main project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train_qnU1GcL.csv')\n",
    "test = pd.read_csv('test_LxCaReE_DvdCKVT_v2s0Z4r.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((79853, 12), (34224, 11))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110936</td>\n",
       "      <td>0.429</td>\n",
       "      <td>12058</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.02</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41492</td>\n",
       "      <td>0.010</td>\n",
       "      <td>21546</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31300</td>\n",
       "      <td>0.917</td>\n",
       "      <td>17531</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19415</td>\n",
       "      <td>0.049</td>\n",
       "      <td>15341</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99379</td>\n",
       "      <td>0.052</td>\n",
       "      <td>31400</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0  110936                             0.429        12058  355060   \n",
       "1   41492                             0.010        21546  315150   \n",
       "2   31300                             0.917        17531   84140   \n",
       "3   19415                             0.049        15341  250510   \n",
       "4   99379                             0.052        31400  198680   \n",
       "\n",
       "   Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                    0.0                     0.0   \n",
       "1                    0.0                     0.0   \n",
       "2                    2.0                     3.0   \n",
       "3                    0.0                     0.0   \n",
       "4                    0.0                     0.0   \n",
       "\n",
       "   Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                             0.0                           99.02   \n",
       "1                             0.0                           99.89   \n",
       "2                             1.0                           98.69   \n",
       "3                             0.0                           99.57   \n",
       "4                             0.0                           99.87   \n",
       "\n",
       "   no_of_premiums_paid sourcing_channel residence_area_type  target  \n",
       "0                   13                C               Urban       1  \n",
       "1                   21                A               Urban       1  \n",
       "2                    7                C               Rural       0  \n",
       "3                    9                A               Urban       1  \n",
       "4                   12                B               Urban       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>0.001</td>\n",
       "      <td>27384</td>\n",
       "      <td>51150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>23735</td>\n",
       "      <td>285140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.93</td>\n",
       "      <td>19</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17170</td>\n",
       "      <td>186030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>16068</td>\n",
       "      <td>123540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>10591</td>\n",
       "      <td>200020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.17</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0    649                             0.001        27384   51150   \n",
       "1  81136                             0.124        23735  285140   \n",
       "2  70762                             1.000        17170  186030   \n",
       "3  53935                             0.198        16068  123540   \n",
       "4  15476                             0.041        10591  200020   \n",
       "\n",
       "   Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                    0.0                     0.0   \n",
       "1                    0.0                     0.0   \n",
       "2                    0.0                     0.0   \n",
       "3                    0.0                     0.0   \n",
       "4                    1.0                     0.0   \n",
       "\n",
       "   Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                             0.0                           99.89   \n",
       "1                             0.0                           98.93   \n",
       "2                             0.0                             NaN   \n",
       "3                             0.0                           99.00   \n",
       "4                             0.0                           99.17   \n",
       "\n",
       "   no_of_premiums_paid sourcing_channel residence_area_type  \n",
       "0                    7                A               Rural  \n",
       "1                   19                A               Urban  \n",
       "2                    2                B               Urban  \n",
       "3                   11                B               Rural  \n",
       "4                   14                A               Rural  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    int64\n",
       "perc_premium_paid_by_cash_credit    float64\n",
       "age_in_days                           int64\n",
       "Income                                int64\n",
       "Count_3-6_months_late               float64\n",
       "Count_6-12_months_late              float64\n",
       "Count_more_than_12_months_late      float64\n",
       "application_underwriting_score      float64\n",
       "no_of_premiums_paid                   int64\n",
       "sourcing_channel                     object\n",
       "residence_area_type                  object\n",
       "target                                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x270c3248128>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sn.distplot(train['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                     0\n",
       "perc_premium_paid_by_cash_credit       0\n",
       "age_in_days                            0\n",
       "Income                                 0\n",
       "Count_3-6_months_late                 97\n",
       "Count_6-12_months_late                97\n",
       "Count_more_than_12_months_late        97\n",
       "application_underwriting_score      2974\n",
       "no_of_premiums_paid                    0\n",
       "sourcing_channel                       0\n",
       "residence_area_type                    0\n",
       "target                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                     0\n",
       "perc_premium_paid_by_cash_credit       0\n",
       "age_in_days                            0\n",
       "Income                                 0\n",
       "Count_3-6_months_late                 31\n",
       "Count_6-12_months_late                31\n",
       "Count_more_than_12_months_late        31\n",
       "application_underwriting_score      1323\n",
       "no_of_premiums_paid                    0\n",
       "sourcing_channel                       0\n",
       "residence_area_type                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.get_dummies(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.get_dummies(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  0\n",
       "perc_premium_paid_by_cash_credit    0\n",
       "age_in_days                         0\n",
       "Income                              0\n",
       "Count_3-6_months_late               0\n",
       "Count_6-12_months_late              0\n",
       "Count_more_than_12_months_late      0\n",
       "application_underwriting_score      0\n",
       "no_of_premiums_paid                 0\n",
       "target                              0\n",
       "sourcing_channel_A                  0\n",
       "sourcing_channel_B                  0\n",
       "sourcing_channel_C                  0\n",
       "sourcing_channel_D                  0\n",
       "sourcing_channel_E                  0\n",
       "residence_area_type_Rural           0\n",
       "residence_area_type_Urban           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  0\n",
       "perc_premium_paid_by_cash_credit    0\n",
       "age_in_days                         0\n",
       "Income                              0\n",
       "Count_3-6_months_late               0\n",
       "Count_6-12_months_late              0\n",
       "Count_more_than_12_months_late      0\n",
       "application_underwriting_score      0\n",
       "no_of_premiums_paid                 0\n",
       "sourcing_channel_A                  0\n",
       "sourcing_channel_B                  0\n",
       "sourcing_channel_C                  0\n",
       "sourcing_channel_D                  0\n",
       "sourcing_channel_E                  0\n",
       "residence_area_type_Rural           0\n",
       "residence_area_type_Urban           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x270c35ed0b8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = train[[\"perc_premium_paid_by_cash_credit\",\"age_in_days\",\"Income\",\"Count_3-6_months_late\",\"Count_6-12_months_late\",\"Count_more_than_12_months_late\",\"application_underwriting_score\",\"no_of_premiums_paid\",\"target\"]].corr()\n",
    "mask = np.array(corr)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "fig,ax= plt.subplots()\n",
    "fig.set_size_inches(20,10)\n",
    "sn.heatmap(corr, mask=mask,vmax=.9, square=True,annot=True, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=train.drop('target',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train=train['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=logreg.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.937409990858202"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_train = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9380361414098406"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(Y_train,predict_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_proj6 = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_proj6['id'] = test['id']\n",
    "submission_proj6['target'] = predict_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_proj6.to_csv('submission_proj6.csv', header=True, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}