Competitions/eczac-ba-ml-challenge.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","execution_count":1,"outputs":[{"output_type":"stream","text":"/kaggle/input/trainecza/Takmlarla Paylalacak Veri Seti.csv\n/kaggle/input/eczacibasi-projesi/sample_submission.csv\n/kaggle/input/eczacibasi-projesi/Takmlarla Paylalacak Veri Seti.xlsx\n/kaggle/input/eczacibasi-projesi/id.xlsx\n/kaggle/input/testecza/id.csv\n/kaggle/input/enflasyon/Untitled 1.csv\n/kaggle/input/eczacibasiiii/Takmlarla Paylalacak Veri Seti.csv\n/kaggle/input/eczacibasiiii/id.csv\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from xgboost import XGBRegressor\nfrom sklearn.metrics import mean_squared_error \nfrom sklearn.model_selection import TimeSeriesSplit\nfrom sklearn.model_selection import cross_val_score\nfrom catboost import CatBoostRegressor\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import RandomForestRegressor\nimport matplotlib.pyplot as plt\nfrom xgboost import plot_importance\nimport seaborn as sns\nimport eli5\nfrom eli5.sklearn import PermutationImportance\nfrom sklearn.metrics import mean_squared_error,r2_score\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\nfrom lightgbm import LGBMRegressor\npd.set_option('display.max_columns', None)\nfrom sklearn.linear_model import LinearRegression,PoissonRegressor","execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style type='text/css'>\n.datatable table.frame { margin-bottom: 0; }\n.datatable table.frame thead { border-bottom: none; }\n.datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n.datatable .bool    { background: #DDDD99; }\n.datatable .object  { background: #565656; }\n.datatable .int     { background: #5D9E5D; }\n.datatable .float   { background: #4040CC; }\n.datatable .str     { background: #CC4040; }\n.datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n.datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n.datatable th:nth-child(2) { padding-left: 12px; }\n.datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n.datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n.datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n.datatable .footer { font-size: 9px; }\n.datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n</style>\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv(\"../input/trainecza/Takmlarla Paylalacak Veri Seti.csv\")\ntest = pd.read_csv(\"../input/testecza/id.csv\")\nsubmission = pd.read_csv(\"/kaggle/input/eczacibasi-projesi/sample_submission.csv\")","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"a = train.groupby([\"customer\",\"item\",\"date\"])[\"order\"].sum()\na","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"customer  item  date   \nA         0     2018-1     216.00\n                2018-10    576.00\n                2018-11    144.00\n                2018-12     72.00\n                2018-2     576.00\n                            ...  \nC         80    2020-1      36.92\n                2020-2     221.52\n                2020-3     295.36\n                2020-6     184.60\n                2020-7     110.76\nName: order, Length: 2382, dtype: float64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"a = pd.merge(a, train, how=\"outer\",on=[\"customer\",\"item\",\"date\"])","execution_count":5,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"a = a.drop_duplicates([\"date\",\"order_x\"])","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"a = a.drop([\"order_y\"], axis=1).rename({\"order_x\":\"order\"} , axis=1)","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = a.copy()","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train[\"Year\"] = train.date.str[:4]\ntest[\"Year\"]= test.date.str[:4]\n\ntrain[\"Month\"] = train.date.str[5:]\ntest[\"Month\"] = test.date.str[5:]","execution_count":9,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.Year = train.Year.astype(\"int\")\ntrain.Month = train.Month.astype(\"int\")\n\ntest.Year = test.Year.astype(\"int\")\ntest.Month = test.Month.astype(\"int\")\n\n#train = train.drop([\"date\"], axis=1)\n#test = test.drop([\"date\"], axis=1)","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"  customer  item     date  order  Year  Month\n0        A     0   2018-1  216.0  2018      1\n2        A     0  2018-10  576.0  2018     10\n6        A     0  2018-11  144.0  2018     11\n8        A     0  2018-12   72.0  2018     12\n9        A     0   2018-2  576.0  2018      2","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>customer</th>\n      <th>item</th>\n      <th>date</th>\n      <th>order</th>\n      <th>Year</th>\n      <th>Month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-1</td>\n      <td>216.0</td>\n      <td>2018</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-10</td>\n      <td>576.0</td>\n      <td>2018</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-11</td>\n      <td>144.0</td>\n      <td>2018</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-12</td>\n      <td>72.0</td>\n      <td>2018</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-2</td>\n      <td>576.0</td>\n      <td>2018</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.loc[train[\"Month\"].isin([1,2,3]),\"quarter\"]=1\ntrain.loc[train[\"Month\"].isin([4,5,6]),\"quarter\"]=2\ntrain.loc[train[\"Month\"].isin([7,8,9]),\"quarter\"]=3\ntrain.loc[train[\"Month\"].isin([10,11,12]),\"quarter\"]=4\n\ntest.loc[test[\"Month\"].isin([1,2,3]),\"quarter\"]=1\ntest.loc[test[\"Month\"].isin([4,5,6]),\"quarter\"]=2\ntest.loc[test[\"Month\"].isin([7,8,9]),\"quarter\"]=3\ntest.loc[test[\"Month\"].isin([10,11,12]),\"quarter\"]=4","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.loc[(train[\"Month\"]>=3)&(train[\"Year\"]>=2020),\"with_corona\"]=1\ntrain[\"with_corona\"]=train[\"with_corona\"].fillna(0)\n\ntest.loc[(test[\"Month\"]>=3)&(test[\"Year\"]>=2020),\"with_corona\"]=1\ntest[\"with_corona\"]=test[\"with_corona\"].fillna(0)","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"  customer  item     date  order  Year  Month  quarter  with_corona\n0        A     0   2018-1  216.0  2018      1      1.0          0.0\n2        A     0  2018-10  576.0  2018     10      4.0          0.0\n6        A     0  2018-11  144.0  2018     11      4.0          0.0\n8        A     0  2018-12   72.0  2018     12      4.0          0.0\n9        A     0   2018-2  576.0  2018      2      1.0          0.0","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>customer</th>\n      <th>item</th>\n      <th>date</th>\n      <th>order</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>quarter</th>\n      <th>with_corona</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-1</td>\n      <td>216.0</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-10</td>\n      <td>576.0</td>\n      <td>2018</td>\n      <td>10</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-11</td>\n      <td>144.0</td>\n      <td>2018</td>\n      <td>11</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-12</td>\n      <td>72.0</td>\n      <td>2018</td>\n      <td>12</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-2</td>\n      <td>576.0</td>\n      <td>2018</td>\n      <td>2</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.quarter.nunique()","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"3"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"agg=train.groupby([\"quarter\",\"item\"])[\"order\"].agg({\"sum\",\"mean\"}).rename({\"sum\":\"sum_item_order\",\"mean\":\"mean_item_order\"},axis=1)\n\ntrain=pd.merge(train,agg,on=[\"quarter\",\"item\"])\ntest=pd.merge(test,agg,on=[\"quarter\",\"item\"])","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"  customer  item    date   order  Year  Month  quarter  with_corona  \\\n0        A     0  2018-1   216.0  2018      1      1.0          0.0   \n1        A     0  2018-2   576.0  2018      2      1.0          0.0   \n2        A     0  2018-3   288.0  2018      3      1.0          0.0   \n3        A     0  2019-1  1008.0  2019      1      1.0          0.0   \n4        A     0  2019-2  1944.0  2019      2      1.0          0.0   \n\n   sum_item_order  mean_item_order  \n0         11304.0           1256.0  \n1         11304.0           1256.0  \n2         11304.0           1256.0  \n3         11304.0           1256.0  \n4         11304.0           1256.0  ","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>customer</th>\n      <th>item</th>\n      <th>date</th>\n      <th>order</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>quarter</th>\n      <th>with_corona</th>\n      <th>sum_item_order</th>\n      <th>mean_item_order</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-1</td>\n      <td>216.0</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-2</td>\n      <td>576.0</td>\n      <td>2018</td>\n      <td>2</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-3</td>\n      <td>288.0</td>\n      <td>2018</td>\n      <td>3</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2019-1</td>\n      <td>1008.0</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2019-2</td>\n      <td>1944.0</td>\n      <td>2019</td>\n      <td>2</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Feature