feature-engine-model-building.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nfrom sklearn.model_selection import train_test_split\nfrom category_encoders import TargetEncoder\nfrom xgboost import XGBClassifier\nimport joblib","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-11-04T11:45:24.289110Z","iopub.execute_input":"2023-11-04T11:45:24.289579Z","iopub.status.idle":"2023-11-04T11:45:26.632404Z","shell.execute_reply.started":"2023-11-04T11:45:24.289544Z","shell.execute_reply":"2023-11-04T11:45:26.630924Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"aisles_df = pd.read_csv('/kaggle/input/instacart/aisles.csv')\ndepartments_df = pd.read_csv('/kaggle/input/instacart/departments.csv')\n# order_products_prior_df = pd.read_csv('order_products__prior.csv.zip')\norder_products_train_df = pd.read_csv('/kaggle/input/instacart/order_products__train.csv')\norders_df = pd.read_csv('/kaggle/input/instacart/orders.csv')\nproducts_df = pd.read_csv('/kaggle/input/instacart/products.csv')","metadata":{"execution":{"iopub.status.busy":"2023-11-04T11:45:26.635520Z","iopub.execute_input":"2023-11-04T11:45:26.637308Z","iopub.status.idle":"2023-11-04T11:45:31.392005Z","shell.execute_reply.started":"2023-11-04T11:45:26.637241Z","shell.execute_reply":"2023-11-04T11:45:31.390909Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df = order_products_train_df.merge(products_df, on='product_id').merge(orders_df, on='order_id').merge(aisles_df, on='aisle_id').merge(departments_df, on='department_id')\n","metadata":{"execution":{"iopub.status.busy":"2023-11-04T11:45:31.393352Z","iopub.execute_input":"2023-11-04T11:45:31.394172Z","iopub.status.idle":"2023-11-04T11:45:34.339595Z","shell.execute_reply.started":"2023-11-04T11:45:31.394137Z","shell.execute_reply":"2023-11-04T11:45:34.338600Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:23:17.244445Z","iopub.execute_input":"2023-11-04T07:23:17.244909Z","iopub.status.idle":"2023-11-04T07:23:17.272502Z","shell.execute_reply.started":"2023-11-04T07:23:17.244876Z","shell.execute_reply":"2023-11-04T07:23:17.271438Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"   order_id  product_id  add_to_cart_order  reordered      product_name  \\\n0         1       49302                  1          1  Bulgarian Yogurt   \n1    816049       49302                  7          1  Bulgarian Yogurt   \n2   1242203       49302                  1          1  Bulgarian Yogurt   \n3   1383349       49302                 11          1  Bulgarian Yogurt   \n4   1787378       49302                  8          0  Bulgarian Yogurt   \n\n   aisle_id  department_id  user_id eval_set  order_number  order_dow  \\\n0       120             16   112108    train             4          4   \n1       120             16    47901    train            14          4   \n2       120             16     2993    train            15          0   \n3       120             16    41425    train             4          3   \n4       120             16   187205    train             5          4   \n\n   order_hour_of_day  days_since_prior_order   aisle  department  \n0                 10                     9.0  yogurt  dairy eggs  \n1                  6                    16.0  yogurt  dairy eggs  \n2                  7                     7.0  yogurt  dairy eggs  \n3                  8                    14.0  yogurt  dairy eggs  \n4                 14                    30.0  yogurt  dairy eggs  ","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>order_id</th>\n      <th>product_id</th>\n      <th>add_to_cart_order</th>\n      <th>reordered</th>\n      <th>product_name</th>\n      <th>aisle_id</th>\n      <th>department_id</th>\n      <th>user_id</th>\n      <th>eval_set</th>\n      <th>order_number</th>\n      <th>order_dow</th>\n      <th>order_hour_of_day</th>\n      <th>days_since_prior_order</th>\n      <th>aisle</th>\n      <th>department</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>49302</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Bulgarian Yogurt</td>\n      <td>120</td>\n      <td>16</td>\n      <td>112108</td>\n      <td>train</td>\n      <td>4</td>\n      <td>4</td>\n      <td>10</td>\n      <td>9.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>816049</td>\n      <td>49302</td>\n      <td>7</td>\n      <td>1</td>\n      <td>Bulgarian Yogurt</td>\n      <td>120</td>\n      <td>16</td>\n      <td>47901</td>\n      <td>train</td>\n      <td>14</td>\n      <td>4</td>\n      <td>6</td>\n      <td>16.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1242203</td>\n      <td>49302</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Bulgarian Yogurt</td>\n      <td>120</td>\n      <td>16</td>\n      <td>2993</td>\n      <td>train</td>\n      <td>15</td>\n      <td>0</td>\n      <td>7</td>\n      <td>7.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1383349</td>\n      <td>49302</td>\n      <td>11</td>\n      <td>1</td>\n      <td>Bulgarian Yogurt</td>\n      <td>120</td>\n      <td>16</td>\n      <td>41425</td>\n      <td>train</td>\n      <td>4</td>\n      <td>3</td>\n      <td>8</td>\n      <td>14.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1787378</td>\n      <td>49302</td>\n      <td>8</td>\n      <td>0</td>\n      <td>Bulgarian Yogurt</td>\n      <td>120</td>\n      <td>16</td>\n      <td>187205</td>\n      <td>train</td>\n      <td>5</td>\n      <td>4</td>\n      <td>14</td>\n      <td>30.