{"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":"![image.png](attachment:c43a50d0-e62f-42ba-a4fb-7a4f49916ea7.png)\n\n\n**H&M Group is a family of brands and businesses with 53 online markets and approximately 4,850 stores. Our online store offers shoppers an extensive selection of products to browse through. But with too many choices, customers might not quickly find what interests them or what they are looking for, and ultimately, they might not make a purchase. To enhance the shopping experience, product recommendations are key. More importantly, helping customers make the right choices also has a positive implications for sustainability, as it reduces returns, and thereby minimizes emissions from transportation.**","metadata":{},"attachments":{"c43a50d0-e62f-42ba-a4fb-7a4f49916ea7.png":{"image/png":"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"}}},{"cell_type":"code","source":"import pandas as pd\nimport pandas as pd\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nimport datetime as dt","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-02-12T10:43:44.601120Z","iopub.execute_input":"2022-02-12T10:43:44.601464Z","iopub.status.idle":"2022-02-12T10:43:45.851995Z","shell.execute_reply.started":"2022-02-12T10:43:44.601373Z","shell.execute_reply":"2022-02-12T10:43:45.850830Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data Loading","metadata":{}},{"cell_type":"code","source":"articles = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/articles.csv')\ncustomers = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')\ntransactions = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:43:45.853830Z","iopub.execute_input":"2022-02-12T10:43:45.854074Z","iopub.status.idle":"2022-02-12T10:45:05.713513Z","shell.execute_reply.started":"2022-02-12T10:43:45.854047Z","shell.execute_reply":"2022-02-12T10:45:05.712063Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Articles:","metadata":{}},{"cell_type":"code","source":"articles.head(5)","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:05.716467Z","iopub.execute_input":"2022-02-12T10:45:05.716803Z","iopub.status.idle":"2022-02-12T10:45:05.780799Z","shell.execute_reply.started":"2022-02-12T10:45:05.716763Z","shell.execute_reply":"2022-02-12T10:45:05.779486Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":" **ladieswear covers a major part, while sportswear are the least!**","metadata":{}},{"cell_type":"code","source":"f, ax = plt.subplots(figsize = (12,7))\nax = sns.histplot( data= articles, y = 'index_name', color = 'pink', edgecolor = 'white')\nax.set_xlabel('Count by index name')\nax.set_ylabel('index_name')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:05.784306Z","iopub.execute_input":"2022-02-12T10:45:05.785227Z","iopub.status.idle":"2022-02-12T10:45:06.419350Z","shell.execute_reply.started":"2022-02-12T10:45:05.785183Z","shell.execute_reply":"2022-02-12T10:45:06.417976Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Fancy Jersey is most frequent garment, along with accessories and other items!**","metadata":{}},{"cell_type":"code","source":"f, ax = plt.subplots(figsize = (12,7))\nax = sns.histplot(data = articles, y = 'garment_group_name', color ='orange', multiple= 'stack', edgecolor = 'white')\nax.set_xlabel('Count by garment group')\nax.set_ylabel('garment group')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:06.421006Z","iopub.execute_input":"2022-02-12T10:45:06.421910Z","iopub.status.idle":"2022-02-12T10:45:06.976951Z","shell.execute_reply.started":"2022-02-12T10:45:06.421858Z","shell.execute_reply":"2022-02-12T10:45:06.975802Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Count of articles that are present based on the Index_group:**","metadata":{}},{"cell_type":"code","source":"articles.groupby(['index_group_name', 'index_name']).count()['article_id']","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:06.978609Z","iopub.execute_input":"2022-02-12T10:45:06.978896Z","iopub.status.idle":"2022-02-12T10:45:07.221620Z","shell.execute_reply.started":"2022-02-12T10:45:06.978862Z","shell.execute_reply":"2022-02-12T10:45:07.220284Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Top 20 Products Available**","metadata":{}},{"cell_type":"code","source":"articles['product_type_name'].value_counts()[:20]","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:07.223369Z","iopub.execute_input":"2022-02-12T10:45:07.223830Z","iopub.status.idle":"2022-02-12T10:45:07.253110Z","shell.execute_reply.started":"2022-02-12T10:45:07.223778Z","shell.execute_reply":"2022-02-12T10:45:07.251535Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nax = articles['product_type_name'].value_counts()[:20].plot( kind = 'bar', color = 'pink', figsize = (12,7))\nax.set_ylabel('Product