3_predictor model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0530b8cc",
   "metadata": {},
   "source": [
    "Ideally this section should be done in front end interfaces for user input. Thus, dummy codes shall be used where applicable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6700f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#machine learning packages\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "#models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#export object\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76aeea1b",
   "metadata": {},
   "source": [
    "# load trained object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cb51d97b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = joblib.load( \"random_forest_predictor.pkl\")\n",
    "scaler = joblib.load( \"feature_scaler.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bef0a49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "71e040d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"sample_format.csv\")\n",
    "df = df.iloc[0]\n",
    "df = pd.DataFrame(df).T\n",
    "df.iloc[0] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4171c5d4",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numofdependence</th>\n",
       "      <th>education</th>\n",
       "      <th>age</th>\n",
       "      <th>household_income</th>\n",
       "      <th>work_experience</th>\n",
       "      <th>years_home_owned</th>\n",
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      "text/plain": [
       "       numofdependence  education       age  household_income  \\\n",
       "0            -0.432175  -0.375479 -0.188866         -0.428969   \n",
       "1             1.908579   1.502083  0.445794          3.172327   \n",
       "2            -0.432175   1.502083 -0.717749         -0.118703   \n",
       "3             0.348077   1.502083  0.551570          1.787213   \n",
       "4             1.908579  -0.375479 -0.506196         -0.539778   \n",
       "...                ...        ...       ...               ...   \n",
       "67339        -1.212426  -0.375479 -1.458185         -0.151946   \n",
       "67340         1.128328   1.502083 -0.611972         -0.428969   \n",
       "67341         1.128328  -0.375479 -0.400419          0.125077   \n",
       "67342         0.348077   1.502083  0.022687         -0.712917   \n",
       "67343         0.348077  -0.375479  0.974676         -0.151946   \n",
       "\n",
       "       work_experience  years_home_owned  Female  Male  DIVORCE  MARRIED  \\\n",
       "0            -0.067164         -0.151991       0     1        0        1   \n",
       "1            -0.033388          1.965045       0     1        0        1   \n",
       "2            -0.089681         -1.092895       0     1        0        1   \n",
       "3             0.022906         -0.465626       0     1        0        1   \n",
       "4            -0.078423         -0.936078       1     0        0        1   \n",
       "...                ...               ...     ...   ...      ...      ...   \n",
       "67339        -0.100940          0.553688       1     0        0        0   \n",
       "67340        -0.067164         -1.249713       1     0        0        1   \n",
       "67341         0.045423          1.337775       1     0        0        1   \n",
       "67342        -0.044647         -0.387217       1     0        0        1   \n",
       "67343         0.000388          2.357088       0     1        0        1   \n",
       "\n",
       "       SINGLE  WIDOWED  Director  Manager  Staff  Supervisor  self-employed  \n",
       "0           0        0         0        0      0           0              1  \n",
       "1           0        0         0        0      1           0              0  \n",
       "2           0        0         0        0      0           0              1  \n",
       "3           0        0         0        0      0           0              1  \n",
       "4           0        0         0        0      0           0              1  \n",
       "...       ...      ...       ...      ...    ...         ...            ...  \n",
       "67339       1        0         0        0      0           0              1  \n",
       "67340       0        0         0        0      1           0              0  \n",
       "67341       0        0         0        0      0           0              1  \n",
       "67342       0        0         0        0      1           0              0  \n",
       "67343       0        0         0        0      0           0              1  \n",
       "\n",
       "[67344 rows x 17 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.read_csv(\"sample_format.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a156d9f",
   "metadata": {},
   "source": [
    "# take in user input\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1f618036",
   "metadata": {},
   "outputs": [],
   "source": [
    "#through portal, this will be the interface with the teller. Each variable will prompt the teller to manually\n",
    "#key in the information of the loan applicant \n",
    "def user_input(str):\n",
    "    #command take in user's input\n",
    "    #command to sort it into the next cell in the row\n",
    "    return str\n",
    "\n",
    "gender = user_input(\"text\")\n",
    "maritalstatus = user_input(\"text\")\n",
    "numofdependence = user_input(\"number\")\n",
    "education = user_input(\"number\")\n",
    "jobpos = user_input(\"number\")\n",
    "age = user_input(\"number\")\n",
    "household_income  = user_input(\"number\")\n",
    "work_experience = user_input(\"number\")\n",
    "years_home_owned = user_input(\"number\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c8173ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#assuming user input correct value the following should be variables \n",
    "gender = \"Male\"\n",
    "maritalstatus = \"SINGLE\"\n",
    "numofdependence = 1\n",
    "education = 4\n",
    "jobpos = \"Staff\"\n",
    "age = 33\n",
    "household_income  = 120000\n",
    "work_experience = 12\n",
    "years_home_owned = 5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d43001d",
   "metadata": {},
   "source": [
    "# Structure data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e3866444",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sort value into place and fill empty cell with 0\n",
    "df[\"numofdependence\"] = numofdependence\n",
    "df['education'] = education\n",
    "df[\"age\"] = age\n",
    "df[\"household_income\"] = household_income\n",
    "df['work_experience'] = work_experience\n",
    "df[\"years_home_owned\"] = years_home_owned\n",
    "df[gender] = 1\n",
    "df[maritalstatus] = 1\n",
    "df[jobpos] = 1\n",
    "df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80d9133",
   "metadata": {},
   "source": [
    "# data preprossing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "58f30cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_columns = df.columns[0:6]\n",
    "df[numeric_columns] = scaler.transform(df[numeric_columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e665dee",
   "metadata": {},
   "source": [
    "# prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "988c9305",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan approved\n"
     ]
    }
   ],
   "source": [
    "if predictor.predict(df) == 0:\n",
    "    print(\"loan approved\")\n",
    "else:\n",
    "    print(\"loan not approved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75e26276",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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