{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# EDA, let's find interesting thing in timeseries :)\n\nThank you for your checking this notebook.\n\nThis is my EDA notebook for \"H&M Personalized Fashion Recommendations\" competition [Link](https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/overview).\n\n![image.png](attachment:04714516-48e4-4a70-936e-00b36880a953.png)\n\nThe following 4 .csv files and one folder of images are provided in this competition. \n\nOur task is to predict <u>what articles each customer will purchase in the 7-day period immediately after the training data ends</u>. \n\n![image.png](attachment:b69d8c06-b6cd-4530-bd27-0632ce2e6312.png)\n\nIn this notebook, I explore these provided datas and look for interesting informaiton based on time series.\n\nIt's still not clear for me how to make a good recommendation based on these data but I believe this EDA would bring me some ideas about next step. \n\nIf you think this notebook is interesting, please leave your comment or question and I appreciate your upvote as well. :) \n\n<a id='top'></a>\n## Contents\n1. [Import Library & Set Config](#config)\n2. [Load Data](#load)\n3. [Look into \"transactions_train.csv\" based on time series](#transaction)\n4. [Append key features from \"articles.csv\" to \"transactions_train.csv\"](#article)\n5. [Append key features from \"customers.csv\" to \"transactions_train.csv\"](#customer)\n6. [Conclution](#conclution)\n7. [Reference](#ref)","metadata":{},"attachments":{"04714516-48e4-4a70-936e-00b36880a953.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"<a id='config'></a>\n\n---\n## 1. Import Library & Set Config\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"# === General ===\nimport sys, warnings, time, os, copy, gc\nwarnings.filterwarnings('ignore')\nfrom IPython.display import display\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\npd.set_option('display.max_rows', 50)\npd.set_option('display.max_columns', None)\npd.set_option(\"display.max_colwidth\", 10000)\nimport seaborn as sns\nsns.set()\nfrom pandas.io.json import json_normalize\nimport random\nimport pprint\nfrom pathlib import Path","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:18.729047Z","iopub.execute_input":"2022-03-05T10:57:18.729512Z","iopub.status.idle":"2022-03-05T10:57:19.881693Z","shell.execute_reply.started":"2022-03-05T10:57:18.729379Z","shell.execute_reply":"2022-03-05T10:57:19.880461Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"DEBUG = False\nPATH_INPUT = r'../input/h-and-m-personalized-fashion-recommendations/'","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:19.883557Z","iopub.execute_input":"2022-03-05T10:57:19.883824Z","iopub.status.idle":"2022-03-05T10:57:19.887668Z","shell.execute_reply.started":"2022-03-05T10:57:19.883786Z","shell.execute_reply":"2022-03-05T10:57:19.886783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='load'></a>\n\n---\n## 2. Load Data\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"def unique_values(data):\n    total = data.count()\n    tt = pd.DataFrame(total)\n    tt.columns = ['Total']\n    uniques = []\n    for col in data.columns:\n        unique = data[col].nunique()\n        uniques.append(unique)\n    tt['Uniques'] = uniques\n    return tt","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:20.094145Z","iopub.execute_input":"2022-03-05T10:57:20.094471Z","iopub.status.idle":"2022-03-05T10:57:20.100214Z","shell.execute_reply.started":"2022-03-05T10:57:20.094424Z","shell.execute_reply":"2022-03-05T10:57:20.099213Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfArticles = pd.read_csv(PATH_INPUT + 'articles.csv')\nprint(f'The shape of articles.csv is {dfArticles.shape}.\\n')\ndfArticles.head(3)","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:20.374601Z","iopub.execute_input":"2022-03-05T10:57:20.374911Z","iopub.status.idle":"2022-03-05T10:57:21.579095Z","shell.execute_reply.started":"2022-03-05T10:57:20.374873Z","shell.execute_reply":"2022-03-05T10:57:21.578462Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'The number of unique value of each columns.