= 18) & (val_prepared[\"age\"] < 65)) & (val_prepared[\"MonthlyIncome\"] == 99999), \"MonthlyIncome\"] = age_working_impute\n",
    "    val_prepared.loc[(df[\"age\"] >= 65) & (val_prepared[\"MonthlyIncome\"] == 99999), \"MonthlyIncome\"] = age_senior_impute\n",
    "    \n",
    "    # Income Per Person\n",
    "    val_prepared['IncomePerPerson'] = val_prepared['MonthlyIncome'] / ( val_prepared['NumberOfDependents'] + 1 )\n",
    "    \n",
    "    # Defaults\n",
    "    val_prepared['CombinedDefaulted'] = (val_prepared['NumberOfTime30-59DaysPastDueNotWorse'] + \\\n",
    "                               val_prepared['NumberOfTime60-89DaysPastDueNotWorse'] + \\\n",
    "                               val_prepared['NumberOfTimes90DaysLate'])\n",
    "        \n",
    "    # Credit & Loans\n",
    "    val_prepared[\"CombinedCreditLoans\"] = val_prepared[\"NumberOfOpenCreditLinesAndLoans\"] + \\\n",
    "                                val_prepared[\"NumberRealEstateLoansOrLines\"]\n",
    "    \n",
    "    # Monthly Debt\n",
    "    val_prepared['MonthlyDebt'] = val_prepared['DebtRatio'] * val_prepared['MonthlyIncome']\n",
    "    \n",
    "    # Monthly Balance\n",
    "    val_prepared['MonthlyBalance'] = val_prepared['MonthlyIncome'] - val_prepared['MonthlyDebt']\n",
    "    \n",
    "    # Dependents\n",
    "    val_prepared['NumberOfDependents'] = val_prepared['NumberOfDependents'].fillna(0)\n",
    "    val_prepared[\"NumberOfDependents\"] = val_prepared[\"NumberOfDependents\"].astype('int64')\n",
    "    \n",
    "    val_prepared = val_prepared.fillna(0)\n"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>Senior</th>\n",
       "      <th>IncomePerPerson</th>\n",
       "      <th>CombinedDefaulted</th>\n",
       "      <th>CombinedCreditLoans</th>\n",
       "      <th>MonthlyDebt</th>\n",
       "      <th>MonthlyBalance</th>\n",
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       "   RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "0                              0.017036  42.0   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     1        0.0         3000.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                6                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  SeriousDlqin2yrs Senior  IncomePerPerson  \\\n",
       "0                   1               0.0      0           1500.0   \n",
       "\n",
       "   CombinedDefaulted  CombinedCreditLoans  MonthlyDebt  MonthlyBalance  \n",
       "0                  1                    7          0.0          3000.0  "
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load model from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.892416Z",
     "start_time": "2019-11-15T14:52:10.851816Z"
    }
   },
   "outputs": [],
   "source": [
    "# load the model from disk\n",
    "loaded_model = pickle.load(open(filename, 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply model to new set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.937833Z",
     "start_time": "2019-11-15T14:52:10.896007Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.04880878]\n"
     ]
    }
   ],
   "source": [
    "target_column = 'SeriousDlqin2yrs'\n",
    "\n",
    "X_val = val_prepared.drop(target_column, axis=1).values\n",
    "y_val = val_prepared[target_column].values\n",
    "\n",
    "y_val_proba = loaded_model.predict_proba(X_val)\n",
    "y_val_proba = y_val_proba[:,1]\n",
    "\n",
    "print(y_val_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T14:52:10.973610Z",
     "start_time": "2019-11-15T14:52:10.943781Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notre modèle estime la probabilité de défaut de remboursement du client à : 4.880878329277039% \n"
     ]
    }
   ],
   "source": [
    "print(f\"Notre modèle estime la probabilité de défaut de remboursement du client à : {y_val_proba[0] * 100}% \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-15T15:03:39.717377Z",
     "start_time": "2019-11-15T15:03:39.708119Z"
    }
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-243-d3b02bc48ab9>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-243-d3b02bc48ab9>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    c2 <- chisq.test(df$MonthlyIncome, output_vector)\u001b[0m\n\u001b[0m                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "c2 <- chisq.test(df$MonthlyIncome, output_vector)\n",
    "print(c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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