model improvement.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test_data = pd.read_csv(\"test.csv\")\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_  = train.copy()\n",
    "test_data_  = test_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_ID               object\n",
      "Gender                object\n",
      "Married               object\n",
      "Dependents            object\n",
      "Education             object\n",
      "Self_Employed         object\n",
      "ApplicantIncome        int64\n",
      "CoapplicantIncome    float64\n",
      "LoanAmount           float64\n",
      "Loan_Amount_Term     float64\n",
      "Credit_History       float64\n",
      "Property_Area         object\n",
      "Loan_Status           object\n",
      "dtype: object \n",
      "\n",
      "train.shape:  (614, 13) \n",
      "\n",
      "test.shape:  (367, 12)\n"
     ]
    }
   ],
   "source": [
    "print(train.dtypes, \"\\n\\ntrain.shape: \", train.shape, \"\\n\\ntest.shape: \", test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981, 13)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.concat([train,test_data], axis = 0).reset_index(drop = True)\n",
    "dataset = dataset.fillna(np.nan)\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Catgorical, ordinal and Nominal Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      339\n",
       "Female     75\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"Loan_Status\"] == \"Y\"][\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "type_ = [\"Y\", \"N\"]\n",
    "fig = make_subplots(rows=1, cols=1)\n",
    "\n",
    "fig.add_trace(go.Pie(labels=type_, values=train['Loan_Status'].value_counts(), name=\"Loan_Status\"))\n",
    "\n",
    "# Use `hole` to create a donut-like pie chart\n",
    "fig.update_traces(hole=.4, hoverinfo=\"label+percent+name\", textfont_size=16)\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text=\"Loan approval status\",\n",
    "    # Add annotations in the center of the donut pies.\n",
    "    annotations=[dict(text='loan_status', x=0.5, y=0.5, font_size=20, showarrow=False)])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "labels =[\"Loan_Status: No\",\"Loan_Status:Yes\"]\n",
    "values = [train.Loan_Status[train.Loan_Status == \"Y\"].groupby(by = train.Gender).count().Female + train.Loan_Status[train.Loan_Status == \"Y\"].groupby(by = train.Gender).count().Male,\n",
    "          train.Loan_Status[train.Loan_Status == \"N\"].groupby(by = train.Gender).count().Female + train.Loan_Status[train.Loan_Status == \"N\"].groupby(by = train.Gender).count().Male]\n",
    "labels_gender = [\"F\",\"M\",\"F\",\"M\"]\n",
    "sizes_gender = [train.Loan_Status[train.Loan_Status == \"Y\"].groupby(by = train.Gender).count().Female,\n",
    "                train.Loan_Status[train.Loan_Status == \"Y\"].groupby(by = train.Gender).count().Male,\n",
    "                train.Loan_Status[train.Loan_Status == \"N\"].groupby(by = train.Gender).count().Female,\n",
    "                train.Loan_Status[train.Loan_Status == \"N\"].groupby(by = train.Gender).count().Male]\n",
    "\n",
    "colors = ['#ff6666', '#66b3ff']\n",
    "colors_gender = ['#c2c2f0','#ffb3e6', '#c2c2f0','#ffb3e6']\n",
    "explode = (0.3,0.3) \n",
    "explode_gender = (0.1,0.1,0.1,0.1)\n",
    "textprops = {\"fontsize\":15}\n",
    "#Plot\n",
    "plt.pie(values, labels=labels,autopct='%1.1f%%',pctdistance=1.08, labeldistance=0.8,colors=colors, startangle=90,frame=True, explode=explode,radius=10, textprops =textprops, counterclock = True, )\n",
    "plt.pie(sizes_gender,labels=labels_gender,colors=colors_gender,startangle=90, explode=explode_gender,radius=7, textprops =textprops, counterclock = True, )\n",
    "#Draw circle\n",
    "centre_circle = plt.Circle((0,0),5,color='black', fc='white',linewidth=0)\n",
    "fig = plt.gcf()\n",
    "fig.gca().add_artist(centre_circle)\n",
    "\n",
    "plt.title('Loan approval w.r.t Gender: Male(M), Female(F)', fontsize=15, y=1.1)\n",
    "\n",
    "# show plot \n",
    " \n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
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   "source": [
    "color_map = {\"Y\": \"#FF97FF\", \"N\": \"#AB63FA\"}\n",
    "fig = px.histogram(train, x=\"Dependents\", color=\"Loan_Status\", barmode=\"group\", title=\"<b>Loan approval based on Dependents</b>\", color_discrete_map=color_map)\n",
    "fig.update_layout(width=700, height=500, bargap=0.1)\n",
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       "                            \n",
       "var gd = document.getElementById('eacbc6cc-6e68-49ed-a852-c3bc13192232');\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_map = {\"Y\": \"#FF97FF\", \"N\": \"#AB63FA\"}\n",
    "fig = px.histogram(train, x=\"Loan_Status\", color=\"Education\", barmode=\"group\", title=\"<b>Loan approval based on Education</b>\", color_discrete_map=color_map)\n",
    "fig.update_layout(width=700, height=500, bargap=0.1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
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   "source": [
    "color_map = {\"Y\": \"#FF97FF\", \"N\": \"#AB63FA\"}\n",
    "fig = px.histogram(train, x=\"Loan_Status\", color=\"Property_Area\", barmode=\"group\", title=\"<b>Loan approval based on property area</b>\", color_discrete_map=color_map)\n",
    "fig.update_layout(width=700, height=500, bargap=0.1)\n",
    "fig.show()"
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  },
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   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