engineering"},{"metadata":{"trusted":true},"cell_type":"code","source":"agg = train.groupby([\"customer\",\"item\"])[\"order\"].agg({\"min\",\"mean\",\"max\",\"sum\",\"count\"})\n\ntrain = pd.merge(train, agg, how=\"inner\", on=[\"customer\",\"item\"])","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = pd.merge(test,agg,how=\"inner\",on=[\"customer\",\"item\"])","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ntrain.shape, test.shape","execution_count":20,"outputs":[{"output_type":"execute_result","execution_count":20,"data":{"text/plain":"((2062, 15), (498, 15))"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":21,"outputs":[{"output_type":"execute_result","execution_count":21,"data":{"text/plain":"  customer  item    date   order  Year  Month  quarter  with_corona  \\\n0        A     0  2018-1   216.0  2018      1      1.0          0.0   \n1        A     0  2018-2   576.0  2018      2      1.0          0.0   \n2        A     0  2018-3   288.0  2018      3      1.0          0.0   \n3        A     0  2019-1  1008.0  2019      1      1.0          0.0   \n4        A     0  2019-2  1944.0  2019      2      1.0          0.0   \n\n   sum_item_order  mean_item_order     max   min    mean  count      sum  \n0         11304.0           1256.0  3453.0  72.0  1216.7     30  36501.0  \n1         11304.0           1256.0  3453.0  72.0  1216.7     30  36501.0  \n2         11304.0           1256.0  3453.0  72.0  1216.7     30  36501.0  \n3         11304.0           1256.0  3453.0  72.0  1216.7     30  36501.0  \n4         11304.0           1256.0  3453.0  72.0  1216.7     30  36501.0  ","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>customer</th>\n      <th>item</th>\n      <th>date</th>\n      <th>order</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>quarter</th>\n      <th>with_corona</th>\n      <th>sum_item_order</th>\n      <th>mean_item_order</th>\n      <th>max</th>\n      <th>min</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>sum</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-1</td>\n      <td>216.0</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-2</td>\n      <td>576.0</td>\n      <td>2018</td>\n      <td>2</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2018-3</td>\n      <td>288.0</td>\n      <td>2018</td>\n      <td>3</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2019-1</td>\n      <td>1008.0</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A</td>\n      <td>0</td>\n      <td>2019-2</td>\n      <td>1944.0</td>\n      <td>2019</td>\n      <td>2</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"agg=train.groupby([\"customer\"])[\"order\"].agg({\"max\",\"min\",\"mean\",\"count\",\"sum\"}).rename({\"sum\":\"sum_cust\",\"max\":\"max_cust\",\"min\":\"min_cust\",\n                                                                                     \"count\":\"count_cust\",\"mean\":\"mean_cust\"},axis=1)\ntrain=pd.merge(train,agg,on=[\"customer\"])\ntest=pd.merge(test,agg,on=[\"customer\"])","execution_count":22,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.head()","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"   id customer  item     date  Year  Month  quarter  with_corona  \\\n0   1        A     0  2020-10  2020     10      4.0          1.0   \n1   2        A     0  2020-11  2020     11      4.0          1.0   \n2   3        A     0  2020-12  2020     12      4.0          1.0   \n3   4        A     0   2020-8  2020      8      3.0          1.0   \n4   5        A     0   2020-9  2020      9      3.0          1.0   \n\n   sum_item_order  mean_item_order     max   min    mean  count      sum  \\\n0          2736.0       456.000000  3453.0  72.0  1216.7     30  36501.0   \n1          2736.0       456.000000  3453.0  72.0  1216.7     30  36501.0   \n2          2736.0       456.000000  3453.0  72.0  1216.7     30  36501.0   \n3          8928.0      1275.428571  3453.0  72.0  1216.7     30  36501.0   \n4          8928.0      1275.428571  3453.0  72.0  1216.7     30  36501.0   \n\n   max_cust  min_cust    mean_cust  count_cust    sum_cust  \n0   13953.6      25.0  1923.975809        1131  2176016.64  \n1   13953.6      25.0  1923.975809        1131  2176016.64  \n2   13953.6      25.0  1923.975809        1131  2176016.64  \n3   13953.6      25.0  1923.975809        1131  2176016.64  \n4   13953.6      25.0  1923.975809        1131  2176016.64  ","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>customer</th>\n      <th>item</th>\n      <th>date</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>quarter</th>\n      <th>with_corona</th>\n      <th>sum_item_order</th>\n      <th>mean_item_order</th>\n      <th>max</th>\n      <th>min</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>sum</th>\n      <th>max_cust</th>\n      <th>min_cust</th>\n      <th>mean_cust</th>\n      <th>count_cust</th>\n      <th>sum_cust</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>A</td>\n      <td>0</td>\n      <td>2020-10</td>\n      <td>2020</td>\n      <td>10</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2736.0</td>\n      <td>456.000000</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>A</td>\n      <td>0</td>\n      <td>2020-11</td>\n      <td>2020</td>\n      <td>11</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2736.0</td>\n      <td>456.000000</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>A</td>\n      <td>0</td>\n      <td>2020-12</td>\n      <td>2020</td>\n      <td>12</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2736.0</td>\n      <td>456.000000</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>A</td>\n      <td>0</td>\n      <td>2020-8</td>\n      <td>2020</td>\n      <td>8</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>8928.0</td>\n      <td>1275.428571</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>A</td>\n      <td>0</td>\n      <td>2020-9</td>\n      <td>2020</td>\n      <td>9</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>8928.0</td>\n      <td>1275.428571</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = train.drop([\"date\"], axis=1)\ntest = test.drop([\"date\"], axis=1)\n\ntrain = pd.get_dummies(train)\ntest  = pd.get_dummies(test)\n","execution_count":24,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"   item   order  Year  Month  quarter  with_corona  sum_item_order  \\\n0     0   216.0  2018      1      1.0          0.0         11304.0   \n1     0   576.0  2018      2      1.0          0.0         11304.0   \n2     0   288.0  2018      3      1.0          0.0         11304.0   \n3     0  1008.0  2019      1      1.0          0.0         11304.0   \n4     0  1944.0  2019      2      1.0          0.0         11304.0   \n\n   mean_item_order     max   min    mean  count      sum  max_cust  min_cust  \\\n0           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n1           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n2           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n3           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n4           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n\n     mean_cust  count_cust    sum_cust  customer_A  customer_B  customer_C  \n0  1923.975809        1131  2176016.64           1           0           0  \n1  1923.975809        1131  2176016.64           1           0           0  \n2  1923.975809        1131  2176016.64           1           0           0  \n3  1923.975809        1131  2176016.64           1           0           0  \n4  1923.975809        1131  2176016.64           1           0           0  ","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>item</th>\n      <th>order</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>quarter</th>\n      <th>with_corona</th>\n      <th>sum_item_order</th>\n      <th>mean_item_order</th>\n      <th>max</th>\n      <th>min</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>sum</th>\n      <th>max_cust</th>\n      <th>min_cust</th>\n      <th>mean_cust</th>\n      <th>count_cust</th>\n      <th>sum_cust</th>\n      <th>customer_A</th>\n      <th>customer_B</th>\n      <th>customer_C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>216.0</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>576.0</td>\n      <td>2018</td>\n      <td>2</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>288.0</td>\n      <td>2018</td>\n      <td>3</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1008.0</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1944.0</td>\n      <td>2019</td>\n      <td>2</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = train.drop([\"order\",\"sum\",\"max\",\"max_cust\",\"mean\",\"sum_item_order\",\"quarter\",\"sum_cust\"], axis=1)\ny = train[\"order\"]","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X.head()","execution_count":27,"outputs":[{"output_type":"execute_result","execution_count":27,"data":{"text/plain":"   item  Year  Month  with_corona  mean_item_order   min  count  min_cust  \\\n0     0  2018      1          0.0           1256.0  72.0     30      25.0   \n1     0  2018      2          0.0           1256.0  72.0     30      25.0   \n2     0  2018      3          0.0           1256.0  72.0     30      25.0   \n3     0  2019      