Available')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:07.254679Z","iopub.execute_input":"2022-02-12T10:45:07.255036Z","iopub.status.idle":"2022-02-12T10:45:07.685051Z","shell.execute_reply.started":"2022-02-12T10:45:07.254989Z","shell.execute_reply":"2022-02-12T10:45:07.684117Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Top 10 Products**","metadata":{}},{"cell_type":"code","source":"articles['prod_name'].value_counts()[:10]","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:07.686713Z","iopub.execute_input":"2022-02-12T10:45:07.687245Z","iopub.status.idle":"2022-02-12T10:45:07.745901Z","shell.execute_reply.started":"2022-02-12T10:45:07.687195Z","shell.execute_reply":"2022-02-12T10:45:07.745181Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ax = articles['prod_name'].value_counts()[:10].plot(kind = 'bar', color = 'pink', figsize =(12,7))\nax.set_title('Top 10 Products')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:07.749005Z","iopub.execute_input":"2022-02-12T10:45:07.749953Z","iopub.status.idle":"2022-02-12T10:45:08.072884Z","shell.execute_reply.started":"2022-02-12T10:45:07.749900Z","shell.execute_reply":"2022-02-12T10:45:08.071456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Customers","metadata":{}},{"cell_type":"code","source":"customers.head()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:08.075195Z","iopub.execute_input":"2022-02-12T10:45:08.075436Z","iopub.status.idle":"2022-02-12T10:45:08.095370Z","shell.execute_reply.started":"2022-02-12T10:45:08.075407Z","shell.execute_reply":"2022-02-12T10:45:08.094150Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**The customers that are in most numbers are in between the age of 20-35**","metadata":{}},{"cell_type":"code","source":"f, ax = plt.subplots(figsize = (12,7))\nax = sns.distplot(customers['age'].dropna(), color = 'orange')\nax.set_title('Distribution of Age')","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:08.096863Z","iopub.execute_input":"2022-02-12T10:45:08.098275Z","iopub.status.idle":"2022-02-12T10:45:13.976471Z","shell.execute_reply.started":"2022-02-12T10:45:08.098228Z","shell.execute_reply":"2022-02-12T10:45:13.975306Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Most Cutomers have an Active Membership status**","metadata":{}},{"cell_type":"code","source":"customers['club_member_status'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:13.977803Z","iopub.execute_input":"2022-02-12T10:45:13.978067Z","iopub.status.idle":"2022-02-12T10:45:14.211001Z","shell.execute_reply.started":"2022-02-12T10:45:13.978037Z","shell.execute_reply":"2022-02-12T10:45:14.209588Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"f, ax = plt.subplots(figsize =(12,7))\nax = sns.histplot( data =customers, x = 'club_member_status', edgecolor = 'white', hue= 'club_member_status')\nax.set_xlabel('Membership Status')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:14.213093Z","iopub.execute_input":"2022-02-12T10:45:14.213388Z","iopub.status.idle":"2022-02-12T10:45:19.322330Z","shell.execute_reply.started":"2022-02-12T10:45:14.213352Z","shell.execute_reply":"2022-02-12T10:45:19.320920Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" customers['fashion_news_frequency'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:19.323997Z","iopub.execute_input":"2022-02-12T10:45:19.324462Z","iopub.status.idle":"2022-02-12T10:45:19.553497Z","shell.execute_reply.started":"2022-02-12T10:45:19.324424Z","shell.execute_reply":"2022-02-12T10:45:19.552844Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ax = sns.histplot(data = customers, x = 'fashion_news_frequency', edgecolor = 'white', hue = 'fashion_news_frequency')\nax.set_xlabel('Number of customers per fashion news frequency')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:19.554812Z","iopub.execute_input":"2022-02-12T10:45:19.555175Z","iopub.status.idle":"2022-02-12T10:45:24.668300Z","shell.execute_reply.started":"2022-02-12T10:45:19.555143Z","shell.execute_reply":"2022-02-12T10:45:24.666827Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Transactions","metadata":{}},{"cell_type":"code","source":"transactions.head()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:24.670436Z","iopub.execute_input":"2022-02-12T10:45:24.670831Z","iopub.status.idle":"2022-02-12T10:45:24.687448Z","shell.execute_reply.started":"2022-02-12T10:45:24.670781Z","shell.execute_reply":"2022-02-12T10:45:24.686290Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Top 10 Dates with Most transaction**","metadata":{}},{"cell_type":"code","source":"transactions['t_dat'].value_counts()[:10]","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:24.689236Z","iopub.execute_input":"2022-02-12T10:45:24.689506Z","iopub.status.idle":"2022-02-12T10:45:28.799786Z","shell.execute_reply.started":"2022-02-12T10:45:24.689469Z","shell.execute_reply":"2022-02-12T10:45:28.798754Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ax = transactions['t_dat'].value_counts()[:10].plot(kind = 'bar', color = 'blue')\nax.set_title('Top 10 dates with