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true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"<b>Loan approval based on Marital status</b>\"}, \"width\": 700, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Married\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"count\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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     "output_type": "display_data"
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   ],
   "source": [
    "color_map = {\"Y\": \"#FF97FF\", \"N\": \"#AB63FA\"}\n",
    "fig = px.histogram(train, x=\"Married\", color=\"Loan_Status\", barmode=\"group\", title=\"<b>Loan approval based on Marital status</b>\", color_discrete_map=color_map)\n",
    "fig.update_layout(width=700, height=500, bargap=0.1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
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       "                            \n",
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     "output_type": "display_data"
    }
   ],
   "source": [
    "color_map = {\"Y\": \"#FF97FF\", \"N\": \"#AB63FA\"}\n",
    "fig = px.histogram(train, x=\"Self_Employed\", color=\"Loan_Status\", barmode=\"group\", title=\"<b>Loan approval based on Self employment status</b>\", color_discrete_map=color_map)\n",
    "fig.update_layout(width=700, height=500, bargap=0.1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical independent variable vs target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kdeplot(param, x_title):\n",
    "    \n",
    "    sns.set_context(\"paper\",font_scale=1.1)\n",
    "    ax = sns.kdeplot(param[(train[\"Loan_Status\"] == 'N') ],\n",
    "                color=\"Red\", shade = True);\n",
    "    ax = sns.kdeplot(param[(train[\"Loan_Status\"] == 'Y') ],\n",
    "                ax =ax, color=\"Blue\", shade= True);\n",
    "    ax.legend([\"Not approved\",\"approved\"],loc='upper right');\n",
    "    ax.set_ylabel('Density');\n",
    "    ax.set_xlabel(x_title);\n",
    "    ax.set_title(\"Distribution of \" + x_title +\" by loan approval status\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kdeplot(train.ApplicantIncome, \"Applicant's income\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kdeplot(train.CoapplicantIncome, \"co-applicant's Income\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kdeplot(train.LoanAmount, \"Loan Amount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kdeplot(train.Loan_Amount_Term, \"Loan Amount term\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.ApplicantIncome.values.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applicant's Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"ApplicantIncome_\"] = list(map(lambda x: 'Low' if x <= 2500 \n",
    "         else('Medium' if (x > 2500 and x <= 4000) \n",
    "              else('High' if (x > 4000 and x <= 6000) else 'Very High')), train.ApplicantIncome))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(y =\"ApplicantIncome\", size = 7, aspect = 2, data = train, x= \"Loan_Status\", palette = \"dark\")\n",
    "plt.ylabel(\"\\nApplicantIncome\")\n",
    "plt.xlabel(\"Loan Approval Status\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "hovertemplate": "Loan_Status=Y<br>ApplicantIncome_=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Y",
         "marker": {
          "color": "#FF97FF"
         },
         "name": "Y",
         "offsetgroup": "Y",
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": [
          "High",
          "Medium",
          "Medium",
          "High",
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    }
   ],
   "source": [
    "color_map = {\"Y\": \"#FF97FF\", \"N\": \"#AB63FA\"}\n",
    "fig = px.histogram(train, x=\"ApplicantIncome_\", color=\"Loan_Status\", barmode=\"group\", title=\"<b>Loan approval based on Applicant's Income</b>\", color_discrete_map=color_map)\n",
    "fig.update_layout(width=700, height=500, bargap=0.1)\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applicant's income does not affect the chances of Loan approval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"CoapplicantIncome_\"] = list(map(lambda x: 'Low' if x <= 1000 \n",
    "                                   else('High' if (x > 1000 and x <= 3000) else 'Very High'), train.CoapplicantIncome))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(y =\"CoapplicantIncome\", size = 7, aspect = 2, data = train, x= \"Loan_Status\", palette = \"dark\")\n",
    "plt.ylabel(\"\\CoapplicantIncome\")\n",
    "plt.xlabel(\"Loan Approval Status\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "hovertemplate": "Loan_Status=Y<br>CoapplicantIncome_=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Y",
         "marker": {
          "color": "#FF97FF"
         },
         "name": "Y",
         "offsetgroup": "Y",
         "orientation": "v",
         "showlegend": true,
         "type": "histogram",
         "x": [
          "Low",
          "Low",
          "High",
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_map = {\"Y\": \"#FF97FF\", \"N\": \"#AB63FA\"}\n",
    "fig = px.histogram(train, x=\"CoapplicantIncome_\", color=\"Loan_Status\", title=\"<b>Loan approval based on Co-Applicant's Income</b>\", color_discrete_map=color_map)\n",
    "fig.update_layout(width=700, height=500, bargap=0.1)\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It shows that if applicants income is less then chances of Loan approval are high. But most of the applicants don't have co-ccplicants hence the value is 0 in that case. Hence this finding is not promising.<br>\n",
    "\n",
    "Let compbine Applicant's and Co-applicant's income and see the the effect of Total income on Loan approval Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Total_income\"] = train.ApplicantIncome + train.CoapplicantIncome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
