Credit_Risk_Web_App.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Credit Risk Web App.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pm9jn-SQkIhs"
      },
      "source": [
        "Running and Back testing the Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcGO5p-EkMUE",
        "outputId": "1f1acef9-8112-4443-d476-8aadb3aa72be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import pandas as pd\n",
        "from pandas import DataFrame\n",
        "from sklearn.feature_extraction import DictVectorizer\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import joblib\n",
        "from sklearn.model_selection import train_test_split, cross_validate\n",
        "from sklearn.metrics import confusion_matrix\n",
        "#Load in data\n",
        "#Simpler to have no inputs here even though  technically less dyanmic \n",
        "def data_set():\n",
        "    df = pd.read_csv(\"https://raw.githubusercontent.com/supalogix/credit_api_poc/master/cs-training.csv\")\n",
        "    df2 = df.drop( df.columns[0], axis=1)\n",
        "\n",
        "    return df2\n",
        "#Vectorize and get features of interest \n",
        "def features(data):\n",
        "    data = data.dropna()\n",
        "    data = data.to_dict(orient='records')\n",
        "    vec = DictVectorizer()\n",
        "    df_data = vec.fit_transform(data).toarray()\n",
        "    names = vec.get_feature_names()\n",
        "    df_data = DataFrame(\n",
        "        df_data,\n",
        "        columns=names)\n",
        "\n",
        "    outcome_feature = df_data['SeriousDlqin2yrs']\n",
        "    target_features = df_data.drop('SeriousDlqin2yrs', axis=1)\n",
        "\n",
        "    return outcome_feature, target_features\n",
        "\n",
        "\n",
        "#calling above functions\n",
        "data = data_set()\n",
        "outcome_feature, target_features = features(data)\n",
        "\n",
        "X1, X2, Y1, Y2 = train_test_split(target_features, outcome_feature, test_size=0.5, random_state=0)\n",
        "    \n",
        "mod = GaussianNB()\n",
        "\n",
        "#Train/Fit model\n",
        "mod.fit(X1,Y1)\n",
        "score = mod.score(X2, Y2)\n",
        "print(\"% Accuracy: {0}\".format(score.mean()))\n",
        "\n",
        "\n",
        "#Confusion matrix to check accuracy \n",
        "confusion_output = mod.predict(X2)\n",
        "\n",
        "confu_matrix = confusion_matrix(confusion_output, Y2)\n",
        "print(confu_matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "% Accuracy: 0.9294088301322025\n",
            "[[55798  4094]\n",
            " [  151    92]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgzZSP0vkQ4b"
      },
      "source": [
        "Deploying the Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkwKGtt2kXcM",
        "outputId": "2a235211-01be-4280-89d1-b9e095e750d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install pomegranate"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pomegranate\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/67/a1/17396f2bf7e00054ed00fa0667c2a28b3d71a97d4569bacc959abb458212/pomegranate-0.13.5.tar.gz (4.5MB)\n",
            "\u001b[K     |████████████████████████████████| 4.5MB 5.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from pomegranate) (1.18.5)\n",
            "Requirement already satisfied: joblib>=0.9.0b4 in /usr/local/lib/python3.6/dist-packages (from pomegranate) (0.17.0)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.6/dist-packages (from pomegranate) (2.5)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from pomegranate) (1.4.1)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from pomegranate) (3.13)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->pomegranate) (4.4.2)\n",
            "Building wheels for collected packages: pomegranate\n",
            "  Building wheel for pomegranate (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pomegranate: filename=pomegranate-0.13.5-cp36-cp36m-linux_x86_64.whl size=15238353 sha256=2e1d3a86f0e6b3714f5032fec0c2cd4ee8966f7b7aef89ed8aa4607ac13df2c8\n",
            "  Stored in directory: /root/.cache/pip/wheels/99/d9/0a/0758302ccad41a61aa08ae4985b84d4cab5086e99c92a68560\n",
            "Successfully built pomegranate\n",
            "Installing collected packages: pomegranate\n",
            "Successfully installed pomegranate-0.13.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KYojCGAfkYd0",
        "outputId": "86ba8020-d642-4768-ecb2-016bb4c3d1e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import pandas as pd\n",