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.shape","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:25:34.724087Z","iopub.execute_input":"2023-11-04T07:25:34.725000Z","iopub.status.idle":"2023-11-04T07:25:34.732554Z","shell.execute_reply.started":"2023-11-04T07:25:34.724954Z","shell.execute_reply":"2023-11-04T07:25:34.731429Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"(1384617, 15)"},"metadata":{}}]},{"cell_type":"code","source":"df = df.drop(['aisle_id','department_id','product_id','eval_set'],axis=1)\ndf = df.drop_duplicates()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:25:58.132380Z","iopub.execute_input":"2023-11-04T07:25:58.132910Z","iopub.status.idle":"2023-11-04T07:25:59.529220Z","shell.execute_reply.started":"2023-11-04T07:25:58.132868Z","shell.execute_reply":"2023-11-04T07:25:59.527936Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"df.shape","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:26:18.651579Z","iopub.execute_input":"2023-11-04T07:26:18.652057Z","iopub.status.idle":"2023-11-04T07:26:18.660480Z","shell.execute_reply.started":"2023-11-04T07:26:18.652013Z","shell.execute_reply":"2023-11-04T07:26:18.659216Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"(1384617, 11)"},"metadata":{}}]},{"cell_type":"code","source":"df = df.drop(['order_id'],axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:27:38.109399Z","iopub.execute_input":"2023-11-04T07:27:38.109892Z","iopub.status.idle":"2023-11-04T07:27:38.183145Z","shell.execute_reply.started":"2023-11-04T07:27:38.109854Z","shell.execute_reply":"2023-11-04T07:27:38.181446Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:27:57.958780Z","iopub.execute_input":"2023-11-04T07:27:57.959171Z","iopub.status.idle":"2023-11-04T07:27:58.308580Z","shell.execute_reply.started":"2023-11-04T07:27:57.959137Z","shell.execute_reply":"2023-11-04T07:27:58.307781Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"       add_to_cart_order     reordered       user_id  order_number  \\\ncount       1.384617e+06  1.384617e+06  1.384617e+06  1.384617e+06   \nmean        8.758044e+00  5.985944e-01  1.031128e+05  1.709141e+01   \nstd         7.423936e+00  4.901829e-01  5.948715e+04  1.661404e+01   \nmin         1.000000e+00  0.000000e+00  1.000000e+00  4.000000e+00   \n25%         3.000000e+00  0.000000e+00  5.173200e+04  6.000000e+00   \n50%         7.000000e+00  1.000000e+00  1.029330e+05  1.100000e+01   \n75%         1.200000e+01  1.000000e+00  1.549590e+05  2.100000e+01   \nmax         8.000000e+01  1.000000e+00  2.062090e+05  1.000000e+02   \n\n          order_dow  order_hour_of_day  days_since_prior_order  \ncount  1.384617e+06       1.384617e+06            1.384617e+06  \nmean   2.701392e+00       1.357759e+01            1.706613e+01  \nstd    2.167646e+00       4.238458e+00            1.042642e+01  \nmin    0.000000e+00       0.000000e+00            0.000000e+00  \n25%    1.000000e+00       1.000000e+01            7.000000e+00  \n50%    3.000000e+00       1.400000e+01            1.500000e+01  \n75%    5.000000e+00       1.700000e+01            3.000000e+01  \nmax    6.000000e+00       2.300000e+01            3.000000e+01  ","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>add_to_cart_order</th>\n      <th>reordered</th>\n      <th>user_id</th>\n      <th>order_number</th>\n      <th>order_dow</th>\n      <th>order_hour_of_day</th>\n      <th>days_since_prior_order</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1.384617e+06</td>\n      <td>1.384617e+06</td>\n      <td>1.384617e+06</td>\n      <td>1.384617e+06</td>\n      <td>1.384617e+06</td>\n      <td>1.384617e+06</td>\n      <td>1.384617e+06</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>8.758044e+00</td>\n      <td>5.985944e-01</td>\n      <td>1.031128e+05</td>\n      <td>1.709141e+01</td>\n      <td>2.701392e+00</td>\n      <td>1.357759e+01</td>\n      <td>1.706613e+01</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>7.423936e+00</td>\n      <td>4.901829e-01</td>\n      <td>5.948715e+04</td>\n      <td>1.661404e+01</td>\n      <td>2.167646e+00</td>\n      <td>4.238458e+00</td>\n      <td>1.042642e+01</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>1.000000e+00</td>\n      <td>4.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>3.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>5.173200e+04</td>\n      <td>6.000000e+00</td>\n      <td>1.000000e+00</td>\n      <td>1.000000e+01</td>\n      <td>7.000000e+00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>7.000000e+00</td>\n      <td>1.000000e+00</td>\n      <td>1.029330e+05</td>\n      <td>1.100000e+01</td>\n      <td>3.000000e+00</td>\n      <td>1.400000e+01</td>\n      <td>1.500000e+01</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.200000e+01</td>\n      <td>1.000000e+00</td>\n      <td>1.549590e+05</td>\n      <td>2.100000e+01</td>\n      <td>5.000000e+00</td>\n      <td>1.700000e+01</td>\n      <td>3.000000e+01</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>8.000000e+01</td>\n      <td>1.000000e+00</td>\n      <td>2.062090e+05</td>\n      <td>1.000000e+02</td>\n      <td>6.000000e+00</td>\n      <td>2.300000e+01</td>\n      <td>3.000000e+01</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:28:24.534469Z","iopub.execute_input":"2023-11-04T07:28:24.534872Z","iopub.status.idle":"2023-11-04T07:28:24.981642Z","shell.execute_reply.started":"2023-11-04T07:28:24.534840Z","shell.execute_reply":"2023-11-04T07:28:24.980562Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1384617 entries, 0 to 1384616\nData columns (total 10 columns):\n #   Column                  Non-Null Count    Dtype  \n---  ------                  --------------    -----  \n 0   add_to_cart_order       1384617 non-null  int64  \n 1   reordered               1384617 non-null  int64  \n 2   product_name            1384617 non-null  object \n 3   user_id                 1384617 non-null  int64  \n 4   order_number            1384617 non-null  int64  \n 5   order_dow               1384617 non-null  int64  \n 6   order_hour_of_day       1384617 non-null  int64  \n 7   days_since_prior_order  1384617 non-null  float64\n 8   aisle                   1384617 non-null  object \n 9   department              1384617 non-null  object \ndtypes: float64(1), int64(6), object(3)\nmemory usage: 105.6+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"# Define the data type