tab-hack-2-0.ipynb

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     "text": [
      "/kaggle/input/tab-hack-20/Week8_test.csv\n",
      "/kaggle/input/tab-hack-20/Week8_train.csv\n"
     ]
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option(\"display.max_columns\",100)\n",
    "\n",
    "#Classification\n",
    "from sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier, AdaBoostClassifier, RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix,precision_score,recall_score,f1_score\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error,r2_score,mean_absolute_error\n",
    "from sklearn.preprocessing import normalize,scale"
   ]
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   "source": [
    "train = pd.read_csv('../input/tab-hack-20/Week8_train.csv')\n",
    "train_labels=train['TARGET']\n",
    "train.drop(columns=['PARTY_ID','TARGET'], inplace=True)\n",
    "\n",
    "test = pd.read_csv('../input/tab-hack-20/Week8_test.csv')\n",
    "test.drop(columns=['PARTY_ID'], inplace=True)\n",
    "\n",
    "df=train.append(test)"
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    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 4,
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   "source": [
    "set(train.columns)-set(test.columns)"
   ]
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   "source": [
    "## EDA"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "(221798, 481)"
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   "source": [
    "df.shape"
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42.335315</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.010373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93638.46286</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>264.225940</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.580345</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Hotels, Restaurants &amp; Leisure</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2248.357090</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103.288586</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>487231.87770</td>\n",
       "      <td>0</td>\n",
       "      <td>4542.194026</td>\n",
       "      <td>376.922314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53.680217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>742.138216</td>\n",
       "      <td>251</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>181.278645</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2285.146561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>239895.66040</td>\n",
       "      <td>0</td>\n",
       "      <td>8339.656991</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31.790216</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1450.723509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.395599</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 481 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  V_1  V_2  V_3  V_4  V_5  V_6  V_7  V_8  V_9        V_10  V_11  V_12  \\\n",
       "0   0    0    0    0    0    0    0    0    0    0    0.000000     0     0   \n",
       "1   1    0    0    0    0    0    0    0    0    0  110.038907     0     5   \n",
       "2   2    0    0    0    0    0    0    0    0    0    0.000000     0     0   \n",
       "3   3    0    0    0    0    2    0    0    0    0  103.288586     0     4   \n",
       "4   4    0    0    0    0    0    0    0    0    0    0.000000     0     0   \n",
       "\n",
       "   V_13  V_14  V_15  V_16          V_17  V_18         V_19         V_20  V_21  \\\n",
       "0     0     1     0     0  111139.07210     0   520.927066   822.838929     1   \n",
       "1     0     0     0     0   33404.59288     0  1260.420076  1914.446743     0   \n",
       "2     0     0     0     0   93638.46286     0     0.000000   264.225940     1   \n",
       "3     0     0     0     3  487231.87770     0  4542.194026   376.922314     0   \n",
       "4     0     0     1     0  239895.66040     0  8339.656991     0.000000     1   \n",
       "\n",
       "   V_22        V_23  V_24  V_25  V_26  V_27  V_28  V_29  V_30  V_31  V_32  \\\n",
       "0     0  110.906416     0     0     0     0   0.0     3     0   0.0     1   \n",
       "1     0    8.334129     0     0     0     0   0.0     1     0   0.0     0   \n",
       "2     0    0.000000     0     0     0     0   0.0     1     0   0.0     0   \n",
       "3     0    0.000000     0     0     0     0   0.0     0     0   0.0     0   \n",
       "4     0   31.790216     0     0     0     0   0.0     0     0   0.0     2   \n",
       "\n",
       "   V_33  V_34  V_35        V_36  V_37  V_38         V_39  V_40  V_41  V_42  \\\n",