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    "sns.factorplot(y =\"Total_income\", size = 7, aspect = 2, data = train, x= \"Loan_Status\", palette = \"dark\")\n",
    "plt.ylabel(\"\\Total_income\")\n",
    "plt.xlabel(\"Loan Approval Status\\n\")\n",
    "plt.show()"
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  },
  {
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   "execution_count": 128,
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    "train[\"Total_income_\"]= list(map(lambda x: 'Low' if x <= 2500 \n",
    "         else('Medium' if (x > 2500 and x <= 4000) \n",
    "              else('High' if (x > 4000 and x <= 6000) else 'Very High')), train.Total_income))"
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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"<b>Loan approval based on Total Income</b>\"}, \"width\": 700, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Total_income_\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"count\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('227de37b-7776-42df-bec4-f730486f7a25');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_map = {\"Y\": \"#FF97FF\", \"N\": \"#AB63FA\"}\n",
    "fig = px.histogram(train, x=\"Total_income_\", color=\"Loan_Status\", title=\"<b>Loan approval based on Total Income</b>\", color_discrete_map=color_map)\n",
    "fig.update_layout(width=700, height=500, bargap=0.1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loan Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(y =\"LoanAmount\", size = 7, aspect = 2, data = train, x= \"Loan_Status\", palette = \"dark\")\n",
    "plt.ylabel(\"\\LoanAmount\")\n",
    "plt.xlabel(\"Loan Approval Status\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "train[\"LoanAmount_\"]= list(map(lambda x: 'Low' if x <= 100 \n",
    "         else('High' if (x > 100 and x <= 200) \n",
    "              else('Very High' if (x > 200) else 'Other')), train.LoanAmount))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Other for null vals\n",
    "color_map = {\"Y\": \"#FF97FF\", \"N\": \"#AB63FA\"}\n",
    "fig = px.histogram(train, x=\"LoanAmount_\", color=\"Loan_Status\",  title=\"<b>Loan approval based on Loan Amount</b>\", color_discrete_map=color_map)\n",
    "fig.update_layout(width=700, height=500, bargap=0.1)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's drop all the created variables\n",
    "train =train.drop(['ApplicantIncome_', 'CoapplicantIncome_', 'Total_income', 'Total_income_', 'LoanAmount_'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Dependents\"].replace(\"3+\", 3, inplace = True)\n",
    "test_data[\"Dependents\"].replace(\"3+\", 3, inplace = True)\n",
    "\n",
    "train[\"Loan_Status\"].replace(\"N\", 0, inplace = True)\n",
    "train[\"Loan_Status\"].replace(\"Y\", 1, inplace = True)\n",
    "\n",
    "train['CoapplicantIncome'].replace(0, np.nan, inplace=True)\n",
    "test_data['CoapplicantIncome'].replace(0, np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_[\"Loan_Status\"].replace(\"N\", 0, inplace = True)\n",
    "train_[\"Loan_Status\"].replace(\"Y\", 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation between numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (9,6))\n",
    "sns.heatmap(matrix, vmax = .8, square = True, cmap = \"BuPu\", annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Value Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop([\"Loan_ID\"], axis = 1)\n",
    "test_data.drop([\"Loan_ID\"], axis = 1)\n",
    "train.drop(\"Loan_Status\", axis =1)\n",
    "train = pd.get_dummies(train)\n",
    "test_data = pd.get_dummies(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_values = pd.DataFrame(train.isnull().sum())\n",
    "missing_values.columns = [\"missing_values\"]\n",
    "\n",
    "missing_values_test = pd.DataFrame(test_data.isnull().sum())\n",
    "missing_values_test.columns = [\"missing_values\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>missing_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID_LP001699</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID_LP001702</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID_LP001708</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID_LP001711</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property_Area_Urban</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     missing_values\n",
       "CoapplicantIncome               273\n",
       "Credit_History                   50\n",
       "LoanAmount                       22\n",
       "Loan_Amount_Term                 14\n",
       "ApplicantIncome                   0\n",
       "...                             ...\n",
       "Loan_ID_LP001699                  0\n",
       "Loan_ID_LP001702                  0\n",
       "Loan_ID_LP001708                  0\n",
       "Loan_ID_LP001711                  0\n",
       "Property_Area_Urban               0\n",
       "\n",
       "[635 rows x 1 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values.sort_values(by = \"missing_values\", ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>missing_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID_LP001652</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID_LP001651</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID_LP001650</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID_LP001627</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property_Area_Urban</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>387 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     missing_values\n",
       "CoapplicantIncome               156\n",
       "Credit_History                   29\n",
       "Loan_Amount_Term                  6\n",
       "LoanAmount                        5\n",