mappings for the columns\ndata_type_mappings = {\n    'add_to_cart_order': np.int16,\n    'reordered':  np.int8,\n    'product_name': str,\n    'user_id': int,\n    'order_number': np.int16,\n    'order_dow': np.int8,\n    'order_hour_of_day': np.int8,\n    'days_since_prior_order': np.float32,  \n    'aisle': str,\n    'department': str\n}\n\n# Apply the data type mappings to the DataFrame\nfor column, data_type in data_type_mappings.items():\n    df[column] = df[column].astype(data_type)\n","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:31:47.558839Z","iopub.execute_input":"2023-11-04T07:31:47.560265Z","iopub.status.idle":"2023-11-04T07:31:47.750975Z","shell.execute_reply.started":"2023-11-04T07:31:47.560196Z","shell.execute_reply":"2023-11-04T07:31:47.749714Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:32:07.017695Z","iopub.execute_input":"2023-11-04T07:32:07.018065Z","iopub.status.idle":"2023-11-04T07:32:07.482685Z","shell.execute_reply.started":"2023-11-04T07:32:07.018033Z","shell.execute_reply":"2023-11-04T07:32:07.481700Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1384617 entries, 0 to 1384616\nData columns (total 10 columns):\n #   Column                  Non-Null Count    Dtype  \n---  ------                  --------------    -----  \n 0   add_to_cart_order       1384617 non-null  int16  \n 1   reordered               1384617 non-null  int8   \n 2   product_name            1384617 non-null  object \n 3   user_id                 1384617 non-null  int64  \n 4   order_number            1384617 non-null  int16  \n 5   order_dow               1384617 non-null  int8   \n 6   order_hour_of_day       1384617 non-null  int8   \n 7   days_since_prior_order  1384617 non-null  float32\n 8   aisle                   1384617 non-null  object \n 9   department              1384617 non-null  object \ndtypes: float32(1), int16(2), int64(1), int8(3), object(3)\nmemory usage: 56.8+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:37:37.095102Z","iopub.execute_input":"2023-11-04T07:37:37.095483Z","iopub.status.idle":"2023-11-04T07:37:37.559657Z","shell.execute_reply.started":"2023-11-04T07:37:37.095454Z","shell.execute_reply":"2023-11-04T07:37:37.558808Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"add_to_cart_order         0\nreordered                 0\nproduct_name              0\nuser_id                   0\norder_number              0\norder_dow                 0\norder_hour_of_day         0\ndays_since_prior_order    0\naisle                     0\ndepartment                0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"user_features = df.groupby('user_id').agg({\n    'order_number': 'mean',\n    'days_since_prior_order': 'mean'\n}).reset_index()\ndata = df.merge(user_features, on='user_id', suffixes=('', '_user'))","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:39:16.430815Z","iopub.execute_input":"2023-11-04T07:39:16.431216Z","iopub.status.idle":"2023-11-04T07:39:16.985964Z","shell.execute_reply.started":"2023-11-04T07:39:16.431185Z","shell.execute_reply":"2023-11-04T07:39:16.984862Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"data.shape","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:40:28.484726Z","iopub.execute_input":"2023-11-04T07:40:28.485051Z","iopub.status.idle":"2023-11-04T07:40:28.492033Z","shell.execute_reply.started":"2023-11-04T07:40:28.485023Z","shell.execute_reply":"2023-11-04T07:40:28.490921Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"(1384617, 12)"},"metadata":{}}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T07:42:16.624641Z","iopub.execute_input":"2023-11-04T07:42:16.625306Z","iopub.status.idle":"2023-11-04T07:42:17.085511Z","shell.execute_reply.started":"2023-11-04T07:42:16.625269Z","shell.execute_reply":"2023-11-04T07:42:17.084441Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1384617 entries, 0 to 1384616\nData columns (total 12 columns):\n #   Column                       Non-Null Count    Dtype  \n---  ------                       --------------    -----  \n 0   add_to_cart_order            1384617 non-null  int16  \n 1   reordered                    1384617 non-null  int8   \n 2   product_name                 1384617 non-null  object \n 3   user_id                      1384617 non-null  int64  \n 4   order_number                 1384617 non-null  int16  \n 5   order_dow                    1384617 non-null  int8   \n 6   order_hour_of_day            1384617 non-null  int8   \n 7   days_since_prior_order       1384617 non-null  float32\n 8   aisle                        1384617 non-null  object \n 9   department                   1384617 non-null  object \n 10  order_number_user            1384617 non-null  float64\n 11  days_since_prior_order_user  1384617 non-null  float32\ndtypes: float32(2), float64(1), int16(2), int64(1), int8(3), object(3)\nmemory usage: 72.6+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T11:21:52.473713Z","iopub.execute_input":"2023-11-04T11:21:52.474165Z","iopub.status.idle":"2023-11-04T11:21:52.491688Z","shell.execute_reply.started":"2023-11-04T11:21:52.474132Z","shell.execute_reply":"2023-11-04T11:21:52.490582Z"},"trusted":true},"execution_count":141,"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":"   add_to_cart_order  reordered      product_name  user_id  order_number  \\\n0                  1          1  Bulgarian Yogurt   112108             4   \n1                  7          1  Bulgarian Yogurt    47901            14   \n2                  1          1  Bulgarian Yogurt     2993            15   \n3                 11          1  Bulgarian Yogurt    41425             4   \n4                  8          0  Bulgarian Yogurt   187205             5   \n\n   order_dow  order_hour_of_day  days_since_prior_order   aisle  department  \n0          4                 10                     9.0  yogurt  dairy eggs  \n1          4                  6                    16.0  yogurt  dairy eggs  \n2          0                  7                     7.0  yogurt  dairy eggs  \n3          3                  8                    14.0  yogurt  dairy eggs  \n4          4                 14                    30.0  yogurt  dairy eggs  ","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>add_to_cart_order</th>\n      <th>reordered</th>\n      <th>product_name</th>\n      <th>user_id</th>\n      <th>order_number</th>\n      <th>order_dow</th>\n      <th>order_hour_of_day</th>\n      <th>days_since_prior_order</th>\n      <th>aisle</th>\n      <th>department</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Bulgarian Yogurt</td>\n      <td>112108</td>\n      <td>4</td>\n      <td>4</td>\n      <td>10</td>\n      <td>9.