{"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":"

In this approach to build recommendations for H&M Group customers I am making a few assumptions:

\n
    \n
  1. Customers make purchases in certain section of the store
    2. \n
  2. Customers have the same taste in terms of colour and patterns
    3. \n
  3. Newer articles should be promoted
    4. \n
  4. Fasion sales is seasonal
    5. \n
  5. Customers do not buy the same article again
    6. \n
\nLoad and simplify the data","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:00:27.027579Z","iopub.execute_input":"2022-05-05T14:00:27.028641Z","iopub.status.idle":"2022-05-05T14:00:28.110288Z","shell.execute_reply.started":"2022-05-05T14:00:27.02859Z","shell.execute_reply":"2022-05-05T14:00:28.109324Z"}}},{"cell_type":"code","source":"#import libraries\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime\nfrom sklearn import preprocessing\nfrom scipy.sparse import coo_matrix\nfrom math import ceil\n\nimport matplotlib.pyplot as plt\n%matplotlib inline","metadata":{"_kg_hide-input":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Load data\ntransactions = pd.read_csv(\"../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv\", parse_dates = [0], dtype={'article_id':'str'}, usecols=['t_dat', 'customer_id', 'article_id'])\ncustomers = pd.read_csv(\"../input/h-and-m-personalized-fashion-recommendations/customers.csv\", dtype={'article_id':'str'}, usecols=['customer_id', 'age'])\narticles = pd.read_csv(\"../input/h-and-m-personalized-fashion-recommendations/articles.csv\", dtype={'article_id':'str'}, usecols=['article_id', 'index_group_no', 'section_no', 'product_type_no', 'product_code', 'graphical_appearance_no', 'perceived_colour_master_id'])","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:02:26.157815Z","iopub.execute_input":"2022-05-05T14:02:26.15869Z","iopub.status.idle":"2022-05-05T14:03:51.191088Z","shell.execute_reply.started":"2022-05-05T14:02:26.158629Z","shell.execute_reply":"2022-05-05T14:03:51.189513Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#CUSTOMERS\n#Customer_id label encoding - change to numerical format to save memory\nle_customer_id = preprocessing.LabelEncoder()\nle_customer_id.fit(customers['customer_id'])\ncustomers['customer_id'] = le_customer_id.transform(customers['customer_id'])\n\n#Age fill in na with 0\ncustomers['age'] = customers['age'].fillna(0)\ncustomers['age'] = customers['age'].astype('int')\n\n#ARTICLES\n#Extract the first 2 digits from article id to identify product release\narticles['article_id_2digit'] = articles['article_id'].apply(lambda x: int(x[:3])/100)\n\n#Create a new numeric article code to reduce memory\nle_article_id = preprocessing.LabelEncoder()\narticles['article_id'] = le_article_id.fit_transform(articles['article_id'])\n\nle_product_code = preprocessing.LabelEncoder()\narticles['product_code'] = le_product_code.fit_transform(articles['product_code'])\n\nle_product_type_no = preprocessing.LabelEncoder()\narticles['product_type_no'] = le_product_type_no.fit_transform(articles['product_type_no'])\n\nle_graphical_appearance_no = preprocessing.LabelEncoder()\narticles['graphical_appearance_no'] = le_product_type_no.fit_transform(articles['graphical_appearance_no'])\n\nle_perceived_colour_master_id = preprocessing.LabelEncoder()\narticles['perceived_colour_master_id'] = le_perceived_colour_master_id.fit_transform(articles['perceived_colour_master_id'])\n\nle_index_group_no = preprocessing.LabelEncoder()\narticles['index_group_no'] = le_index_group_no.fit_transform(articles['index_group_no'])\n\nle_section_no = preprocessing.LabelEncoder()\narticles['section_no'] = le_section_no.fit_transform(articles['section_no'])\n\n#List articles to be copied to transactions dataframe\narticles_features = ['article_id', 'article_id_2digit', 'product_code', 'product_type_no', 'graphical_appearance_no',\n 'perceived_colour_master_id', 'index_group_no', 'section_no']\n\n#Add 'item' column to serve for matrix factorization counting\narticles['item'] = 1\n\n#TRANSACTIONS\n#Customer_id change to numeric\ntransactions['customer_id'] = le_customer_id.transform(transactions['customer_id'])\n#Article_id to numeric\ntransactions['article_id'] = le_article_id.transform(transactions['article_id'])\n\n#Add articles info to transactions\ntransactions = transactions.merge(articles[articles_features], on = 'article_id', how = 'left')\n\n#Add month for seasonability\ntransactions['month'] = pd.DatetimeIndex(transactions['t_dat']).month\n\n#Add column with week\ntransactions['week'] = transactions['t_dat'].apply(lambda x: int(x.strftime(\"%Y%W\")))\n\ntransactions['item'] = 1","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:11:09.388689Z","iopub.execute_input":"2022-05-05T14:11:09.38997Z","iopub.status.idle":"2022-05-05T14:17:28.659344Z","shell.execute_reply.started":"2022-05-05T14:11:09.389861Z","shell.execute_reply":"2022-05-05T14:17:28.658053Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"1. Customers make purchases in certain section of the store
\nReasoning: Customers visit certain section of the store: men got to men's section, active people to sports section, etc.