       "ApplicantIncome                   0\n",
       "...                             ...\n",
       "Loan_ID_LP001652                  0\n",
       "Loan_ID_LP001651                  0\n",
       "Loan_ID_LP001650                  0\n",
       "Loan_ID_LP001627                  0\n",
       "Property_Area_Urban               0\n",
       "\n",
       "[387 rows x 1 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_test.sort_values(by = \"missing_values\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ntrain[\"Gender\"].fillna(train[\"Gender\"].mode()[0], inplace=True)\\ntrain[\"Married\"].fillna(train[\"Married\"].mode()[0], inplace=True)\\ntrain[\"Dependents\"].fillna(train[\"Dependents\"].mode()[0], inplace=True)\\ntrain[\"Self_Employed\"].fillna(train[\"Self_Employed\"].mode()[0], inplace=True)\\ntrain[\"Credit_History\"].fillna(train[\"Credit_History\"].mode()[0], inplace=True)\\n'"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "train[\"Gender\"].fillna(train[\"Gender\"].mode()[0], inplace=True)\n",
    "train[\"Married\"].fillna(train[\"Married\"].mode()[0], inplace=True)\n",
    "train[\"Dependents\"].fillna(train[\"Dependents\"].mode()[0], inplace=True)\n",
    "train[\"Self_Employed\"].fillna(train[\"Self_Employed\"].mode()[0], inplace=True)\n",
    "train[\"Credit_History\"].fillna(train[\"Credit_History\"].mode()[0], inplace=True)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'test_data[\"Credit_History\"].fillna(test_data[\"Credit_History\"].mode()[0], inplace=True)'"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"test_data[\"Credit_History\"].fillna(test_data[\"Credit_History\"].mode()[0], inplace=True)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "## KNNImputer\n",
    "\n",
    "from sklearn.impute import KNNImputer\n",
    "imputer= KNNImputer(n_neighbors=10)\n",
    "train = imputer.fit_transform(train[[\"CoapplicantIncome\", \"Credit_History\", \n",
    "                                     \"Loan_Amount_Term\", \"LoanAmount\"]])\n",
    "\n",
    "test_data = imputer.fit_transform(test_data[[\"CoapplicantIncome\", \"Credit_History\", \n",
    "                                     \"Loan_Amount_Term\", \"LoanAmount\"]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "value of 360 is repeating most of the times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train\n",
    "y = train_.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier,GradientBoostingClassifier, VotingClassifier, ExtraTreesClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_cv, y_train, y_cv = train_test_split(X, y, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "\n",
    "models.append(('Logistic Regression', LogisticRegression(solver='liblinear', random_state = 0, class_weight='balanced')))\n",
    "#models.append(('SVC', SVC(kernel = 'linear', random_state = 0)))\n",
    "#models.append(('Kernel SVM', SVC(kernel = 'rbf', random_state = 0)))\n",
    "models.append(('KNN', KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)))\n",
    "models.append(('Gaussian NB', GaussianNB()))\n",
    "models.append(('Decision Tree Classifier', DecisionTreeClassifier(criterion = 'entropy', random_state = 0)))\n",
    "models.append(('Random Forest', RandomForestClassifier(n_estimators=100, criterion = 'entropy', random_state = 0)))\n",
    "models.append((\"Adaboost\", AdaBoostClassifier()))\n",
    "models.append((\"Gradient boost classifier\", GradientBoostingClassifier()))\n",
    "models.append((\"XGBoost\", XGBClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score, roc_curve, f1_score, accuracy_score\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "KNN\n",
      "Gaussian NB\n",
      "Decision Tree Classifier\n",
      "Random Forest\n",
      "Adaboost\n",
      "Gradient boost classifier\n",
      "XGBoost\n",
      "[22:32:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "names = []\n",
    "acc_results = []\n",
    "test_labels = []\n",
    "roc_auc_results = []\n",
    "models_fit = []\n",
    "for name, model in models:\n",
    "    print(name)\n",
    "    names.append(name)\n",
    "    model_ = model.fit(X_train,y_train)\n",
    "    models_fit.append(model_)\n",
    "    \n",
    "    acc = model_.score(X_cv, y_cv)\n",
    "    acc_results.append(acc)\n",
    "    \n",
    "    test_ = model_.predict_proba(np.array(X_cv))\n",
    "    test_ = test_[:, 1]\n",
    "    test_labels.append(test_)\n",
    "    \n",
    "    roc_auc = roc_auc_score(y_cv, test_, multi_class='ovo', average='weighted')\n",
    "    roc_auc_results.append(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_ = list(zip(names, acc_results, roc_auc_results))\n",
    "model_results_df = pd.DataFrame(list_,\n",
    "                  columns = ['Model', 'Accuracy', \"ROC_AUC\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaussian NB</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.684700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.709137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.794595</td>\n",
       "      <td>0.693117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Adaboost</td>\n",
       "      <td>0.783784</td>\n",
       "      <td>0.690266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gradient boost classifier</td>\n",
       "      <td>0.772973</td>\n",
       "      <td>0.688162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.762162</td>\n",
       "      <td>0.711920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.697297</td>\n",
       "      <td>0.622862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.637838</td>\n",
       "      <td>0.575550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  Accuracy   ROC_AUC\n",
       "2                Gaussian NB  0.810811  0.684700\n",