        "from pomegranate import *\n",
        "\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/supalogix/credit_api_poc/master/cs-training.csv\")\n",
        "df2 = df.drop( df.columns[0], axis=1)\n",
        "\n",
        "data = df2.dropna()\n",
        "\n",
        "data = data.to_dict(orient='records')\n",
        "\n",
        "from sklearn.feature_extraction import DictVectorizer\n",
        "from pandas import DataFrame\n",
        "vec = DictVectorizer()\n",
        "df_data = vec.fit_transform(data).toarray()\n",
        "feature_names = vec.get_feature_names()\n",
        "df_data = DataFrame(\n",
        "df_data,\n",
        "columns=feature_names)\n",
        "outcome_feature = df_data['SeriousDlqin2yrs']\n",
        "target_features = df_data.drop('SeriousDlqin2yrs', axis=1)\n",
        "\n",
        "\n",
        "#from sklearn import cross_validate\n",
        "from sklearn.model_selection import train_test_split\n",
        "\"\"\"\n",
        "    X1: independent (target) variables for first data set\n",
        "    Y1: dependent (outcome) variable for first data set\n",
        "    X2: independent (target) variables for the second data set\n",
        "    Y2: dependent (outcome) variable for the second data set\n",
        "\"\"\"\n",
        "\n",
        "def our_model(DebtRatio, Monthlyincome, NumberOfDependents,\n",
        "              NumberOfOpenCreditLinesAndLoans,\n",
        "              NumberOfTime3050DaysPastDueNotWorse,\n",
        "             NumberOfTime6089DaysPastDueNotWorse,\n",
        "              NumberOfTimes90DaysLate,\n",
        "              NumberRealEstateLoansOrLines,\n",
        "              RevolvingUtilizationOfUnsecuredLines,\n",
        "              age):\n",
        "    \n",
        "    X1, X2, Y1, Y2 = train_test_split(\n",
        "    target_features, outcome_feature, test_size=0.5, random_state=0)\n",
        "                            \n",
        "    from sklearn.naive_bayes import GaussianNB\n",
        "    mod = GaussianNB()\n",
        "                          \n",
        "    mod.fit(X1,Y1)\n",
        "    \n",
        "    new_input = target_features.iloc[0:1,:]\n",
        "    new_input.iloc[:,:] = DebtRatio,Monthlyincome,NumberOfDependents,NumberOfOpenCreditLinesAndLoans,NumberOfTime3050DaysPastDueNotWorse,NumberOfTime6089DaysPastDueNotWorse,NumberOfTimes90DaysLate,NumberRealEstateLoansOrLines,RevolvingUtilizationOfUnsecuredLines,age\n",
        " \n",
        "    output = mod.predict(X2)\n",
        "    predicted_out = mod.predict(new_input)\n",
        "    \n",
        "    if predicted_out == 0.0:\n",
        "        #gives value to tell how much credit \n",
        "        return 'Approved for:', mod.predict_proba(new_input)[0][0]*100000\n",
        "    else:\n",
        "        return 'Your are considered high risk and are only be ellgible for: ',mod.predict_proba(new_input)[0][0]*100000\n",
        "    \n",
        "our_model(.88879,2300.0,4.0,9.0,0.0,0.0,0.0,1.0,.1999,65.0)\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:50: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Approved for:', 99416.95853601566)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pe50i8vpk-vF"
      },
      "source": [
        "Deployment with Flask"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMA4gnJZlEEb",
        "outputId": "cc5c300c-f629-4b2f-c4fe-7c9e26032e83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#source: https://medium.com/@kshitijvijay271199/flask-on-google-colab-f6525986797b\n",
        "#other source: https://www.youtube.com/watch?v=Pc8WdnIdXZg\n",
        "from flask import Flask, request\n",
        " #importing Flask to run the model on a host\n",
        "#from flask_ngrok import run_with_ngrok #importing ngrok to make Flask apps available on the internet (in this case, temporarily)\n",
        "#from pyngrok import ngrok\n",
        "app = Flask(__name__)\n",
        "\n",
        "\n",
        "#!ngrok authtoken 1hsQiSSJ05rnXkZfXKgyLkgMMbn_6Bfshx2Do22MRtsZYT9aM\n",
        "#!killall ngrok\n",
        "#get_ipython().system_raw('./ngrok http 5000 &')\n",
        "#!ngrok http -subdomain=priceforecasting 5000 \n",
        "\n",
        "#!ngrok by @priceforecasting\n",
        "#run_with_ngrok(app)   #starts ngrok when the app is run\n",
        "@app.route(\"/\") #Assigns \"/\" as the URL for the home function, using Flask\n",
        "def home():\n",
        "  #return a simple form with a title and place for ticker and a button named \"Predict\"\n",
        "    return '''\n",