       "0   640     0     0  128.140948     0     0   151.719648   0.0     0     0   \n",
       "1    13     0     0   43.323947     0     0     0.000000   0.0     0     0   \n",
       "2    21     0     0  108.580345     0     0     0.000000   0.0     0     0   \n",
       "3    91     0     0    0.000000     0     0    53.680217   0.0     0     0   \n",
       "4    14     0     0    0.000000     0     0  1450.723509   0.0     0     0   \n",
       "\n",
       "   V_43  V_44  V_45  V_46  V_47  V_48  V_49  ...  V_433  \\\n",
       "0     0   0.0     0     0     0     0     0  ...      0   \n",
       "1     0   0.0     0     0     0     0     0  ...      0   \n",
       "2     0   0.0     0     0     0     0     0  ...      0   \n",
       "3     0   0.0     0     0     0     0     0  ...      0   \n",
       "4     0   0.0     1     0     2     0     0  ...      1   \n",
       "\n",
       "                           V_434  V_435  V_436  V_437       V_438  V_439  \\\n",
       "0            Aerospace & Defense      0      0      0    0.000000      2   \n",
       "1       Food & Staples Retailing      0      1      0   42.335315      5   \n",
       "2  Hotels, Restaurants & Leisure      0      0      0    0.000000      0   \n",
       "3                            NaN      0      0      0  742.138216    251   \n",
       "4       Industrial Conglomerates      0      0      1    0.000000     11   \n",
       "\n",
       "   V_440  V_441  V_442  V_443  V_444  V_445  V_446  V_447  V_448  V_449  \\\n",
       "0      5      0    0.0      0      0      0      0      0      0      0   \n",
       "1      3      0    0.0      0      0      0      0      0      0      0   \n",
       "2      4      0    0.0      0      0      0      0      0      0      0   \n",
       "3      0      0    0.0      0      0      0      0      0      0      0   \n",
       "4      0      0    0.0      0      0      0      3      0      0      0   \n",
       "\n",
       "        V_450  V_451  V_452  V_453  V_454  V_455  V_456  V_457  V_458  V_459  \\\n",
       "0  384.761680      0    0.0      0      0      0    0.0    0.0    0.0      0   \n",
       "1    0.000000      0    0.0      0      0      0    0.0    0.0    0.0      0   \n",
       "2    0.000000      0    0.0      0      0      0    0.0    0.0    0.0      0   \n",
       "3  181.278645      0    0.0      0      1      0    0.0    0.0    0.0      0   \n",
       "4    0.000000      0    0.0      0      1      0    0.0    0.0    0.0      0   \n",
       "\n",
       "   V_460  V_461  V_462  V_463  V_464  V_465  V_466  V_467  V_468  V_469  \\\n",
       "0      0      0    0.0      0      0      5      0      0      0    0.0   \n",
       "1      0      0    0.0      0      0      0      0      0      0    0.0   \n",
       "2      0      0    0.0      0      0      0      0      0      0    0.0   \n",
       "3      0      0    0.0      0      0      0      0      0      0    0.0   \n",
       "4      2      0    0.0      0      0      0      0      0      0    0.0   \n",
       "\n",
       "   V_470  V_471  V_472  V_473  V_474  V_475  V_476  V_477  V_478  V_479  \\\n",
       "0      0      0      0      0      0      0      0      0      1      0   \n",
       "1      0      0      0      0      0      0      0      0      0      2   \n",
       "2      0      0      0      0      0      0      0      0      1      0   \n",
       "3      0      0      0      0      0      0      0      0      0      0   \n",
       "4      0      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "         V_480  V_481  V_482  \n",
       "0     0.000000    0.0    0.0  \n",
       "1    26.010373    0.0    0.0  \n",
       "2  2248.357090    0.0    0.0  \n",
       "3  2285.146561    0.0    0.0  \n",
       "4    98.395599    0.0    0.0  \n",
       "\n",
       "[5 rows x 481 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0252f245",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:15:50.590322Z",
     "iopub.status.busy": "2022-08-30T13:15:50.589857Z",
     "iopub.status.idle": "2022-08-30T13:15:50.629493Z",
     "shell.execute_reply": "2022-08-30T13:15:50.628193Z"
    },
    "papermill": {
     "duration": 0.053135,
     "end_time": "2022-08-30T13:15:50.632737",
     "exception": false,
     "start_time": "2022-08-30T13:15:50.579602",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 221798 entries, 0 to 110898\n",
      "Columns: 481 entries, id to V_482\n",
      "dtypes: float64(98), int64(375), object(8)\n",
      "memory usage: 815.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34978379",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:15:50.651919Z",
     "iopub.status.busy": "2022-08-30T13:15:50.651383Z",