       "0        Logistic Regression  0.800000  0.709137\n",
       "7                    XGBoost  0.794595  0.693117\n",
       "5                   Adaboost  0.783784  0.690266\n",
       "6  Gradient boost classifier  0.772973  0.688162\n",
       "4              Random Forest  0.762162  0.711920\n",
       "3   Decision Tree Classifier  0.697297  0.622862\n",
       "1                        KNN  0.637838  0.575550"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_results_df.sort_values(by = \"Accuracy\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_lr, tpr_lr, _ = roc_curve(y_cv, test_labels[0])\n",
    "fpr_knn, tpr_knn, _ = roc_curve(y_cv, test_labels[1])\n",
    "fpr_gnb, tpr_gnb, _ = roc_curve(y_cv, test_labels[2])\n",
    "fpr_dt, tpr_dt, _ = roc_curve(y_cv, test_labels[3])\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_cv, test_labels[4])\n",
    "fpr_ada, tpr_ada, _ = roc_curve(y_cv, test_labels[5])\n",
    "fpr_gb, tpr_gb, _ = roc_curve(y_cv, test_labels[6])\n",
    "fpr_xgb, tpr_xgb, _ = roc_curve(y_cv, test_labels[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the roc curve\n",
    "plt.figure(figsize = (12,6), linewidth= 1)\n",
    "plt.plot(fpr_lr, tpr_lr, label = 'Logistic Regression: ' + str(round(roc_auc_results[0], 5)))\n",
    "plt.plot(fpr_knn, tpr_knn, label = 'KNN Score: ' + str(round(roc_auc_results[1], 5)))\n",
    "plt.plot(fpr_gnb, tpr_gnb, label = 'Gaussian NB score: ' + str(round(roc_auc_results[2], 5)))\n",
    "plt.plot(fpr_dt, tpr_dt, label = 'DT score: ' + str(round(roc_auc_results[3], 5)))\n",
    "plt.plot(fpr_rf, tpr_rf, label = 'RF score: ' + str(round(roc_auc_results[4], 5)))\n",
    "plt.plot(fpr_ada, tpr_ada, label = 'AdaBoost Score: ' + str(round(roc_auc_results[5], 5)))\n",
    "plt.plot(fpr_gb, tpr_gb, label = 'GB Score: ' + str(round(roc_auc_results[6], 5)))\n",
    "plt.plot(fpr_xgb, tpr_xgb, label = 'XGB Score: ' + str(round(roc_auc_results[7], 5)))\n",
    "\n",
    "plt.plot([0,1], [0,1], 'k--', label = 'Random guessing: 0.5')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC Curve ')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Cross Validation Scores\n",
    "\n",
    "# Import the cross-validation module\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Function that will track the mean value and the standard deviation of the accuracy\n",
    "def CrossValidationDict(models_list, scr, X_train = X, y_cv = y, cv = 5):\n",
    "    cvDict = {}\n",
    "    for mo in models_list:\n",
    "        cvScore = cross_val_score(mo, X_train, y_cv, cv = cv, scoring = scr)\n",
    "        \n",
    "        cvDict[str(mo).split('(')[0]] = [cvScore.mean(), cvScore.std()]\n",
    "    \n",
    "    return cvDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22:33:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[22:33:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[22:33:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[22:33:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[22:33:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': [0.7283703035870218, 0.04676781543409788],\n",
       " 'KNeighborsClassifier': [0.5235283060592659, 0.046413298514538334],\n",
       " 'GaussianNB': [0.6906953658656445, 0.05270309148117042],\n",
       " 'DecisionTreeClassifier': [0.6726131599738411, 0.040057655775073385],\n",
       " 'RandomForestClassifier': [0.7307822724231393, 0.057326139718518694],\n",
       " 'AdaBoostClassifier': [0.7009450001323067, 0.04933617151912804],\n",
       " 'GradientBoostingClassifier': [0.6997079709076613, 0.06236805864274935],\n",
       " 'XGBClassifier': [0.7247474738883408, 0.054509359378854094]}"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CrossValidationDict(models_fit, scr = 'roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22:33:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[22:33:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[22:33:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[22:33:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[22:33:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': [0.7801412768226041, 0.02945067493434803],\n",
       " 'KNeighborsClassifier': [0.6530987604958016, 0.04095523084475143],\n",
       " 'GaussianNB': [0.7964414234306278, 0.02846093668748405],\n",
       " 'DecisionTreeClassifier': [0.6906037584966013, 0.030815609139694077],\n",
       " 'RandomForestClassifier': [0.7410502465680394, 0.022543967925961923],\n",
       " 'AdaBoostClassifier': [0.791536718645875, 0.030199217684751926],\n",
       " 'GradientBoostingClassifier': [0.7850459816073571, 0.0356773863339701],\n",
       " 'XGBClassifier': [0.7394242303078769, 0.03401901465246312]}"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CrossValidationDict(models_fit, scr = 'accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyper Parameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ADABOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 600 candidates, totalling 3000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  20 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=6)]: Done 116 tasks      | elapsed:   21.3s\n",
      "[Parallel(n_jobs=6)]: Done 276 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=6)]: Done 500 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=6)]: Done 788 tasks      | elapsed:  2.8min\n",
      "[Parallel(n_jobs=6)]: Done 1140 tasks      | elapsed:  4.1min\n",
      "[Parallel(n_jobs=6)]: Done 1556 tasks      | elapsed:  5.6min\n",
      "[Parallel(n_jobs=6)]: Done 2036 tasks      | elapsed:  7.5min\n",
      "[Parallel(n_jobs=6)]: Done 2580 tasks      | elapsed:  9.7min\n",