\\n')\nunique_values(dfArticles)","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:21.580654Z","iopub.execute_input":"2022-03-05T10:57:21.581049Z","iopub.status.idle":"2022-03-05T10:57:21.804981Z","shell.execute_reply.started":"2022-03-05T10:57:21.581017Z","shell.execute_reply":"2022-03-05T10:57:21.803999Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfCustomers = pd.read_csv(PATH_INPUT + 'customers.csv')\nprint(f'The shape of customers.csv is {dfCustomers.shape}.\\n')\ndfCustomers.head(3)","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:21.806241Z","iopub.execute_input":"2022-03-05T10:57:21.806474Z","iopub.status.idle":"2022-03-05T10:57:27.123888Z","shell.execute_reply.started":"2022-03-05T10:57:21.806444Z","shell.execute_reply":"2022-03-05T10:57:27.122997Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'memory usage of dfCustomers : {dfCustomers.memory_usage(index=True).sum() / 1024 ** 2} KB \\n')\ndfCustomers['age'] = dfCustomers['age'].fillna(99)\ndfCustomers['age'] = dfCustomers['age'].astype('int8')\nprint(f'memory usage of dfCustomers after change dtype: {dfCustomers.memory_usage(index=True).sum() / 1024 ** 2} KB \\n')","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:27.127362Z","iopub.execute_input":"2022-03-05T10:57:27.127628Z","iopub.status.idle":"2022-03-05T10:57:27.165368Z","shell.execute_reply.started":"2022-03-05T10:57:27.127595Z","shell.execute_reply":"2022-03-05T10:57:27.164330Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'The number of unique value of each columns.\\n')\nunique_values(dfCustomers)","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:27.166873Z","iopub.execute_input":"2022-03-05T10:57:27.167282Z","iopub.status.idle":"2022-03-05T10:57:28.968724Z","shell.execute_reply.started":"2022-03-05T10:57:27.167234Z","shell.execute_reply":"2022-03-05T10:57:28.967596Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfSampleSub = pd.read_csv(PATH_INPUT + 'sample_submission.csv')\nprint(f'The shape of sample_submission.csv is {dfSampleSub.shape}.\\n')\ndfSampleSub.head(3)","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:28.970311Z","iopub.execute_input":"2022-03-05T10:57:28.970605Z","iopub.status.idle":"2022-03-05T10:57:33.615228Z","shell.execute_reply.started":"2022-03-05T10:57:28.970564Z","shell.execute_reply":"2022-03-05T10:57:33.614339Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfTransactions = pd.read_csv(PATH_INPUT + 'transactions_train.csv', index_col=0, parse_dates=True)\nprint(f'The shape of transactions_train.csv is {dfTransactions.shape}.\\n')\ndfTransactions.head(3)","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:57:33.616609Z","iopub.execute_input":"2022-03-05T10:57:33.617381Z","iopub.status.idle":"2022-03-05T10:58:36.013471Z","shell.execute_reply.started":"2022-03-05T10:57:33.617340Z","shell.execute_reply":"2022-03-05T10:58:36.012594Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- \"Price\" is not real value. It's scaled by competion organizer to protect privacy.\n- \"sales_channel_id\" has 1 / 2 values and it means online / offline.\n- [Data Questions - Missing Transactions](https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/306016#1680549)","metadata":{}},{"cell_type":"code","source":"print(f'memory usage of dfTransactions : {dfTransactions.memory_usage(index=True).sum() / 1024 ** 2} KB \\n')\ndfTransactions['price'] = dfTransactions['price'].astype('float32')\nprint(f'memory usage of dfTransactions after change dtype: {dfTransactions.memory_usage(index=True).sum() / 1024 ** 2} KB \\n')","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:58:36.015279Z","iopub.execute_input":"2022-03-05T10:58:36.015639Z","iopub.status.idle":"2022-03-05T10:58:36.092720Z","shell.execute_reply.started":"2022-03-05T10:58:36.015594Z","shell.execute_reply":"2022-03-05T10:58:36.090931Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='transaction'></a>\n\n---\n## 3. Look into \"transactions_train.csv\" based on time series\n- \"transactions_train.csv\" includes the history of purchasing by customers.\n- The target of this competition is to predict purchasing results within 7 days after the period which is provided in this file.\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"oldestDay = dfTransactions.index.min()\nlatestDay = dfTransactions.index.max()\ntargetDay = latestDay + pd.DateOffset(days=7)\nprint(f'The period of train data is from {oldestDay} until {latestDay}.\\n')\nprint(f'So we need to predict purchasing from {latestDay} until {targetDay}.