     "iopub.status.idle": "2022-08-30T13:15:51.763171Z",
     "shell.execute_reply": "2022-08-30T13:15:51.761848Z"
    },
    "papermill": {
     "duration": 1.124737,
     "end_time": "2022-08-30T13:15:51.765961",
     "exception": false,
     "start_time": "2022-08-30T13:15:50.641224",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V_279    221796\n",
       "V_17     221785\n",
       "V_165    221782\n",
       "V_307    221758\n",
       "V_359    210560\n",
       "V_303    210406\n",
       "V_281    208707\n",
       "V_294    208582\n",
       "V_19     206783\n",
       "V_103    206216\n",
       "V_356    201962\n",
       "V_341    201848\n",
       "V_424    193026\n",
       "V_20     192996\n",
       "V_219    138282\n",
       "V_181    121235\n",
       "id       110899\n",
       "V_480    103375\n",
       "V_36     103075\n",
       "V_110    100395\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique().sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51c3e1eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:15:51.787710Z",
     "iopub.status.busy": "2022-08-30T13:15:51.787186Z",
     "iopub.status.idle": "2022-08-30T13:15:59.604715Z",
     "shell.execute_reply": "2022-08-30T13:15:59.603228Z"
    },
    "papermill": {
     "duration": 7.833323,
     "end_time": "2022-08-30T13:15:59.607916",
     "exception": false,
     "start_time": "2022-08-30T13:15:51.774593",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 7200x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(100,10))\n",
    "df.nunique().sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e554bd27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:15:59.632765Z",
     "iopub.status.busy": "2022-08-30T13:15:59.632227Z",
     "iopub.status.idle": "2022-08-30T13:16:00.791163Z",
     "shell.execute_reply": "2022-08-30T13:16:00.789719Z"
    },
    "papermill": {
     "duration": 1.176122,
     "end_time": "2022-08-30T13:16:00.794070",
     "exception": false,
     "start_time": "2022-08-30T13:15:59.617948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()['V_385'] # From graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07ef804f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:00.815945Z",
     "iopub.status.busy": "2022-08-30T13:16:00.815377Z",
     "iopub.status.idle": "2022-08-30T13:16:03.094346Z",
     "shell.execute_reply": "2022-08-30T13:16:03.092806Z"
    },
    "papermill": {
     "duration": 2.293839,
     "end_time": "2022-08-30T13:16:03.097784",
     "exception": false,
     "start_time": "2022-08-30T13:16:00.803945",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "object_cols = df.nunique()[df.nunique()<25].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "599ad20b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:03.120513Z",
     "iopub.status.busy": "2022-08-30T13:16:03.120027Z",
     "iopub.status.idle": "2022-08-30T13:16:03.124977Z",
     "shell.execute_reply": "2022-08-30T13:16:03.124048Z"
    },
    "papermill": {
     "duration": 0.019248,
     "end_time": "2022-08-30T13:16:03.127165",
     "exception": false,
     "start_time": "2022-08-30T13:16:03.107917",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df.loc[:, object_cols] = df[object_cols].astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a960a85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:03.149475Z",
     "iopub.status.busy": "2022-08-30T13:16:03.148972Z",
     "iopub.status.idle": "2022-08-30T13:16:03.167321Z",
     "shell.execute_reply": "2022-08-30T13:16:03.165926Z"
    },
    "papermill": {
     "duration": 0.032781,
     "end_time": "2022-08-30T13:16:03.169995",
     "exception": false,
     "start_time": "2022-08-30T13:16:03.137214",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 221798 entries, 0 to 110898\n",
      "Columns: 481 entries, id to V_482\n",
      "dtypes: float64(98), int64(375), object(8)\n",
      "memory usage: 815.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41eef0a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:03.192460Z",
     "iopub.status.busy": "2022-08-30T13:16:03.191166Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V_301    12536\n",
       "V_304     3015\n",
       "V_434    12670\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()[df.isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76288f23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:03.847025Z",
     "iopub.status.busy": "2022-08-30T13:16:03.845839Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 221798 entries, 0 to 110898\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   V_301   209262 non-null  object\n",
      " 1   V_304   218783 non-null  object\n",