most transactions')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:28.802041Z","iopub.execute_input":"2022-02-12T10:45:28.803512Z","iopub.status.idle":"2022-02-12T10:45:33.268005Z","shell.execute_reply.started":"2022-02-12T10:45:28.803460Z","shell.execute_reply":"2022-02-12T10:45:33.266831Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Top 10 customers with highest number of transactions**","metadata":{}},{"cell_type":"code","source":"transactions['customer_id'].value_counts()[:10]","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:33.269562Z","iopub.execute_input":"2022-02-12T10:45:33.270462Z","iopub.status.idle":"2022-02-12T10:45:43.215576Z","shell.execute_reply.started":"2022-02-12T10:45:33.270406Z","shell.execute_reply":"2022-02-12T10:45:43.214430Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Prices**","metadata":{}},{"cell_type":"code","source":"pd.set_option('display.float_format', '{:.4f}'.format)\ntransactions['price'].describe()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:43.217095Z","iopub.execute_input":"2022-02-12T10:45:43.218019Z","iopub.status.idle":"2022-02-12T10:45:44.472525Z","shell.execute_reply.started":"2022-02-12T10:45:43.217975Z","shell.execute_reply":"2022-02-12T10:45:44.471401Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"art_merge = articles[['index_name', 'index_group_name', 'section_name', 'garment_group_name','colour_group_name', 'product_group_name']]\ntrans_merge = transactions[['t_dat', 'customer_id', 'article_id', 'price']]\ncombinedData = pd.concat(objs = [art_merge,trans_merge], axis = 1)\ncombinedData.head()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:44.473506Z","iopub.execute_input":"2022-02-12T10:45:44.473739Z","iopub.status.idle":"2022-02-12T10:45:50.473855Z","shell.execute_reply.started":"2022-02-12T10:45:44.473700Z","shell.execute_reply":"2022-02-12T10:45:50.472790Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"section_mean = combinedData[['section_name','price']].groupby('section_name'). mean()\nsection_mean.head()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:45:50.475490Z","iopub.execute_input":"2022-02-12T10:45:50.475874Z","iopub.status.idle":"2022-02-12T10:46:05.364982Z","shell.execute_reply.started":"2022-02-12T10:45:50.475827Z","shell.execute_reply":"2022-02-12T10:46:05.363806Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Ladies Other section has the highest avergae mean price and young girl section is the lowest!**","metadata":{}},{"cell_type":"code","source":"f, ax = plt.subplots(figsize= (15,12))\nax = sns.barplot(x= section_mean.price, y = section_mean.index , color ='pink')\nax.set_xlabel('Price by Section')\nax.set_ylabel('Sections')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:46:05.366461Z","iopub.execute_input":"2022-02-12T10:46:05.366840Z","iopub.status.idle":"2022-02-12T10:46:06.870143Z","shell.execute_reply.started":"2022-02-12T10:46:05.366791Z","shell.execute_reply":"2022-02-12T10:46:06.869432Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Furniture has the highest Mean price while interior textile the lowest**","metadata":{}},{"cell_type":"code","source":"product= combinedData[['product_group_name','price']].groupby('product_group_name'). mean()\nproduct.head()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:46:06.871540Z","iopub.execute_input":"2022-02-12T10:46:06.872017Z","iopub.status.idle":"2022-02-12T10:46:10.948043Z","shell.execute_reply.started":"2022-02-12T10:46:06.871973Z","shell.execute_reply":"2022-02-12T10:46:10.946814Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"f, ax = plt.subplots(figsize= (12,9))\nax = sns.barplot(x= product.price, y = product.index , color ='orange')\nax.set_xlabel('Price by Product Group')\nax.set_ylabel('Product Group Name')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-12T10:46:10.949375Z","iopub.execute_input":"2022-02-12T10:46:10.949614Z","iopub.status.idle":"2022-02-12T10:46:11.327553Z","shell.execute_reply.started":"2022-02-12T10:46:10.949584Z","shell.execute_reply":"2022-02-12T10:46:11.326679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submission: \n\nhis task is to predict what article_id will be purchased in 7 days for each customer specified here","metadata":{"_kg_hide-input":true}},{"cell_type":"code","source":"sample = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')\nsample.head()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-02-12T10:46:45.481582Z","iopub.execute_input":"2022-02-12T10:46:45.481886Z","iopub.status.idle":"2022-02-12T10:46:50.495888Z","shell.execute_reply.started":"2022-02-12T10:46:45.481854Z","shell.execute_reply":"2022-02-12T10:46:50.494793Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Recommendation","metadata":{}},{"cell_type":"markdown","source":"* Look into the most frequenly purchased items\n* Look into the top products","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}