\\n')","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:58:36.094261Z","iopub.execute_input":"2022-03-05T10:58:36.094615Z","iopub.status.idle":"2022-03-05T10:58:36.171939Z","shell.execute_reply.started":"2022-03-05T10:58:36.094568Z","shell.execute_reply":"2022-03-05T10:58:36.170973Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfTransactions['article_id'].resample('M').count().plot(figsize=(15, 10), kind='bar')","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:58:36.174850Z","iopub.execute_input":"2022-03-05T10:58:36.175093Z","iopub.status.idle":"2022-03-05T10:58:37.562043Z","shell.execute_reply.started":"2022-03-05T10:58:36.175064Z","shell.execute_reply":"2022-03-05T10:58:37.561148Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfTransactions['article_id'].resample('W').count().plot(figsize=(20, 10), kind='bar')","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:58:37.563479Z","iopub.execute_input":"2022-03-05T10:58:37.563767Z","iopub.status.idle":"2022-03-05T10:58:42.573665Z","shell.execute_reply.started":"2022-03-05T10:58:37.563733Z","shell.execute_reply":"2022-03-05T10:58:42.572747Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- The peak of sold Qty. was tend to appear arround June.\n- The target month, September, did not have so high qty. even consider the lack of data.","metadata":{}},{"cell_type":"code","source":"dfUniqCust = dfTransactions.reset_index(drop=False)\ndfUniqCust = dfUniqCust[['t_dat', 'customer_id']].drop_duplicates()\ndfUniqCust = dfUniqCust.set_index('t_dat')\ndfUniqCust['customer_id'].resample('M').count().plot(figsize=(15, 10), kind='bar')","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:58:42.575020Z","iopub.execute_input":"2022-03-05T10:58:42.575254Z","iopub.status.idle":"2022-03-05T10:58:58.498320Z","shell.execute_reply.started":"2022-03-05T10:58:42.575224Z","shell.execute_reply":"2022-03-05T10:58:58.497468Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- The trend of number of unique customers are similar to sold Qty of items.\n- The peak of number of unique customers is less than 500,000. It is less than 50% of total customers, 1,371,980. So prediciton might be necesary for only 50 % of total customers as well.\n- It might be better to classify who would buy during target period before detail prediction because of reducing computing time.","metadata":{}},{"cell_type":"code","source":"plt.clf()\nplt.close()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:58:58.499974Z","iopub.execute_input":"2022-03-05T10:58:58.500520Z","iopub.status.idle":"2022-03-05T10:58:58.506471Z","shell.execute_reply.started":"2022-03-05T10:58:58.500458Z","shell.execute_reply":"2022-03-05T10:58:58.505672Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del dfUniqCust, dfSampleSub, oldestDay, latestDay, targetDay \ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:58:58.507896Z","iopub.execute_input":"2022-03-05T10:58:58.508172Z","iopub.status.idle":"2022-03-05T10:58:58.772940Z","shell.execute_reply.started":"2022-03-05T10:58:58.508133Z","shell.execute_reply":"2022-03-05T10:58:58.772007Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfTransactions.drop(labels=['price', 'sales_channel_id'], inplace=True, axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:58:58.774132Z","iopub.execute_input":"2022-03-05T10:58:58.774911Z","iopub.status.idle":"2022-03-05T10:58:59.326332Z","shell.execute_reply.started":"2022-03-05T10:58:58.774873Z","shell.execute_reply":"2022-03-05T10:58:59.325556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='article'></a>\n\n---\n## 4. Append key features from \"articles.csv\" to \"transactions_train.csv\"\n- Append features \"product_group_name\", \"perceived_colour_master_name\" & \"index_name\" from \"articles.csv\" to \"transactions_train.csv\".\n- Check seasonality or any other interesting trend of articles.