1          0.0           1256.0  72.0     30      25.0   \n4     0  2019      2          0.0           1256.0  72.0     30      25.0   \n\n     mean_cust  count_cust  customer_A  customer_B  customer_C  \n0  1923.975809        1131           1           0           0  \n1  1923.975809        1131           1           0           0  \n2  1923.975809        1131           1           0           0  \n3  1923.975809        1131           1           0           0  \n4  1923.975809        1131           1           0           0  ","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>item</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>with_corona</th>\n      <th>mean_item_order</th>\n      <th>min</th>\n      <th>count</th>\n      <th>min_cust</th>\n      <th>mean_cust</th>\n      <th>count_cust</th>\n      <th>customer_A</th>\n      <th>customer_B</th>\n      <th>customer_C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1256.0</td>\n      <td>72.0</td>\n      <td>30</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>2018</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>1256.0</td>\n      <td>72.0</td>\n      <td>30</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>2018</td>\n      <td>3</td>\n      <td>0.0</td>\n      <td>1256.0</td>\n      <td>72.0</td>\n      <td>30</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1256.0</td>\n      <td>72.0</td>\n      <td>30</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>2019</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>1256.0</td>\n      <td>72.0</td>\n      <td>30</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"params={'max_depth':2,\"n_estimators\":10,\"reg_lambda\":100,\"reg_alpha\":0.9}\nrmse = []\ntscv = TimeSeriesSplit(gap=0 , n_splits=5)\nr2s=[]\ntrain_r2s=[]\n\nfor train_index, test_index in tscv.split(X):\n    X_train_, X_test_ = X.iloc[train_index], X.iloc[test_index]\n    y_train_, y_test_ = y.iloc[train_index], y.iloc[test_index]\n    \n    model=LGBMRegressor(**params)\n    model.fit(X_train_, y_train_)\n    y_pred = model.predict(X_test_)\n    rmse_x = np.sqrt(mean_squared_error(y_test_, y_pred))\n    r2=r2_score(y_test_,y_pred)\n    train_r2=model.score(X_train_,y_train_)\n    rmse.append(rmse_x)\n    r2s.append(r2)\n    train_r2s.append(train_r2)\n    \n    \nprint(\"rmse:\",rmse)\nprint(\"test r2:\",r2s)\nprint(\"train r2:\",train_r2s)\nprint(\"rmse mean:\",np.mean(rmse),\n     \"train r2 mean: \",np.mean(train_r2s),\n     \"test r2 mean: \",np.mean(r2s))","execution_count":28,"outputs":[{"output_type":"stream","text":"rmse: [2211.804152201231, 1486.3634573004679, 2938.2286627482595, 2104.632316464517, 1288.7669595156865]\ntest r2: [-0.01680262210024508, 0.34522916117119096, 0.17784774695071914, 0.39506049736126814, 0.19995324817625648]\ntrain r2: [0.2677118075875353, 0.43884717143881435, 0.4179019359220568, 0.3769705210345753, 0.4180015412907704]\nrmse mean: 2005.9591096460322 train r2 mean:  0.38388659545475046 test r2 mean:  0.22025760631183794\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"   item   order  Year  Month  quarter  with_corona  sum_item_order  \\\n0     0   216.0  2018      1      1.0          0.0         11304.0   \n1     0   576.0  2018      2      1.0          0.0         11304.0   \n2     0   288.0  2018      3      1.0          0.0         11304.0   \n3     0  1008.0  2019      1      1.0          0.0         11304.0   \n4     0  1944.0  2019      2      1.0          0.0         11304.0   \n\n   mean_item_order     max   min    mean  count      sum  max_cust  min_cust  \\\n0           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n1           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n2           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n3           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n4           1256.0  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n\n     mean_cust  count_cust    sum_cust  customer_A  customer_B  customer_C  \n0  1923.975809        1131  2176016.64           1           0           0  \n1  1923.975809        1131  2176016.64           1           0           0  \n2  1923.975809        1131  2176016.64           1           0           0  \n3  1923.975809        1131  2176016.64           1           0           0  \n4  1923.975809        1131  2176016.64           1           0           0  ","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>item</th>\n      <th>order</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>quarter</th>\n      <th>with_corona</th>\n      <th>sum_item_order</th>\n      <th>mean_item_order</th>\n      <th>max</th>\n      <th>min</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>sum</th>\n      <th>max_cust</th>\n      <th>min_cust</th>\n      <th>mean_cust</th>\n      <th>count_cust</th>\n      <th>sum_cust</th>\n      <th>customer_A</th>\n      <th>customer_B</th>\n      <th>customer_C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>216.0</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>576.0</td>\n      <td>2018</td>\n      <td>2</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>288.0</td>\n      <td>2018</td>\n      <td>3</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1008.0</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1944.0</td>\n      <td>2019</td>\n      <td>2</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>11304.0</td>\n      <td>1256.0</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.head()","execution_count":30,"outputs":[{"output_type":"execute_result","execution_count":30,"data":{"text/plain":"   id  item  Year  Month  quarter  with_corona  sum_item_order  \\\n0   1     0  2020     10      4.0          1.0          2736.0   \n1   2     0  2020     11      4.0          1.0          2736.0   \n2   3     0  2020     12      4.0          1.0          2736.0   \n3   4     0  2020      8      3.0          1.0          8928.0   \n4   5     0  2020      9      3.0          1.0          8928.0   \n\n   mean_item_order     max   min    mean  count      sum  max_cust  min_cust  \\\n0       456.000000  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n1       456.000000  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n2       456.000000  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n3      1275.428571  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n4      1275.428571  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n\n     mean_cust  count_cust    sum_cust  customer_A  customer_B  customer_C  \n0  1923.975809        1131  2176016.64           1           0           0  \n1  1923.975809        1131  2176016.64           1           0           0  \n2  1923.975809        1131  2176016.64           1           0           0  \n3  1923.975809        1131  2176016.64           1           0           0  \n4  1923.975809        1131  2176016.64           1           0           0  ","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>item</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>quarter</th>\n      <th>with_corona</th>\n      <th>sum_item_order</th>\n      <th>mean_item_order</th>\n      <th>max</th>\n      <th>min</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>sum</th>\n      <th>max_cust</th>\n      <th>min_cust</th>\n      <th>mean_cust</th>\n      <th>count_cust</th>\n      <th>sum_cust</th>\n      <th>customer_A</th>\n      <th>customer_B</th>\n      <th>customer_C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>10</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2736.0</td>\n      <td>456.000000</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>11</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2736.0</td>\n      <td>456.000000</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>12</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2736.0</td>\n      <td>456.000000</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>8</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>8928.0</td>\n      <td>1275.428571</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>9</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>8928.0</td>\n      <td>1275.428571</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#X_train=train.drop([\"order\",\"sum\",\"max\",\"max_cust\",\"mean\",\"sum_item_order\",\"quarter\",\"sum_cust\"],axis=1)\n#X_test=test.drop([\"id\",\"sum\",\"max\",\"max_cust\",\"mean\",\"sum_item_order\",\"quarter\",\"sum_cust\"],axis=1)\n\nX_train=train.drop([\"order\"],axis=1)\nX_test=test.drop([\"id\"],axis=1)\ny_train=train[\"order\"]","execution_count":31,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler","execution_count":32,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"scaler=MinMaxScaler()\nX_train_scaled=scaler.fit_transform(X_train)\nX_test_scaled=scaler.transform(X_test)","execution_count":33,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model=PoissonRegressor(max_iter=3000,alpha=10)\nmodel_fitted=model.fit(X_train_scaled,y_train)\npred=model.predict(X_test_scaled)\nprint(\"Train Score:\",model.score(X_train_scaled,y_train))","execution_count":34,"outputs":[{"output_type":"stream","text":"Train Score: 