        "    <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css\">\n",
        "<body style=\"background-color:lightgrey;\"></body>\n",
        "<div class=\"topnav\" id=\"myTopnav\">\n",
        "  <a href=\"#news\"></a>\n",
        "  <a href=\"https://worlddatascience.tech\" target=\"_blank\">Home </a>\n",
        "  <a href=\"javascript:void(0);\" class=\"icon\" onclick=\"myFunction()\">\n",
        "    <i class=\"fa fa-bars\"></i>\n",
        "  </a>\n",
        "</div>\n",
        "<style>\n",
        "body {\n",
        "  margin: 0;\n",
        "  font-family: Arial, Helvetica, sans-serif;\n",
        "}\n",
        "\n",
        ".topnav {\n",
        "  overflow: hidden;\n",
        "  background-color: #333;\n",
        "}\n",
        "\n",
        ".topnav a {\n",
        "  float: left;\n",
        "  display: block;\n",
        "  color: #f2f2f2;\n",
        "  text-align: center;\n",
        "  padding: 14px 16px;\n",
        "  text-decoration: none;\n",
        "  font-size: 17px;\n",
        "}\n",
        "\n",
        ".topnav a:hover {\n",
        "  background-color: #ddd;\n",
        "  color: black;\n",
        "}\n",
        "\n",
        ".topnav a.active {\n",
        "  background-color: #04a5e3;\n",
        "  color: white;\n",
        "}\n",
        "\n",
        ".topnav .icon {\n",
        "  display: none;\n",
        "}\n",
        "\n",
        "@media screen and (max-width: 600px) {\n",
        "  .topnav a:not(:first-child) {display: none;}\n",
        "  .topnav a.icon {\n",
        "    float: right;\n",
        "    display: block;\n",
        "  }\n",
        "}\n",
        "\n",
        "@media screen and (max-width: 600px) {\n",
        "  .topnav.responsive {position: relative;}\n",
        "  .topnav.responsive .icon {\n",
        "    position: absolute;\n",
        "    right: 0;\n",
        "    top: 0;\n",
        "  }\n",
        "  .topnav.responsive a {\n",
        "    float: none;\n",
        "    display: block;\n",
        "    text-align: left;\n",
        "  }\n",
        "}\n",
        "</style>\n",
        "    <center>\n",
        "    <head>\n",
        "    <h1> ~ Created By First Cohort ~ </h1>\n",
        "    </head>\n",
        "    </center>\n",
        "    <center>\n",
        "    <head>\n",
        "    <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\" integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\" crossorigin=\"anonymous\">\n",
        "  </head>\n",
        "<form action='/predict' method=\"post\" class=\"col s12\">\n",
        "      <h1>Credit risk model</h1>\n",
        "\n",
        "<form action=\"/action_page_binary.asp\" method=\"post\" enctype=\"multipart/form-data\">\n",
        "  <label for=\"DebtRatio\">What is your Debt Ratio:</label>\n",
        "  <input type=\"text\" id=\"DebtRatio\" name=\"DebtRatio\"><br><br>\n",
        "  <label for=\"MonthlyIncome\">Input your MonthlyIncome:</label>\n",
        "  <input type=\"text\" id=\"MonthlyIncome\" name=\"MonthlyIncome\"><br><br>\n",
        "  <label for=\"NumberOfDependents\">Input your amount of dependeants:</label>\n",
        "  <input type=\"text\" id=\"NumberOfDependents\" name=\"NumberOfDependents\"><br><br>\n",
        "  <label for=\"NumberOfOpenCreditLinesAndLoans\">Input the amount of open credit Lines & Loans:</label>\n",
        "  <input type=\"text\" id=\"NumberOfOpenCreditLinesAndLoans\" name=\"NumberOfOpenCreditLinesAndLoans\"><br><br>\n",
        "  <label for=\"NumberOfTime3050DaysPastDueNotWorse\">Input number of times you were 30-50 days past due:</label>\n",
        "  <input type=\"text\" id=\"NumberOfTime3050DaysPastDueNotWorse\" name=\"NumberOfTime3050DaysPastDueNotWorse\"><br><br>\n",
        "  <label for=\"NumberOfTime6089DaysPastDueNotWorse\">Input number of time you were 30-89 days past due:</label>\n",
        "  <input type=\"text\" id=\"NumberOfTime6089DaysPastDueNotWorse\" name=\"NumberOfTime6089DaysPastDueNotWorse\"><br><br>\n",
        "  <label for=\"NumberOfTimes90DaysLate\">Input number of times you were 90 days past due:</label>\n",
        "  <input type=\"text\" id=\"NumberOfTimes90DaysLate\" name=\"NumberOfTimes90DaysLate\"><br><br>\n",
        "  <label for=\"NumberRealEstateLoansOrLines\">Input amount fo real estate loans:</label>\n",
        "  <input type=\"text\" id=\"NumberRealEstateLoansOrLines\" name=\"NumberRealEstateLoansOrLines\"><br><br>\n",
        "  <label for=\"RevolvingUtilizationOfUnsecuredLines\">Input your revolving utilization of unsecured lines:</label>\n",
        "  <input type=\"text\" id=\"RevolvingUtilizationOfUnsecuredLines\" name=\"RevolvingUtilizationOfUnsecuredLines\"><br><br>\n",