\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"dfTransArt = dfTransactions.reset_index(drop=False).merge(dfArticles[['article_id', \"product_group_name\", \"perceived_colour_master_name\", \"index_name\"]], on='article_id', how='inner')\ndfTransArt = dfTransArt.set_index('t_dat')\ndfTransArt.drop(labels=['customer_id'], inplace=True, axis=1)\ndfTransArt","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:58:59.327722Z","iopub.execute_input":"2022-03-05T10:58:59.328048Z","iopub.status.idle":"2022-03-05T10:59:36.234269Z","shell.execute_reply.started":"2022-03-05T10:58:59.328004Z","shell.execute_reply":"2022-03-05T10:59:36.233285Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del dfArticles\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:59:36.235634Z","iopub.execute_input":"2022-03-05T10:59:36.235886Z","iopub.status.idle":"2022-03-05T10:59:36.375835Z","shell.execute_reply.started":"2022-03-05T10:59:36.235851Z","shell.execute_reply":"2022-03-05T10:59:36.374884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"target_period = [['2018-10-01', '2019-09-30'], ['2019-10-01', '2020-09-21']]\ntarget_column = 'product_group_name'\n\nfor i in target_period:\n    print(f'The sold Qty from {i[0]} until {i[1]} by {target_column}. \\n')\n    \n    x = dfTransArt.loc[i[0]:i[1]]\n    x = pd.get_dummies(x[target_column], columns=target_column)\n    x = x.astype('int32')\n    x.resample('M').sum().plot.bar(figsize=(15, 10), stacked=True)\n    plt.legend(bbox_to_anchor=(1.0, 1.0))\n    plt.show()\n    plt.clf()\n    plt.close()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T10:59:36.377487Z","iopub.execute_input":"2022-03-05T10:59:36.378585Z","iopub.status.idle":"2022-03-05T11:00:05.914978Z","shell.execute_reply.started":"2022-03-05T10:59:36.378534Z","shell.execute_reply":"2022-03-05T11:00:05.914016Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- The reason of peak on June is looks like due to Swimwear and Garment Full body.\n- On September, Swimwear was not sold so much but Garment Full body still had ceratin share.","metadata":{}},{"cell_type":"code","source":"target_column = 'perceived_colour_master_name'\n\nfor i in target_period:\n    print(f'The sold Qty from {i[0]} until {i[1]} by {target_column}. \\n')\n    \n    x = dfTransArt.loc[i[0]:i[1]]\n    x = pd.get_dummies(x[target_column], columns=target_column)\n    x = x.astype('int32')\n    x.resample('M').sum().plot.bar(figsize=(15, 10), stacked=True)\n    plt.legend(bbox_to_anchor=(1.0, 1.0))\n    plt.show()\n    plt.clf()\n    plt.close()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T11:00:05.916301Z","iopub.execute_input":"2022-03-05T11:00:05.916654Z","iopub.status.idle":"2022-03-05T11:00:38.049598Z","shell.execute_reply.started":"2022-03-05T11:00:05.916608Z","shell.execute_reply":"2022-03-05T11:00:38.048558Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Black, white & blue have most of the share.","metadata":{}},{"cell_type":"code","source":"target_column = 'index_name'\n\nfor i in target_period:\n    print(f'The sold Qty from {i[0]} until {i[1]} by {target_column}. \\n')\n    \n    x = dfTransArt.loc[i[0]:i[1]]\n    x = pd.get_dummies(x[target_column], columns=target_column)\n    x = x.astype('int32')\n    x.resample('M').sum().plot.bar(figsize=(15, 10), stacked=True)\n    plt.legend(bbox_to_anchor=(1.0, 1.0))\n    plt.show()\n    plt.clf()\n    plt.close()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T11:00:38.051051Z","iopub.execute_input":"2022-03-05T11:00:38.051294Z","iopub.status.idle":"2022-03-05T11:01:03.272285Z","shell.execute_reply.started":"2022-03-05T11:00:38.051266Z","shell.execute_reply":"2022-03-05T11:01:03.271315Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- \"Ladieswear\" has the most of share.\n- What's the \"Divided\"? I am asking in Discussion [Link](https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/310081)","metadata":{}},{"cell_type":"code","source":"# Check the latest popular articles.\n# Create new feature from 'product_group_name' & 'perceived_colour_maseter_name'\n\nx = dfTransArt.loc['2020-09-01':'2020-09-30']\nx['pg_colour'] = x['product_group_name'] + '_' + x['perceived_colour_master_name']\nx = pd.get_dummies(x['pg_colour'], columns='pg_colour')\nx = x.astype('int32')\nx = x.resample('M').sum().T\nx = x.sort_values('2020-09-30', ascending=False)\nx.head(40).plot.bar(figsize=(15, 10), stacked=True)\nplt.legend(bbox_to_anchor=(1.0, 1.0))\nplt.show()\nplt.clf()\nplt.close()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T11:01:03.273746Z","iopub.execute_input":"2022-03-05T11:01:03.273975Z","iopub.status.idle":"2022-03-05T11:01:09.322419Z","shell.execute_reply.started":"2022-03-05T11:01:03.273947Z","shell.execute_reply":"2022-03-05T11:01:09.321607Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"listPopularArt = x.head(20).index.values.tolist()\nprint(listPopularArt)","metadata":{"execution":{"iopub.status.busy":"2022-03-05T11:01:09.323581Z","iopub.execute_input":"2022-03-05T11:01:09.324247Z","iopub.status.idle":"2022-03-05T11:01:09.329163Z","shell.execute_reply.started":"2022-03-05T11:01:09.324203Z","shell.execute_reply":"2022-03-05T11:01:09.328567Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x = dfTransArt.loc['2020-09-01':'2020-09-30']\nx['pg_colour'] = x['product_group_name'] + '_' + x['perceived_colour_master_name']\ny = x[x['pg_colour'].isin(listPopularArt)]['article_id'].nunique()\nprint(f'The number of popular articles in top 20 product group & colour is {y}. \\n')","metadata":{"execution":{"iopub.status.busy":"2022-03-05T11:01:09.330411Z","iopub.execute_input":"2022-03-05T11:01:09.330719Z","iopub.status.idle":"2022-03-05T11:01:10.145571Z","shell.execute_reply.started":"2022-03-05T11:01:09.330688Z","shell.execute_reply":"2022-03-05T11:01:10.144565Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Created new feature from product group and colour master.\n- There were 15, 871 articles in top 20 popular group and colour on Sep in 2020.","metadata":{}},{"cell_type":"code","source":"target_period = [['2018-10-01', '2019-09-30'], ['2019-10-01', '2020-09-21']]\ntarget_column = 'pg_colour'\nlistColumn = []\n\nfor i in target_period:    \n    x = dfTransArt.loc[i[0]:i[1]]\n    x['pg_colour'] = x['product_group_name'] + '_' + x['perceived_colour_master_name']\n    x = pd.get_dummies(x[target_column], columns=target_column)\n    listColumn.append(x.columns.values.tolist())","metadata":{"execution":{"iopub.status.busy":"2022-03-05T11:01:10.147342Z","iopub.execute_input":"2022-03-05T11:01:10.147681Z","iopub.status.idle":"2022-03-05T11:01:59.438122Z","shell.execute_reply.started":"2022-03-05T11:01:10.147638Z","shell.execute_reply":"2022-03-05T11:01:59.436955Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"listFeatures = x.sum().sort_values(ascending=False).head(100).index.values.tolist()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T11:01:59.439784Z","iopub.execute_input":"2022-03-05T11:01:59.440092Z","iopub.status.idle":"2022-03-05T11:02:02.855126Z","shell.execute_reply.started":"2022-03-05T11:01:59.440056Z","shell.execute_reply":"2022-03-05T11:02:02.854345Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"listX = set(listFeatures) & set(listColumn[0]) \nprint(len(listX))","metadata":{"execution":{"iopub.status.busy":"2022-03-05T11:02:02.856766Z","iopub.execute_input":"2022-03-05T11:02:02.857102Z","iopub.status.idle":"2022-03-05T11:02:02.863088Z","shell.execute_reply.started":"2022-03-05T11:02:02.857058Z","shell.execute_reply":"2022-03-05T11:02:02.862203Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(listFeatures)","metadata":{"execution":{"iopub.status.busy":"2022-03-05T11:02:02.864236Z","iopub.execute_input":"2022-03-05T11:02:02.864466Z","iopub.status.idle":"2022-03-05T11:02:02.875457Z","shell.execute_reply.started":"2022-03-05T11:02:02.864436Z","shell.execute_reply":"2022-03-05T11:02:02.874789Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- 100 product grounp & colour conbination would be used as features for training model.","metadata":{}},{"cell_type":"code","source":"del dfTransArt\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T08:16:37.170382Z","iopub.status.idle":"2022-03-05T08:16:37.170917Z","shell.execute_reply.started":"2022-03-05T08:16:37.170734Z","shell.execute_reply":"2022-03-05T08:16:37.170755Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='customer'></a>\n\n---\n## 5. Append key features from \"customers.csv\" to \"transactions_train.csv\"\n- Append features 'club_member_status', 'fashion_news_frequency' & 'age' from \"customers.csv\" to \"transactions_train.csv\".\n- Check seasonality or any other interesting trend of customers.