      "[Parallel(n_jobs=6)]: Done 3000 out of 3000 | elapsed: 11.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8088372093023256"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ABC = AdaBoostClassifier()\n",
    "\n",
    "ABC_param_grid = {\"algorithm\" : [\"SAMME\",\"SAMME.R\"],\n",
    "                  \"n_estimators\" :list(range(100, 700, 10)),\n",
    "                  \"learning_rate\":  [0.001, 0.01, 0.1, 0.11,0.3]}\n",
    "\n",
    "gsABC = GridSearchCV(ABC, param_grid = ABC_param_grid, cv = 5, scoring = \"accuracy\", n_jobs = 6, verbose =3)\n",
    "\n",
    "gsABC.fit(X_train,y_train)\n",
    "\n",
    "ada_best = gsABC.best_estimator_\n",
    "\n",
    "gsABC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME', learning_rate=0.001, n_estimators=100)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsABC.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'SAMME', 'learning_rate': 0.001, 'n_estimators': 100}"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsABC.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 972 candidates, totalling 4860 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  64 tasks      | elapsed:    4.7s\n",
      "[Parallel(n_jobs=6)]: Done 230 tasks      | elapsed:   15.4s\n",
      "[Parallel(n_jobs=6)]: Done 480 tasks      | elapsed:   32.9s\n",
      "[Parallel(n_jobs=6)]: Done 830 tasks      | elapsed:   58.1s\n",
      "[Parallel(n_jobs=6)]: Done 1280 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=6)]: Done 1830 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=6)]: Done 2480 tasks      | elapsed:  3.0min\n",
      "[Parallel(n_jobs=6)]: Done 3230 tasks      | elapsed:  4.2min\n",
      "[Parallel(n_jobs=6)]: Done 4080 tasks      | elapsed:  5.3min\n",
      "[Parallel(n_jobs=6)]: Done 4860 out of 4860 | elapsed:  6.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8111901504787962"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient Boost\n",
    "\n",
    "GBM = GradientBoostingClassifier()\n",
    "\n",
    "gb_param_grid = {'loss' : [\"deviance\"],\n",
    "                 'n_estimators' : list(range(100,700,100)),\n",
    "                 'learning_rate': [0.01,0.1,0.11],\n",
    "                 'max_depth': [2,3,4,5,6,7,8,9,10],\n",
    "                 'min_samples_leaf': [30,40],\n",
    "                 'max_features': [0.6,0.7,0.8]}\n",
    "\n",
    "gsGBC = GridSearchCV(GBM, param_grid = gb_param_grid, cv = 5, scoring = \"accuracy\", n_jobs = 6, verbose = 1)\n",
    "\n",
    "gsGBC.fit(X_train,y_train)\n",
    "\n",
    "gbm_best = gsGBC.best_estimator_\n",
    "\n",
    "gsGBC.best_score_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 864 candidates, totalling 4320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:    2.2s\n",
      "[Parallel(n_jobs=6)]: Done 188 tasks      | elapsed:   24.9s\n",
      "[Parallel(n_jobs=6)]: Done 438 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=6)]: Done 788 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=6)]: Done 1238 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=6)]: Done 1788 tasks      | elapsed:  4.5min\n",
      "[Parallel(n_jobs=6)]: Done 2438 tasks      | elapsed:  6.1min\n",
      "[Parallel(n_jobs=6)]: Done 3188 tasks      | elapsed:  8.3min\n",
      "[Parallel(n_jobs=6)]: Done 4038 tasks      | elapsed: 10.3min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23:08:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Done 4320 out of 4320 | elapsed: 10.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8088372093023256"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGBOOSt\n",
    "XGB = XGBClassifier()\n",
    "\n",
    "xgb_param_grid = {'learning_rate': [0.01,0.1,0.11], \n",
    "                  'max_depth': [4,5,6,7],\n",
    "                  'n_estimators': [50,100,200,400,500,550], \n",
    "                  'gamma': [0,1,5,8],\n",
    "                  'subsample': [0.8,0.95,1.0]}\n",
    "\n",
    "gsXBC = GridSearchCV(XGB, param_grid = xgb_param_grid, cv = 5, scoring = \"accuracy\", n_jobs = 6, verbose = 1)\n",
    "\n",
    "gsXBC.fit(X_train,y_train)\n",
    "\n",
    "xgb_best = gsXBC.best_estimator_\n",
    "\n",
    "gsXBC.best_score_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
      "tuned hpyerparameters :(best parameters)  {'C': 1.5848931924611136, 'penalty': 'l2'}\n",
      "accuracy : 0.8135658914728682\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  68 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=6)]: Done  80 out of  80 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "grid={\"C\":np.logspace(0.1,0.4,4), \"penalty\":[\"l1\",\"l2\"]}# l1 lasso l2 ridge\n",
    "logreg=LogisticRegression(random_state=0, solver='liblinear')\n",
    "logreg_cv=GridSearchCV(logreg,grid,cv=10, scoring = \"accuracy\", n_jobs =6,verbose = 1)\n",
    "logreg_cv.fit(X_train,y_train)\n",
    "\n",
    "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
    "print(\"accuracy :\",logreg_cv.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest\n",
    "\n",
    "rf_test = {\"max_depth\": [3,4,5],\n",
    "           \"n_estimators\" :[100,300,500,600],\n",
    "           \"min_samples_leaf\":[10,15]}\n",
    "\n",
    "tuning = GridSearchCV(RandomForestClassifier(), param_grid = rf_test, scoring = 'accuracy', n_jobs = 6)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8088372093023256"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning.fit(X_train,y_train)\n",
    "\n",
    "rf_best = tuning.best_estimator_\n",
    "\n",
    "tuning.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'min_samples_leaf': 10, 'n_estimators': 100}"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train F1 Score  0.8757575757575758\n",
      "Train Accuracy  0.8088578088578089\n",
      "Validation Mean F1 Score:  0.7298838994138311\n",