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>7</td>\n      <td>1</td>\n      <td>Bulgarian Yogurt</td>\n      <td>47901</td>\n      <td>14</td>\n      <td>4</td>\n      <td>6</td>\n      <td>16.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Bulgarian Yogurt</td>\n      <td>2993</td>\n      <td>15</td>\n      <td>0</td>\n      <td>7</td>\n      <td>7.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11</td>\n      <td>1</td>\n      <td>Bulgarian Yogurt</td>\n      <td>41425</td>\n      <td>4</td>\n      <td>3</td>\n      <td>8</td>\n      <td>14.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>0</td>\n      <td>Bulgarian Yogurt</td>\n      <td>187205</td>\n      <td>5</td>\n      <td>4</td>\n      <td>14</td>\n      <td>30.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### ReOrder from the store Model","metadata":{}},{"cell_type":"code","source":"# Create train and test datasets\nX = data[['order_number', 'days_since_prior_order', 'order_dow', 'order_hour_of_day', 'aisle', 'department', 'product_name']]\ny = data['reordered']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n# Encode aisle, department, and product_name using Target Encoding after splitting\nencoder = TargetEncoder(cols=['aisle','department','product_name'])\nX_train[['aisle','department','product_name']] = encoder.fit_transform(X_train[['aisle','department','product_name']], y_train)\nX_test[['aisle','department','product_name']] = encoder.transform(X_test[['aisle','department','product_name']])\n\n","metadata":{"execution":{"iopub.status.busy":"2023-11-04T08:15:02.897814Z","iopub.execute_input":"2023-11-04T08:15:02.898801Z","iopub.status.idle":"2023-11-04T08:15:09.372709Z","shell.execute_reply.started":"2023-11-04T08:15:02.898731Z","shell.execute_reply":"2023-11-04T08:15:09.371498Z"},"trusted":true},"execution_count":66,"outputs":[]},{"cell_type":"code","source":"X_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T08:07:18.491223Z","iopub.execute_input":"2023-11-04T08:07:18.492447Z","iopub.status.idle":"2023-11-04T08:07:18.508851Z","shell.execute_reply.started":"2023-11-04T08:07:18.492348Z","shell.execute_reply":"2023-11-04T08:07:18.507617Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"         order_number  days_since_prior_order  order_dow  order_hour_of_day  \\\n525159             18                    10.0          1                 17   \n421659             20                    17.0          4                  9   \n1300544             8                    17.0          0                 10   \n314776              4                    21.0          0                 14   \n749153              8                    30.0          0                 20   \n\n            aisle  department  product_name  \n525159   0.791408    0.675031      0.870798  \n421659   0.737746    0.665112      0.885910  \n1300544  0.400649    0.425116      0.480045  \n314776   0.529728    0.659285      0.512561  \n749153   0.737746    0.665112      0.693274  ","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>order_number</th>\n      <th>days_since_prior_order</th>\n      <th>order_dow</th>\n      <th>order_hour_of_day</th>\n      <th>aisle</th>\n      <th>department</th>\n      <th>product_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>525159</th>\n      <td>18</td>\n      <td>10.0</td>\n      <td>1</td>\n      <td>17</td>\n      <td>0.791408</td>\n      <td>0.675031</td>\n      <td>0.870798</td>\n    </tr>\n    <tr>\n      <th>421659</th>\n      <td>20</td>\n      <td>17.0</td>\n      <td>4</td>\n      <td>9</td>\n      <td>0.737746</td>\n      <td>0.665112</td>\n      <td>0.885910</td>\n    </tr>\n    <tr>\n      <th>1300544</th>\n      <td>8</td>\n      <td>17.0</td>\n      <td>0</td>\n      <td>10</td>\n      <td>0.400649</td>\n      <td>0.425116</td>\n      <td>0.480045</td>\n    </tr>\n    <tr>\n      <th>314776</th>\n      <td>4</td>\n      <td>21.0</td>\n      <td>0</td>\n      <td>14</td>\n      <td>0.529728</td>\n      <td>0.659285</td>\n      <td>0.512561</td>\n    </tr>\n    <tr>\n      <th>749153</th>\n      <td>8</td>\n      <td>30.0</td>\n      <td>0</td>\n      <td>20</td>\n      <td>0.737746</td>\n      <td>0.665112</td>\n      <td>0.693274</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"X_test.