\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"dfTransCust = dfTransactions.reset_index(drop=False).merge(dfCustomers[['customer_id', 'club_member_status', 'fashion_news_frequency', 'age']], on='customer_id', how='inner')\ndfTransCust = dfTransCust.set_index('t_dat')\ndfTransCust.drop(labels=['article_id'], inplace=True, axis=1)\ndfTransCust","metadata":{"execution":{"iopub.status.busy":"2022-03-05T08:16:37.172088Z","iopub.status.idle":"2022-03-05T08:16:37.172650Z","shell.execute_reply.started":"2022-03-05T08:16:37.172376Z","shell.execute_reply":"2022-03-05T08:16:37.172408Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del dfCustomers\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T08:16:37.173960Z","iopub.status.idle":"2022-03-05T08:16:37.174279Z","shell.execute_reply.started":"2022-03-05T08:16:37.174110Z","shell.execute_reply":"2022-03-05T08:16:37.174131Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"target_column = 'club_member_status'\n\nfor i in target_period:\n    print(f'The sold Qty from {i[0]} until {i[1]} by {target_column}. \\n')\n    \n    x = dfTransCust.loc[i[0]:i[1]]\n    x = pd.get_dummies(x[target_column], columns=target_column)\n    x = x.astype('int32')\n    x.resample('M').sum().plot.bar(figsize=(15, 10), stacked=True)\n    plt.legend(bbox_to_anchor=(1.0, 1.0))\n    plt.show()\n    plt.clf()\n    plt.close()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T08:16:37.175446Z","iopub.status.idle":"2022-03-05T08:16:37.175827Z","shell.execute_reply.started":"2022-03-05T08:16:37.175610Z","shell.execute_reply":"2022-03-05T08:16:37.175634Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Almost all customers are active.","metadata":{}},{"cell_type":"code","source":"target_column = 'fashion_news_frequency'\n\nfor i in target_period:\n    print(f'The sold Qty from {i[0]} until {i[1]} by {target_column}. \\n')\n    \n    x = dfTransCust.loc[i[0]:i[1]]\n    x = pd.get_dummies(x[target_column], columns=target_column)\n    x = x.astype('int32')\n    x.resample('M').sum().plot.bar(figsize=(15, 10), stacked=True)\n    plt.legend(bbox_to_anchor=(1.0, 1.0))\n    plt.show()\n    plt.clf()\n    plt.close()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T08:16:37.177181Z","iopub.status.idle":"2022-03-05T08:16:37.177503Z","shell.execute_reply.started":"2022-03-05T08:16:37.177330Z","shell.execute_reply":"2022-03-05T08:16:37.177353Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- \"Regulaly\" and \"None\" are 50:50.\n- Not so much fractuation of that share.","metadata":{}},{"cell_type":"code","source":"target_column = 'age'\n\nfor i in target_period:\n    print(f'The sold Qty from {i[0]} until {i[1]} by {target_column}. \\n')\n    \n    x = dfTransCust.loc[i[0]:i[1]]\n    x['age_bins'] = pd.cut(x[target_column], [-1, 19, 29, 39, 49, 59, 69, 119])\n    x = pd.get_dummies(x['age_bins'], columns='age_bins')\n    x = x.astype('int32')\n    x.resample('M').sum().plot.bar(figsize=(15, 10), stacked=True)\n    plt.legend(bbox_to_anchor=(1.0, 1.0))\n    plt.show()\n    plt.clf()\n    plt.close()","metadata":{"execution":{"iopub.status.busy":"2022-03-05T08:16:37.179237Z","iopub.status.idle":"2022-03-05T08:16:37.179527Z","shell.execute_reply.started":"2022-03-05T08:16:37.179373Z","shell.execute_reply":"2022-03-05T08:16:37.179389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- 20 ~ 29 years old have the biggest share.\n- Each 30 ~ 39, 40 ~ 49, 50 ~ 59 have almost same share.","metadata":{}},{"cell_type":"markdown","source":"<a id='conclution'></a>\n\n---\n\n## 6. Conclution\n\n- The period of train data is from 2018.09.20 to 2020.09.21.\n- So target period is from 2020.09.22 to 2020.09.29.\n- The peak of sold Qty was tend to on June.\n- The sold Qty. on September was not so high.\n- Prediciton might be necesary for only 50 % of total customers.\n- The reason of peak on June was \"Swimware\" & \"Garment Full body\".\n- \"Garment Full body\" still had ceratin share on September. There might be colleaction in Summer & Winter.\n- The features of customer still does not show valiable informaiton but \"age\" info could be used.\n\nThank you for your reading through this Notebook!\n\nBased on this EDA, I am thinking to create a model based on features of articles first. Let's see!\n\nIf you think this notebook is interesting for you, please do click upvote :)\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"markdown","source":"<a id='ref'></a>\n\n---\n## 7. Reference\n\n- [H&M EDA FIRST LOOK](https://www.kaggle.com/vanguarde/h-m-eda-first-look) by DANIIL KARPOV\n- [H&M EDA and Prediction](https://www.kaggle.com/gpreda/h-m-eda-and-prediction) by GABRIEL PREDA\n- [Pandas.DataFrameのメモリサイズを削減する（最大で8分の1） [Python]](https://qiita.com/nannoki/items/2a8934de31ad2258439d) by @nannoki\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}