        "  <label for=\"age\">Input your age:</label>\n",
        "  <input type=\"text\" id=\"age\" name=\"age\"><br><br>\n",
        "  <input type=\"submit\" value=\"Submit\">\n",
        "</form>\n",
        "<form action='/predict' method=\"post\" class=\"col s12\">\n",
        "\n",
        "</center>\n",
        "        '''\n",
        "@app.route('/predict',methods=['POST','GET']) #Assigns \"/predict\" as the URL for the predict function, using Flask\n",
        "def predict():\n",
        "    \n",
        "    Debtratio = request.form[\"DebtRatio\"]\n",
        "    MonthlyIncome = request.form[\"MonthlyIncome\"]\n",
        "    NumberOfDependents = request.form['NumberOfDependents']\n",
        "    NumberOfOpenCreditLinesAndLoans = request.form['NumberOfOpenCreditLinesAndLoans']\n",
        "    NumberOfTime3050DaysPastDueNotWorse = request.form['NumberOfTime3050DaysPastDueNotWorse']\n",
        "    NumberOfTime6089DaysPastDueNotWorse = request.form['NumberOfTime6089DaysPastDueNotWorse']\n",
        "    NumberOfTimes90DaysLate = request.form['NumberOfTimes90DaysLate']\n",
        "    NumberRealEstateLoansOrLines = request.form['NumberRealEstateLoansOrLines']\n",
        "    RevolvingUtilizationOfUnsecuredLines = request.form['RevolvingUtilizationOfUnsecuredLines']\n",
        "    age = request.form['age']\n",
        "    \n",
        "    \n",
        "    #prediction = model.our_model()\n",
        "\n",
        "    return '''\n",
        "    <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css\">\n",
        "<body style=\"background-color:lightgrey;\"></body>\n",
        "<div class=\"topnav\" id=\"myTopnav\">\n",
        "  <a href=\"#news\"></a>\n",
        "  <a href=\"https://worlddatascience.tech\" target=\"_blank\">Home </a>\n",
        "  <a href=\"javascript:void(0);\" class=\"icon\" onclick=\"myFunction()\">\n",
        "    <i class=\"fa fa-bars\"></i>\n",
        "  </a>\n",
        "</div>\n",
        "<style>\n",
        "body {\n",
        "  margin: 0;\n",
        "  font-family: Arial, Helvetica, sans-serif;\n",
        "}\n",
        ".topnav {\n",
        "  overflow: hidden;\n",
        "  background-color: #333;\n",
        "}\n",
        "\n",
        ".topnav a {\n",
        "  float: left;\n",
        "  display: block;\n",
        "  color: #f2f2f2;\n",
        "  text-align: center;\n",
        "  padding: 14px 16px;\n",
        "  text-decoration: none;\n",
        "  font-size: 17px;\n",
        "}\n",
        "\n",
        ".topnav a:hover {\n",
        "  background-color: #ddd;\n",
        "  color: black;\n",
        "}\n",
        "\n",
        ".topnav a.active {\n",
        "  background-color: #04a5e3;\n",
        "  color: white;\n",
        "}\n",
        "\n",
        ".topnav .icon {\n",
        "  display: none;\n",
        "}\n",
        "\n",
        "@media screen and (max-width: 600px) {\n",
        "  .topnav a:not(:first-child) {display: none;}\n",
        "  .topnav a.icon {\n",
        "    float: right;\n",
        "    display: block;\n",
        "  }\n",
        "}\n",
        "\n",
        "@media screen and (max-width: 600px) {\n",
        "  .topnav.responsive {position: relative;}\n",
        "  .topnav.responsive .icon {\n",
        "    position: absolute;\n",
        "    right: 0;\n",
        "    top: 0;\n",
        "  }\n",
        "  .topnav.responsive a {\n",
        "    float: none;\n",
        "    display: block;\n",
        "    text-align: left;\n",
        "  }\n",
        "}\n",
        "</style>\n",
        "    <center>\n",
        "    <head>\n",
        "    <h1> ~ Created By First Cohort ~ </h1>\n",
        "    </head>\n",
        "    </center>\n",
        "<center>\n",
        "      <head>\n",
        "    <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\" integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\" crossorigin=\"anonymous\">\n",
        "  </head>\n",
        "      </div>\n",
        "      <br>\n",
        "        ''' + \"<h2> outputs: \" + Debtratio + MonthlyIncome + NumberOfDependents + NumberOfOpenCreditLinesAndLoans+ NumberOfTime3050DaysPastDueNotWorse + NumberOfTime6089DaysPastDueNotWorse + NumberOfTimes90DaysLate + NumberRealEstateLoansOrLines +RevolvingUtilizationOfUnsecuredLines + age +'''\n",
        "        <br>\n",
        "    </div>\n",
        "</center>\n",
        "''' \n",
        "app.run() #run the Flask app"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: off\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}