      "Validation Mean Accuracy:  0.8088372093023256\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_clf = RandomForestClassifier(n_estimators=100,max_depth=3,min_samples_leaf = 10)\n",
    "rf_clf.fit(X_train,y_train)\n",
    "y_pred = rf_clf.predict(X_train)\n",
    "print(\"Train F1 Score \", f1_score(y_train,y_pred))\n",
    "print(\"Train Accuracy \", accuracy_score(y_train,y_pred))\n",
    "\n",
    "print(\"Validation Mean F1 Score: \",cross_val_score(rf_clf,X_train,y_train,cv=5,scoring='f1_macro').mean())\n",
    "print(\"Validation Mean Accuracy: \",cross_val_score(rf_clf,X_train,y_train,cv=5,scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 756 candidates, totalling 3780 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:    4.1s\n",
      "[Parallel(n_jobs=6)]: Done 3694 tasks      | elapsed:    6.0s\n",
      "[Parallel(n_jobs=6)]: Done 3780 out of 3780 | elapsed:    6.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8088372093023256"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Decision tree classifier\n",
    "\n",
    "\n",
    "tree_param = {'criterion': ['entropy', 'gini'], \n",
    "               'max_depth': [2,3,4,5,6,7,8,9,10,11,12,13,14,15],\n",
    "             \"min_samples_leaf\":list(range(1,80,3))}\n",
    "              \n",
    "gsDTC = GridSearchCV(DecisionTreeClassifier(), param_grid = tree_param, cv = 5, scoring = \"accuracy\", n_jobs = 6, verbose = 1)\n",
    "\n",
    "gsDTC.fit(X_train,y_train)\n",
    "\n",
    "dtc_best = gsDTC.best_estimator_\n",
    "\n",
    "gsDTC.best_score_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 14 candidates, totalling 70 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:   29.2s\n",
      "[Parallel(n_jobs=6)]: Done  70 out of  70 | elapsed:   29.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8088645690834474"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GaussianNB\n",
    "\n",
    "\n",
    "gnb_clf = GaussianNB()\n",
    "parameters = {\n",
    "    'var_smoothing': [1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8, 1e-9, 1e-10, 1e-11, 1e-12, 1e-13, 1e-14, 1e-15]\n",
    "}\n",
    "clf = GridSearchCV(gnb_clf, parameters, cv=5, verbose=1, n_jobs=6, scoring = \"accuracy\")\n",
    "clf.fit(X_train, y_train)\n",
    "#plot_grid_search(clf)\n",
    "#table_grid_search(clf)\n",
    "clf_best = clf.best_estimator_\n",
    "\n",
    "clf.best_score_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 199 candidates, totalling 995 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  64 tasks      | elapsed:    1.2s\n",
      "[Parallel(n_jobs=6)]: Done 364 tasks      | elapsed:    5.6s\n",
      "[Parallel(n_jobs=6)]: Done 864 tasks      | elapsed:   14.2s\n",
      "[Parallel(n_jobs=6)]: Done 995 out of 995 | elapsed:   16.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6876607387140903"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#knn\n",
    "\n",
    "param_grid_knn = {\"n_neighbors\": list(range(1,200))}\n",
    "\n",
    "gs_knn = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=5, verbose=1, n_jobs=6, scoring = \"accuracy\")\n",
    "gs_knn.fit(X_train, y_train)\n",
    "\n",
    "knn_best = gs_knn.best_estimator_\n",
    "\n",
    "gs_knn.best_score_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 29}"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_knn.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 648 candidates, totalling 3240 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:   22.8s\n",
      "[Parallel(n_jobs=6)]: Done 188 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=6)]: Done 438 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=6)]: Done 788 tasks      | elapsed:  4.6min\n",
      "[Parallel(n_jobs=6)]: Done 1238 tasks      | elapsed:  4.9min\n",
      "[Parallel(n_jobs=6)]: Done 1788 tasks      | elapsed:  5.3min\n",
      "[Parallel(n_jobs=6)]: Done 2438 tasks      | elapsed:  5.8min\n",
      "[Parallel(n_jobs=6)]: Done 3188 tasks      | elapsed:  6.5min\n",
      "[Parallel(n_jobs=6)]: Done 3240 out of 3240 | elapsed:  6.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8088372093023256"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extratrees\n",
    "ExtC = ExtraTreesClassifier()\n",
    "\n",
    "ex_param_grid = {\"max_depth\": [3, 4, 5],\n",
    "                 \"max_features\": [3, 10, 15],\n",
    "                 \"min_samples_split\": [2, 3, 4],\n",
    "                 \"min_samples_leaf\": [1, 2],\n",
    "                 \"bootstrap\": [False,True],\n",
    "                 \"n_estimators\" :[100,200,300],\n",
    "                 \"criterion\": [\"gini\",\"entropy\"]}\n",
    "\n",
    "gsExtC = GridSearchCV(ExtC, param_grid = ex_param_grid, cv = 5, scoring = \"accuracy\", n_jobs = 6, verbose = 1)\n",
    "\n",
    "gsExtC.fit(X_train,y_train)\n",
    "\n",
    "ext_best = gsExtC.best_estimator_\n",
    "\n",
    "gsExtC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done   5 out of   5 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:19:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8088372093023256"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting = VotingClassifier(estimators=[('abc', gsABC.best_estimator_), \n",
    "                                      ('lr',logreg_cv.best_estimator_),\n",
    "                                      ('xgb',gsXBC.best_estimator_),\n",
    "                                      ('gbc',gsGBC.best_estimator_),\n",
    "                                      ('gnb',clf_best),\n",
    "                                      ('dtc',dtc_best),\n",
    "                                     (\"ext\",ext_best)])\n",
    "\n",
    "v_param_grid = {'voting':['soft']} # tuning voting parameter\n",