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T08:07:25.585652Z","iopub.execute_input":"2023-11-04T08:07:25.586098Z","iopub.status.idle":"2023-11-04T08:07:25.602563Z","shell.execute_reply.started":"2023-11-04T08:07:25.586068Z","shell.execute_reply":"2023-11-04T08:07:25.601444Z"},"trusted":true},"execution_count":62,"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"         order_number  days_since_prior_order  order_dow  order_hour_of_day  \\\n619147             15                     6.0          2                 14   \n1208847             8                     9.0          6                 21   \n42965              39                     6.0          6                  9   \n760387              5                     7.0          4                 19   \n416646             24                    14.0          0                 13   \n\n            aisle  department  product_name  \n619147   0.606062    0.665112      0.553846  \n1208847  0.656726    0.665112      0.526971  \n42965    0.584979    0.569850      0.628116  \n760387   0.737746    0.665112      0.792741  \n416646   0.606062    0.665112      0.567029  ","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>order_number</th>\n      <th>days_since_prior_order</th>\n      <th>order_dow</th>\n      <th>order_hour_of_day</th>\n      <th>aisle</th>\n      <th>department</th>\n      <th>product_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>619147</th>\n      <td>15</td>\n      <td>6.0</td>\n      <td>2</td>\n      <td>14</td>\n      <td>0.606062</td>\n      <td>0.665112</td>\n      <td>0.553846</td>\n    </tr>\n    <tr>\n      <th>1208847</th>\n      <td>8</td>\n      <td>9.0</td>\n      <td>6</td>\n      <td>21</td>\n      <td>0.656726</td>\n      <td>0.665112</td>\n      <td>0.526971</td>\n    </tr>\n    <tr>\n      <th>42965</th>\n      <td>39</td>\n      <td>6.0</td>\n      <td>6</td>\n      <td>9</td>\n      <td>0.584979</td>\n      <td>0.569850</td>\n      <td>0.628116</td>\n    </tr>\n    <tr>\n      <th>760387</th>\n      <td>5</td>\n      <td>7.0</td>\n      <td>4</td>\n      <td>19</td>\n      <td>0.737746</td>\n      <td>0.665112</td>\n      <td>0.792741</td>\n    </tr>\n    <tr>\n      <th>416646</th>\n      <td>24</td>\n      <td>14.0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>0.606062</td>\n      <td>0.665112</td>\n      <td>0.567029</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Define the model hyperparameters\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import StratifiedKFold\n\n\nmodel_params = {\n    'learning_rate': 0.1,\n    'n_estimators': 1000,\n    'max_depth': 4,\n    'n_jobs': -1,\n    'eta': 0.2,\n    'early_stopping_rounds': 100\n}\n\n# Define the number of folds    'verbosity': 0,\n\nn_splits = 5\n\n# Initialize the StratifiedKFold object\nskf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n\n# Initialize the lists to store the ROC AUC scores for each fold\nroc_auc_scores = []\n\n# Loop over each fold\nfor fold, (train_idx, valid_idx) in enumerate(skf.split(X_train, y_train)):\n    # Get the training and validation data for this fold\n    X_train_fold, y_train_fold = X_train.iloc[train_idx], y_train.iloc[train_idx]\n    X_valid_fold, y_valid_fold = X_train.iloc[valid_idx], y_train.iloc[valid_idx]\n\n    # Train the XGBoost model on the training data for this fold\n    xgb_model = XGBClassifier(**model_params)\n    xgb_model.fit(X_train_fold, y_train_fold, eval_set=[(X_valid_fold, y_valid_fold)], verbose=0)\n\n    # Make predictions on the validation data for this fold\n    y_pred = xgb_model.predict_proba(X_valid_fold)[:, 1]\n\n    # Calculate the ROC AUC score for this fold\n    roc_auc = roc_auc_score(y_valid_fold, y_pred)\n    roc_auc_scores.append(roc_auc)\n\n    # Print the ROC AUC score for this fold\n    print(f'Fold {fold}: ROC AUC = {roc_auc}')\n\n# Calculate and print the mean ROC AUC score across all folds\nmean_roc_auc = sum(roc_auc_scores) / n_splits\nprint(f'Mean ROC AUC = {mean_roc_auc}')","metadata":{"execution":{"iopub.status.busy":"2023-11-04T08:15:23.325722Z","iopub.execute_input":"2023-11-04T08:15:23.326148Z","iopub.status.idle":"2023-11-04T08:34:17.820985Z","shell.execute_reply.started":"2023-11-04T08:15:23.326118Z","shell.execute_reply":"2023-11-04T08:34:17.819815Z"},"trusted":true},"execution_count":67,"outputs":[{"name":"stdout","text":"Fold 0: ROC AUC = 0.7574679009123141\nFold 1: ROC AUC = 0.7559416356247333\nFold 2: ROC AUC = 0.7573801286793242\nFold 3: ROC AUC = 0.7596700975039944\nFold 4: ROC AUC = 0.755928555723051\nMean ROC AUC = 0.7572776636886834\n","output_type":"stream"}]},{"cell_type":"code","source":"joblib.dump(xgb_model,'xgb_model.pkl')","metadata":{"execution":{"iopub.status.busy":"2023-11-04T08:44:57.802567Z","iopub.execute_input":"2023-11-04T08:44:57.802994Z","iopub.status.idle":"2023-11-04T08:44:57.843452Z","shell.execute_reply.started":"2023-11-04T08:44:57.802960Z","shell.execute_reply":"2023-11-04T08:44:57.842473Z"},"trusted":true},"execution_count":68,"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":"['xgb_model.pkl']"},"metadata":{}}]},{"cell_type":"markdown","source":"## Same Product ReOrdered","metadata":{}},{"cell_type":"code","source":"# Create train and test datasets\nX = data[['user_id', 'product_name', 'order_number', 'days_since_prior_order', 'order_dow', 'order_hour_of_day']]\ny = data['reordered']  # Now, 'reordered' is whether the user reordered a specific product\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-11-04T09:12:11.050714Z","iopub.execute_input":"2023-11-04T09:12:11.051100Z","iopub.status.idle":"2023-11-04T09:12:11.496953Z","shell.execute_reply.started":"2023-11-04T09:12:11.051071Z","shell.execute_reply":"2023-11-04T09:12:11.495793Z"},"trusted":true},"execution_count":74,"outputs":[]},{"cell_type":"code","source":"encoder2 = TargetEncoder(cols=['product_name'])\nX_train[['product_name']] = encoder2.fit_transform(X_train[['product_name']], y_train)\nX_test[['product_name']] = encoder2.transform(X_test[['product_name']])\n","metadata":{"execution":{"iopub.status.busy":"2023-11-04T09:13:44.318296Z","iopub.execute_input":"2023-11-04T09:13:44.318723Z","iopub.status.idle":"2023-11-04T09:13:46.926612Z","shell.execute_reply.started":"2023-11-04T09:13:44.318692Z","shell.execute_reply":"2023-11-04T09:13:46.925625Z"},"trusted":true},"execution_count":76,"outputs":[]},{"cell_type":"code","source":"model_params = {\n    'learning_rate': 0.1,\n    'n_estimators': 1000,\n    'max_depth': 4,\n    'n_jobs': -1,\n    'eta': 0.2,\n    'early_stopping_rounds': 100\n}\n\n# Define the number of folds    'verbosity': 0,\n\nn_splits = 5\n\n# Initialize the StratifiedKFold object\nskf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n\n# Initialize the lists to store the ROC AUC scores