      " 2   V_434   209128 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df[['V_301', 'V_304', 'V_434']].info() # we can fill with NaN string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "992d680c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:04.984848Z",
     "iopub.status.busy": "2022-08-30T13:16:04.983869Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df.fillna('NaN', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a29ece91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:05.013916Z",
     "iopub.status.busy": "2022-08-30T13:16:05.013028Z",
     "iopub.status.idle": "2022-08-30T13:16:05.023628Z",
     "shell.execute_reply": "2022-08-30T13:16:05.022330Z"
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     "status": "completed"
    },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    89367\n",
       "1    21532\n",
       "Name: TARGET, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64290e81",
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     "duration": 0.009564,
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     "start_time": "2022-08-30T13:16:05.036472",
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    "tags": []
   },
   "source": [
    "## Training and Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f536471d",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 221798 entries, 0 to 110898\n",
      "Columns: 481 entries, id to V_482\n",
      "dtypes: float64(98), int64(375), object(8)\n",
      "memory usage: 815.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16c19dda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:05.127387Z",
     "iopub.status.busy": "2022-08-30T13:16:05.126811Z",
     "iopub.status.idle": "2022-08-30T13:16:07.525532Z",
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     "end_time": "2022-08-30T13:16:07.528894",
     "exception": false,
     "start_time": "2022-08-30T13:16:05.114474",
     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "df_dummies = pd.get_dummies(df.drop(columns='id'), drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "398ad467",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:07.551765Z",
     "iopub.status.busy": "2022-08-30T13:16:07.551231Z",
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     "shell.execute_reply": "2022-08-30T13:16:07.556917Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train = df_dummies[:train.shape[0]]\n",
    "df_test = df_dummies[train.shape[0]:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "458c4bde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:07.583653Z",
     "iopub.status.busy": "2022-08-30T13:16:07.582584Z",
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     "start_time": "2022-08-30T13:16:07.570751",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df_train, train_labels, train_size=0.2, random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "da73ecb7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:08.754143Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8264201983769162,\n",
       " 0.6585453242565389,\n",
       " 0.21409435061153173,\n",
       " 0.32313642756680727)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbc=XGBClassifier()\n",
    "predict = xgbc.fit(x_train, y_train).predict(x_test)\n",
    "accuracy_score(y_test,predict), precision_score(y_test,predict), recall_score(y_test,predict), f1_score(y_test,predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e9d0aa3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:16:38.994856Z",
     "iopub.status.busy": "2022-08-30T13:16:38.994340Z",
     "iopub.status.idle": "2022-08-30T13:19:09.871408Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred = xgbc.fit(df_train, train_labels).predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a1ef928b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-30T13:19:09.899734Z",
     "iopub.status.busy": "2022-08-30T13:19:09.899191Z",
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     "shell.execute_reply": "2022-08-30T13:19:10.037541Z"
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     "status": "completed"
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   },
   "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>id</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   id  TARGET\n",
       "0   0       0\n",
       "1   1       0\n",
       "2   2       0\n",
       "3   3       0\n",
       "4   4       0"
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "submission = pd.DataFrame(train.id)\n",
    "submission['TARGET'] = pred\n",
    "submission.to_csv('submission.csv', index=False)\n",
    "submission.head()"
   ]
  },
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