    "\n",
    "gsV = GridSearchCV(voting, \n",
    "                   param_grid = v_param_grid, cv = 5, \n",
    "                   scoring = \"accuracy\",\n",
    "                   n_jobs = 6, \n",
    "                   verbose = 1)\n",
    "\n",
    "gsV.fit(X_train,y_train)\n",
    "\n",
    "v_best = gsV.best_estimator_\n",
    "\n",
    "gsV.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adaboost\n",
      "KNN\n",
      "GaussianNB\n",
      "Decision Trees\n",
      "Random Forest\n",
      "Logistic Regression\n",
      "XGBoost\n",
      "[00:19:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "GradientBoost\n",
      "ExtraTrees\n",
      "Voting_classifier\n",
      "[00:19:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "models_tuned = [gsABC.best_estimator_, gs_knn.best_estimator_, clf.best_estimator_, gsDTC.best_estimator_, \n",
    "                tuning.best_estimator_, logreg_cv.best_estimator_, gsXBC.best_estimator_, gsGBC.best_estimator_,ext_best, v_best]\n",
    "model_names= [\"Adaboost\", \"KNN\", \"GaussianNB\", \"Decision Trees\", \"Random Forest\", \"Logistic Regression\", \n",
    "              \"XGBoost\", \"GradientBoost\",\"ExtraTrees\", \"Voting_classifier\"]\n",
    "\n",
    "\n",
    "names = []\n",
    "acc_results = []\n",
    "test_labels = []\n",
    "roc_auc_results = []\n",
    "models_fit = []\n",
    "\n",
    "for name, model in zip(model_names,models_tuned):\n",
    "    print(name)\n",
    "    names.append(name)\n",
    "    model_ = model.fit(X_train,y_train)\n",
    "    models_fit.append(model_)\n",
    "    \n",
    "    acc = model_.score(X_cv, y_cv)\n",
    "    acc_results.append(acc)\n",
    "    \n",
    "    test_ = model_.predict_proba(np.array(X_cv))\n",
    "    test_ = test_[:, 1]\n",
    "    test_labels.append(test_)\n",
    "    \n",
    "    roc_auc = roc_auc_score(y_cv, test_, multi_class='ovo', average='weighted')\n",
    "    roc_auc_results.append(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_tuned = list(zip(names, acc_results, roc_auc_results))\n",
    "model_tuned_df = pd.DataFrame(list_tuned,\n",
    "                  columns = ['Model', 'Accuracy', \"ROC_AUC\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.816216</td>\n",
       "      <td>0.693660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adaboost</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.702960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.684700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Trees</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.708526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.729636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.758756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ExtraTrees</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.737103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Voting_classifier</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.741447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GradientBoost</td>\n",
       "      <td>0.789189</td>\n",
       "      <td>0.787334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.686486</td>\n",
       "      <td>0.482283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy   ROC_AUC\n",
       "5  Logistic Regression  0.816216  0.693660\n",
       "0             Adaboost  0.810811  0.702960\n",
       "2           GaussianNB  0.810811  0.684700\n",
       "3       Decision Trees  0.810811  0.708526\n",
       "4        Random Forest  0.810811  0.729636\n",
       "6              XGBoost  0.810811  0.758756\n",
       "8           ExtraTrees  0.810811  0.737103\n",
       "9    Voting_classifier  0.810811  0.741447\n",
       "7        GradientBoost  0.789189  0.787334\n",
       "1                  KNN  0.686486  0.482283"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tuned_df.sort_values(by = \"Accuracy\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_results(classifier, output_fileName):\n",
    "    predict_results = classifier.predict(test_data)\n",
    "    submission = pd.read_csv(\"sample_submission.csv\")\n",
    "    submission[\"Loan_Status\"] = predict_results\n",
    "    submission[\"Loan_ID\"] = test_data_[\"Loan_ID\"]\n",
    "    submission[\"Loan_Status\"].replace(0, \"N\", inplace = True)\n",
    "    submission[\"Loan_Status\"].replace(1, \"Y\", inplace = True)\n",
    "    pd.DataFrame(submission, columns=[\"Loan_ID\", \"Loan_Status\"]).to_csv(output_fileName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adaboost classifier results\n",
    "predict_results(gsABC.best_estimator_, \"Adaboost_output.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gradientboosting classifier result\n",
    "predict_results(gsGBC.best_estimator_, \"GradientBoosting_output_.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGBOOST classifier result\n",
    "predict_results( gsXBC.best_estimator_, \"XGBoost_output.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression result\n",
    "predict_results(logreg_cv.best_estimator_, \"LogisticRegression_output.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest result\n",
    "predict_results(tuning.best_estimator_, \"RandomForest_output.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GaussianNB Classifier result\n",
    "predict_results(clf.best_estimator_, \"GNB_output_.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Voting classifier\n",
    "predict_results(v_best, \"voting_classifier_.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_results(knn_best, \"KNN.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_results(ext_best, \"extratrees.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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