for each fold\nroc_auc_scores = []\n\n# Loop over each fold\nfor fold, (train_idx, valid_idx) in enumerate(skf.split(X_train, y_train)):\n    # Get the training and validation data for this fold\n    X_train_fold, y_train_fold = X_train.iloc[train_idx], y_train.iloc[train_idx]\n    X_valid_fold, y_valid_fold = X_train.iloc[valid_idx], y_train.iloc[valid_idx]\n\n    # Train the XGBoost model on the training data for this fold\n    same_prod_model = XGBClassifier(**model_params)\n    same_prod_model.fit(X_train_fold, y_train_fold, eval_set=[(X_valid_fold, y_valid_fold)], verbose=0)\n\n    # Make predictions on the validation data for this fold\n    y_pred = same_prod_model.predict_proba(X_valid_fold)[:, 1]\n\n    # Calculate the ROC AUC score for this fold\n    roc_auc = roc_auc_score(y_valid_fold, y_pred)\n    roc_auc_scores.append(roc_auc)\n\n    # Print the ROC AUC score for this fold\n    print(f'Fold {fold}: ROC AUC = {roc_auc}')\n\n# Calculate and print the mean ROC AUC score across all folds\nmean_roc_auc = sum(roc_auc_scores) / n_splits\nprint(f'Mean ROC AUC = {mean_roc_auc}')","metadata":{"execution":{"iopub.status.busy":"2023-11-04T09:14:54.221664Z","iopub.execute_input":"2023-11-04T09:14:54.222155Z","iopub.status.idle":"2023-11-04T09:36:40.368755Z","shell.execute_reply.started":"2023-11-04T09:14:54.222119Z","shell.execute_reply":"2023-11-04T09:36:40.367594Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"Fold 0: ROC AUC = 0.7555337260218458\nFold 1: ROC AUC = 0.7576412763894531\nFold 2: ROC AUC = 0.7597318906698497\nFold 3: ROC AUC = 0.7563830262633131\nFold 4: ROC AUC = 0.7569442960094304\nMean ROC AUC = 0.7572468430707783\n","output_type":"stream"}]},{"cell_type":"code","source":"joblib.dump(same_prod_model,'same_prod_model.pkl')","metadata":{"execution":{"iopub.status.busy":"2023-11-04T09:41:48.404561Z","iopub.execute_input":"2023-11-04T09:41:48.406202Z","iopub.status.idle":"2023-11-04T09:41:48.453686Z","shell.execute_reply.started":"2023-11-04T09:41:48.406142Z","shell.execute_reply":"2023-11-04T09:41:48.452452Z"},"trusted":true},"execution_count":79,"outputs":[{"execution_count":79,"output_type":"execute_result","data":{"text/plain":"['same_prod_model.pkl']"},"metadata":{}}]},{"cell_type":"code","source":"joblib.dump(encoder,'target_encoder.pkl')","metadata":{"execution":{"iopub.status.busy":"2023-11-04T10:12:26.234275Z","iopub.execute_input":"2023-11-04T10:12:26.234669Z","iopub.status.idle":"2023-11-04T10:12:26.274606Z","shell.execute_reply.started":"2023-11-04T10:12:26.234629Z","shell.execute_reply":"2023-11-04T10:12:26.273305Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"['target_encoder.pkl']"},"metadata":{}}]},{"cell_type":"code","source":"test_input.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T10:32:05.124166Z","iopub.execute_input":"2023-11-04T10:32:05.124649Z","iopub.status.idle":"2023-11-04T10:32:05.139280Z","shell.execute_reply.started":"2023-11-04T10:32:05.124614Z","shell.execute_reply":"2023-11-04T10:32:05.138120Z"},"trusted":true},"execution_count":136,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1 entries, 0 to 0\nData columns (total 9 columns):\n #   Column                  Non-Null Count  Dtype  \n---  ------                  --------------  -----  \n 0   add_to_cart_order       1 non-null      int64  \n 1   product_name            1 non-null      float64\n 2   user_id                 1 non-null      int64  \n 3   order_number            1 non-null      int64  \n 4   order_dow               1 non-null      int64  \n 5   order_hour_of_day       1 non-null      int64  \n 6   days_since_prior_order  1 non-null      float64\n 7   aisle                   1 non-null      float64\n 8   department              1 non-null      float64\ndtypes: float64(4), int64(5)\nmemory usage: 200.0 bytes\n","output_type":"stream"}]},{"cell_type":"code","source":"# cols1 = ['order_number', 'days_since_prior_order', 'order_dow', 'order_hour_of_day', 'aisle', 'department', 'product_name']\ntest_input = pd.DataFrame(data= filtered_df,index=range(0,1))\n\nxgb_model = joblib.load('/kaggle/working/xgb_model.pkl')\nsame_prod_model = joblib.load('/kaggle/working/same_prod_model.pkl')\n# reorder model\ndef predict(test_input):\n    test_input[['product_name','aisle','department']] = encoder.transform(test_input[['product_name','aisle','department']])\n    input1=test_input[['order_number', 'days_since_prior_order', 'order_dow', 'order_hour_of_day', 'aisle', 'department', 'product_name']]\n    reorder_preds = xgb_model.predict(input1).item()\n    \n    input2 = test_input[['user_id', 'product_name', 'order_number', 'days_since_prior_order', 'order_dow', 'order_hour_of_day']]\n    same_preds = np.max(same_prod_model.predict_proba(input2))\n    \n    return reorder_preds,same_preds\n\nreorder_predis, same_preds = predict(test_input)\nprint(reorder_predis, same_preds)","metadata":{"execution":{"iopub.status.busy":"2023-11-04T10:09:47.791419Z","iopub.execute_input":"2023-11-04T10:09:47.791911Z","iopub.status.idle":"2023-11-04T10:09:47.897367Z","shell.execute_reply.started":"2023-11-04T10:09:47.791875Z","shell.execute_reply":"2023-11-04T10:09:47.896409Z"},"trusted":true},"execution_count":129,"outputs":[{"name":"stdout","text":"1 0.9210505\n","output_type":"stream"}]},{"cell_type":"code","source":"test_input","metadata":{"execution":{"iopub.status.busy":"2023-11-04T11:01:37.497837Z","iopub.execute_input":"2023-11-04T11:01:37.499218Z","iopub.status.idle":"2023-11-04T11:01:37.514651Z","shell.execute_reply.started":"2023-11-04T11:01:37.499165Z","shell.execute_reply":"2023-11-04T11:01:37.513258Z"},"trusted":true},"execution_count":140,"outputs":[{"execution_count":140,"output_type":"execute_result","data":{"text/plain":"   add_to_cart_order  product_name  user_id  order_number  order_dow  \\\n0                  2      0.662688   199120            49          3   \n\n   order_hour_of_day  