0.5760888152335877\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred[:10]","execution_count":35,"outputs":[{"output_type":"execute_result","execution_count":35,"data":{"text/plain":"array([1222.83106894, 1186.47246201, 1151.19491061, 1346.25624641,\n       1306.22782143, 1405.86379509, 1121.49673563, 1088.15111658,\n       1055.79696747, 1164.3961015 ])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.head()","execution_count":36,"outputs":[{"output_type":"execute_result","execution_count":36,"data":{"text/plain":"   id  item  Year  Month  quarter  with_corona  sum_item_order  \\\n0   1     0  2020     10      4.0          1.0          2736.0   \n1   2     0  2020     11      4.0          1.0          2736.0   \n2   3     0  2020     12      4.0          1.0          2736.0   \n3   4     0  2020      8      3.0          1.0          8928.0   \n4   5     0  2020      9      3.0          1.0          8928.0   \n\n   mean_item_order     max   min    mean  count      sum  max_cust  min_cust  \\\n0       456.000000  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n1       456.000000  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n2       456.000000  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n3      1275.428571  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n4      1275.428571  3453.0  72.0  1216.7     30  36501.0   13953.6      25.0   \n\n     mean_cust  count_cust    sum_cust  customer_A  customer_B  customer_C  \n0  1923.975809        1131  2176016.64           1           0           0  \n1  1923.975809        1131  2176016.64           1           0           0  \n2  1923.975809        1131  2176016.64           1           0           0  \n3  1923.975809        1131  2176016.64           1           0           0  \n4  1923.975809        1131  2176016.64           1           0           0  ","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>item</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>quarter</th>\n      <th>with_corona</th>\n      <th>sum_item_order</th>\n      <th>mean_item_order</th>\n      <th>max</th>\n      <th>min</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>sum</th>\n      <th>max_cust</th>\n      <th>min_cust</th>\n      <th>mean_cust</th>\n      <th>count_cust</th>\n      <th>sum_cust</th>\n      <th>customer_A</th>\n      <th>customer_B</th>\n      <th>customer_C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>10</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2736.0</td>\n      <td>456.000000</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>11</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2736.0</td>\n      <td>456.000000</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>12</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2736.0</td>\n      <td>456.000000</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>8</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>8928.0</td>\n      <td>1275.428571</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>2020</td>\n      <td>9</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>8928.0</td>\n      <td>1275.428571</td>\n      <td>3453.0</td>\n      <td>72.0</td>\n      <td>1216.7</td>\n      <td>30</td>\n      <td>36501.0</td>\n      <td>13953.6</td>\n      <td>25.0</td>\n      <td>1923.975809</td>\n      <td>1131</td>\n      <td>2176016.64</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.head()","execution_count":37,"outputs":[{"output_type":"execute_result","execution_count":37,"data":{"text/plain":"   id  order\n0   1    0.0\n1   2    0.0\n2   3    0.0\n3   4    0.0\n4   5    0.0","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>order</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission[\"order\"]=pred","execution_count":38,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_csv=submission.to_csv(\"submission.csv\",index=False)\nfrom IPython.display import FileLink\nFileLink(r'submission.csv')","execution_count":39,"outputs":[{"output_type":"execute_result","execution_count":39,"data":{"text/plain":"/kaggle/working/submission.csv","text/html":"<a href='submission.csv' target='_blank'>submission.csv</a><br>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n#perm = PermutationImportance(model, random_state=0).fit(X_train,y_train)\n#eli5.show_weights(perm, feature_names = X_train.columns.tolist(),top=50)\n","execution_count":41,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plt.figure(figsize=(20,7))\n\n\n#sns.heatmap(X_train.corr(),annot=True,cmap=\"coolwarm\");","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.9","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}