days_since_prior_order     aisle  department  \n0                 20                     7.0  0.687523    0.675945  ","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>add_to_cart_order</th>\n      <th>product_name</th>\n      <th>user_id</th>\n      <th>order_number</th>\n      <th>order_dow</th>\n      <th>order_hour_of_day</th>\n      <th>days_since_prior_order</th>\n      <th>aisle</th>\n      <th>department</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>0.662688</td>\n      <td>199120</td>\n      <td>49</td>\n      <td>3</td>\n      <td>20</td>\n      <td>7.0</td>\n      <td>0.687523</td>\n      <td>0.675945</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"filtered_df = df[(df['user_id'] == 199120) & (df['add_to_cart_order'] == 2)].to_dict(orient='records')[0]\n\n","metadata":{"execution":{"iopub.status.busy":"2023-11-04T09:52:06.215887Z","iopub.execute_input":"2023-11-04T09:52:06.216500Z","iopub.status.idle":"2023-11-04T09:52:06.233763Z","shell.execute_reply.started":"2023-11-04T09:52:06.216449Z","shell.execute_reply":"2023-11-04T09:52:06.231922Z"},"trusted":true},"execution_count":106,"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"{'add_to_cart_order': 2,\n 'reordered': 1,\n 'product_name': 'Bulgarian Yogurt',\n 'user_id': 199120,\n 'order_number': 49,\n 'order_dow': 3,\n 'order_hour_of_day': 20,\n 'days_since_prior_order': 7.0,\n 'aisle': 'yogurt',\n 'department': 'dairy eggs'}"},"metadata":{}}]},{"cell_type":"code","source":"del filtered_df['reordered']\n","metadata":{"execution":{"iopub.status.busy":"2023-11-04T09:53:51.070512Z","iopub.execute_input":"2023-11-04T09:53:51.070946Z","iopub.status.idle":"2023-11-04T09:53:51.076775Z","shell.execute_reply.started":"2023-11-04T09:53:51.070911Z","shell.execute_reply":"2023-11-04T09:53:51.075389Z"},"trusted":true},"execution_count":107,"outputs":[]},{"cell_type":"code","source":"print(joblib.__version__)\nprint(np.__version__)\nprint(pd.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-11-04T10:20:17.492312Z","iopub.execute_input":"2023-11-04T10:20:17.492727Z","iopub.status.idle":"2023-11-04T10:20:17.499225Z","shell.execute_reply.started":"2023-11-04T10:20:17.492696Z","shell.execute_reply":"2023-11-04T10:20:17.498033Z"},"trusted":true},"execution_count":132,"outputs":[{"name":"stdout","text":"1.3.2\n1.23.5\n2.0.3\n","output_type":"stream"}]},{"cell_type":"code","source":"filtered_df.info","metadata":{"execution":{"iopub.status.busy":"2023-11-04T10:31:48.300220Z","iopub.execute_input":"2023-11-04T10:31:48.300684Z","iopub.status.idle":"2023-11-04T10:31:48.336638Z","shell.execute_reply.started":"2023-11-04T10:31:48.300651Z","shell.execute_reply":"2023-11-04T10:31:48.335549Z"},"trusted":true},"execution_count":135,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","Cell \u001b[0;32mIn[135], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfiltered_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfo\u001b[49m\n","\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'info'"],"ename":"AttributeError","evalue":"'dict' object has no attribute 'info'","output_type":"error"}]},{"cell_type":"code","source":"import xgboost\nxgboost.__version__","metadata":{"execution":{"iopub.status.busy":"2023-11-04T10:54:40.378137Z","iopub.execute_input":"2023-11-04T10:54:40.378556Z","iopub.status.idle":"2023-11-04T10:54:40.386106Z","shell.execute_reply.started":"2023-11-04T10:54:40.378524Z","shell.execute_reply":"2023-11-04T10:54:40.384802Z"},"trusted":true},"execution_count":139,"outputs":[{"execution_count":139,"output_type":"execute_result","data":{"text/plain":"'1.7.6'"},"metadata":{}}]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T11:34:27.146346Z","iopub.execute_input":"2023-11-04T11:34:27.146761Z","iopub.status.idle":"2023-11-04T11:34:27.165013Z","shell.execute_reply.started":"2023-11-04T11:34:27.146717Z","shell.execute_reply":"2023-11-04T11:34:27.163827Z"},"trusted":true},"execution_count":144,"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":"   add_to_cart_order  reordered  \\\n0                  1          1   \n1                  2          1   \n2                  8          1   \n3                  3          0   \n4                  4          0   \n\n                                    product_name  user_id  order_number  \\\n0                               Bulgarian Yogurt   112108             4   \n1  Organic 4% Milk Fat Whole Milk Cottage Cheese   112108             4   \n2                    Organic Whole String Cheese   112108             4   \n3                          Organic Celery Hearts   112108             4   \n4                                 Cucumber Kirby   112108             4   \n\n   order_dow  order_hour_of_day  days_since_prior_order                 aisle  \\\n0          4                 10                     9.0                yogurt   \n1          4                 10                     9.0  other creams cheeses   \n2          4                 10                     9.0       packaged cheese   \n3          4                 10                     9.0      fresh vegetables   \n4          4                 10                     9.0      fresh vegetables   \n\n   department  order_number_user  days_since_prior_order_user  \n0  dairy eggs                4.0                          9.0  \n1  dairy eggs                4.0                          9.0  \n2  dairy eggs                4.0                          9.0  \n3     produce                4.0                          9.0  \n4     produce                4.0                          9.0  ","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>add_to_cart_order</th>\n      <th>reordered</th>\n      <th>product_name</th>\n      <th>user_id</th>\n      <th>order_number</th>\n      <th>order_dow</th>\n      <th>order_hour_of_day</th>\n      <th>days_since_prior_order</th>\n      <th>aisle</th>\n      <th>department</th>\n      <th>order_number_user</th>\n      <th>days_since_prior_order_user</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Bulgarian Yogurt</td>\n      <td>112108</td>\n      <td>4</td>\n      <td>4</td>\n      <td>10</td>\n      <td>9.0</td>\n      <td>yogurt</td>\n      <td>dairy eggs</td>\n      <td>4.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>Organic 4% Milk Fat Whole Milk Cottage Cheese</td>\n      <td>112108</td>\n      <td>4</td>\n      <td>4</td>\n      <td>10</td>\n      <td>9.0</td>\n      <td>other creams cheeses</td>\n      <td>dairy eggs</td>\n      <td>4.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>1</td>\n      <td>Organic Whole String Cheese</td>\n      <td>112108</td>\n      <td>4</td>\n      <td>4</td>\n      <td>10</td>\n      <td>9.0</td>\n      <td>packaged cheese</td>\n      <td>dairy eggs</td>\n      <td>4.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0</td>\n      <td>Organic Celery Hearts</td>\n      <td>112108</td>\n      <td>4</td>\n      <td>4</td>\n      <td>10</td>\n      <td>9.0</td>\n      <td>fresh vegetables</td>\n      <td>produce</td>\n      <td>4.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0</td>\n      <td>Cucumber Kirby</td>\n      <td>112108</td>\n      <td>4</td>\n      <td>4</td>\n      <td>10</td>\n      <td>9.0</td>\n      <td>fresh vegetables</td>\n      <td>produce</td>\n      <td>4.0</td>\n      <td>9.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import linear_kernel\n","metadata":{"execution":{"iopub.status.busy":"2023-11-04T11:33:11.252264Z","iopub.execute_input":"2023-11-04T11:33:11.252684Z","iopub.status.idle":"2023-11-04T11:33:11.258452Z","shell.execute_reply.started":"2023-11-04T11:33:11.252652Z","shell.execute_reply":"2023-11-04T11:33:11.257349Z"},"trusted":true},"execution_count":143,"outputs":[]},{"cell_type":"code","source":"# # Load your data (sample data provided)\n# data = pd.DataFrame({\n#     'user_id': [112108, 47901],\n#     'product_name': ['Bulgarian Yogurt', 'Bulgarian Yogurt']\n## Create a user-product matrix with user interactions (e.g., 1 for purchased, 0 for not)\n\ndata = df[['user_id','product_name']]\nuser_product_matrix = data.pivot_table(index='user_id', columns='product_name', aggfunc='size', fill_value=0)\n\n# # Calculate the cosine similarity between products\n# product_similarity = cosine_similarity(user_product_matrix.T)\n\n# # Recommend products for a given user based on product similarity\n# def recommend_products(user_id, user_product_matrix, product_similarity, num_recommendations=5):\n#     user_products = user_product_matrix.loc[user_id]\n#     recommended_products = []\n\n#     for product_name in user_products.index:\n#         if user_products[product_name] == 1:\n#             # Find similar products\n#             similar_products = product_similarity[data['product_name'] == product_name]\n#             similar_products = list(enumerate(similar_products[0]))\n\n#             # Sort products by similarity in descending order\n#             similar_products = sorted(similar_products, key=lambda x: x[1], reverse=True)[1:]\n\n#             # Extract product names from the indices\n#             recommended_products.extend([data['product_name'][i[0]] for i in similar_products[:num_recommendations]])\n\n#     return recommended_products[:num_recommendations]\n\n# # Example usage\n# user_id_to_recommend = 112108\n# recommendations = recommend_products(user_id_to_recommend, user_product_matrix, product_similarity)\n\n\n\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-11-04T11:41:45.432173Z","iopub.execute_input":"2023-11-04T11:41:45.432620Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_2581/1478684624.py:8: PerformanceWarning: The following operation may generate 5133289707 cells in the resulting pandas object.\n  user_product_matrix = data.pivot_table(index='user_id', columns='product_name', aggfunc='size', fill_value=0)\n","output_type":"stream"}]},{"cell_type":"code","source":"def recommend_products_for_user(user_id, num_recommendations=5):\n    user_data = df[df['user_id'] == user_id]\n    user_products = user_data['product_name'].unique()\n\n    # Calculate product scores based on user behavior or other criteria\n    # In this simplified example, we recommend the most popular products\n    product_scores = df['product_name'].value_counts().reset_index()\n    product_scores.columns = ['product_name', 'popularity']\n\n    # Filter out products the user has already purchased\n    product_scores = product_scores[~product_scores['product_name'].isin(user_products)]\n\n    # Sort products by popularity and recommend the top N products\n    recommended_products = product_scores.nlargest(num_recommendations, 'popularity')['product_name']\n\n    return recommended_products.tolist()\n\n# Usage example:\nuser_id_to_recommend = 199120\nrecommended_products = recommend_products_for_user(user_id_to_recommend)\nprint(f\"Recommended products for user {user_id_to_recommend}: {recommended_products}\")\n","metadata":{"execution":{"iopub.status.busy":"2023-11-04T11:46:52.003150Z","iopub.execute_input":"2023-11-04T11:46:52.003697Z","iopub.status.idle":"2023-11-04T11:46:52.244552Z","shell.execute_reply.started":"2023-11-04T11:46:52.003655Z","shell.execute_reply":"2023-11-04T11:46:52.243116Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Recommended products for user 199120: ['Banana', 'Organic Strawberries', 'Organic Baby Spinach', 'Large Lemon', 'Organic Avocado']\n","output_type":"stream"}]},{"cell_type":"code","source":"df[['user_id','product_name']].to_csv('recommend_products.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2023-11-04T11:50:27.695802Z","iopub.execute_input":"2023-11-04T11:50:27.696268Z","iopub.status.idle":"2023-11-04T11:50:32.534873Z","shell.execute_reply.started":"2023-11-04T11:50:27.696220Z","shell.execute_reply":"2023-11-04T11:50:32.533813Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}