\nImplementation: In transactions_train table I check in which index_group_no and section_no has each customer made his previous purchases. Then, based on articles table, I list all the articles in the sections, in which the each customer previously made the purchase. This is done by matrix factorization.","metadata":{}},{"cell_type":"markdown","source":"![1logic.JPG](attachment:b65acfeb-0374-4b91-a1df-063505cc0193.JPG)","metadata":{},"attachments":{"b65acfeb-0374-4b91-a1df-063505cc0193.JPG":{"image/jpeg":"/9j/4AAQSkZJRgABAQEAYABgAAD/4RD6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAQAAAISodpAAQAAAABAAAIWpydAAEAAAAgAAAQ0uocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFRvbWFzeiBQb3J6eWNraQAABZADAAIAAAAUAAAQqJAEAAIAAAAUAAAQvJKRAAIAAAADOTYAAJKSAAIAAAADOTYAAOocAAcAAAgMAAAInAAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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f+v9f/SaCtWsrSP+Qprv/X+v/pNBWkdpen6omW6NWiiisygooooAKKKKACvPfF+psdWttS0WbWrDUbMz2Rki8PT38Miboy4ZFA4yqlXDDPPXt6FRQBzHgKMDQZ53m1C4urq7ea6m1DT3snkkIUZWJgCqgBQOvTqTmunrnPEH/I2eFP8Ar8n/APSWWo9W1C51PUptNsLiS1tbbC3VxFw7uQD5aN/DgEFiOeQAQQauMXJ2Jk7I6eiuKPhnQ25l0mznbu88KyMfqzZJpP8AhF9A/wCgHpv/AICR/wCFa+yj3/D/AIJHO+x21FcT/wAIvoH/AEA9N/8AASP/AAo/4RfQP+gHpv8A4CR/4Ueyj3/D/ghzvsdtRXE/8IvoH/QD03/wEj/wo/4RfQP+gHpv/gJH/hR7KPf8P+CHO+x21FcT/wAIvoH/AEA9N/8AASP/AAo/4RfQP+gHpv8A4CR/4Ueyj3/D/ghzvsdtRXE/8IvoH/QD03/wEj/wo/4RfQP+gHpv/gJH/hR7KPf8P+CHO+xsXfg3TL28luZrrW1klYswh169iQE+iJMFUewAFbNpbJZ2cVtC0rRxKEUzTNK5A9XclmPuSTXHf8IvoH/QD03/AMBI/wDCj/hF9A/6Aem/+Akf+FHso9/w/wCCHO+x21FcT/wi+gf9APTf/ASP/Cj/AIRfQP8AoB6b/wCAkf8AhR7KPf8AD/ghzvsdtRXE/wDCL6B/0A9N/wDASP8Awo/4RfQP+gHpv/gJH/hR7KPf8P8AghzvsdtRXFDwzoa8xaTZwN2eCFY2H0ZcEVe0nULnTNSh02/uJLq1ucra3EvMiOAT5bt/FkAlSeeCCSSKmVLS6Y1PXU6eiiisTQKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuH8RWPwu0OS2h8U2Phe1k8siBL+3g3bNxPyhhnbuJ9sk13FeZ614ltNN8ZS6zo3iXwbEl/YRRMNU1QRyOI5JMMuB93LMOpBI7EHIB3fh99Gk0G1bwwLMaUVP2b7CqrDjJztC8Yznp3ql40eFPCtybjUdR05CyDz9MieSfO4YVVRWY56HABweo61d0DUG1XQbW9kubC6aZSTNp03mwPyR8j9xx+eaZ4i1seH9HN6LSW9laaK3htoSqtLJJIsaLuYgAbmGSTwKAOU+HljfW+pXc1x4dW0t3hCx6pdQmK8uTn7rq8sshXHOWZef4a7+sHQdX8QaheTR654ZOjwqmY5jfxz7znptXpxzmt6gDyXxDpOn3lxcJoei+Ml1drsGOU3V4lsz+YCdxaXYIjznGPl+7zivWq8U1KTyH1C+Nr41m8URajJ9nvLe01A2wj8/5AEC+UYhHgMu0k4bBYkE+uaNrEOt2Jure2vrZA5TZfWcltJkY52SAHHPXGKAL9FFFABRRRQAVla7/wAw7/r/AIv61q1la7/zDv8Ar/i/rV0/iRMtjVrK0j/kKa7/ANf6/wDpNBWrWVpH/IU13/r/AF/9JoKcdpen6oJbo1aKKKzKCiiigCtqN2bDS7q8WPzTbwvKI94XftUnGTwM46msPw/8QvC3iOGxWw1/S2vbyNXWxW9QzqxXJTZkNkc5GM8VtatPNbaLez2lv9qnit5HigP/AC1YKSF/E8fjXl3g7xf9t1q20+DXNP1q7m1KPJtraJGS3+x+ZJhUGVVZflyxyCdpOeKAOqvtKOi+F/Dnh8z/AGiMXMdvI5TaJVjjkl5XJ6mMcZNXpZY4IXmnkWOKNSzu7YVQOSST0FP8Vf8AH1oH/YRb/wBJZ65jxdcGR4LCaC6NkUae4khtZJVfb9yM7FOMthjnjC471tKfJSuXhqDxFdU/6stdPPt5m6up2Tz20Udwjtdo0kBT5lkUYyQw4PBB69KtVwmnTq/hPwdND5iSRXUEQLRshOY2VgMgZUg9RwcV3dKlUdRX9PyNsbhVhpKKvu1r5Nr8rfiFFFFbHnhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACMquhV1DKwwQRkEVP4Od28LwRuxb7PLNbKT/dimeNf0UVDUng7/kXm/wCv+9/9Kpamp8Hz/wAxx+I3aKKK5jYKKKKACiiigArK0j/kKa7/ANf6/wDpNBWrWVpH/IU13/r/AF/9JoK0jtL0/VEy3Rq0UUVmUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZWkf8AIU13/r/X/wBJoK1aytI/5Cmu/wDX+v8A6TQVpHaXp+qJlujVorj/AA3qN1pWj2r6nKZtNnZhHct1tW3kBHP9w9m/h6HjBrsKmUeV2CLugoooqSgooooAK4H4hXjaJrmj63Br9jpNxFFPbiO8s5roTo5Qt+7jYEBSqkv2zycGu+rzf4tJaxwwSjW7fTb25s7iyEU1lLdefA5jZyqRfOCpVPmwRzgjkYAOy8PXN/PaXMWr31heXttcGKVrC3eFEO1WClXdznDA5zjkViaNzBeOfvNqN5k+uLiRR+gFT/D2eG90C51CO+N9Pe3kk11N9iltF8zCjakco3BQqoMnOcZzVfRf+PW7/wCwje/+lUtb0tmZz3Q7VtWTSIYHe3muGuJ1gjjg27izZx95gO3rUNzrUlpZ29zc6dNbpJdJBKs8ibolY7Q/yFgRuKjqOpPbk1+KyltYBqVteTxLOHVrNZS8TgHDfuvn9sjuRXN332seB9VjuftJgmvFj05bst5xRpECg7vm+8SRu+bGM1jUqSi3Z9P6uevhMNRrQp3Wrkk2+t3bR33W9red7Hc0UUV1HihRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWfrPEFm4+8uo2eD6ZuI1P6E1oVna1/wAetp/2EbL/ANKoqqO6E9jsqKKz9W1YaULULZ3F5Ldz+RFDblAxbY7k5dlAG1G71xpNuyOi9jQorG/tzUP+hW1b/v7af/H6P7c1D/oVtW/7+2n/AMfquR/00TzI2aKxv7c1D/oVtW/7+2n/AMfo/tzUP+hW1b/v7af/AB+jkf8ATQcyNmisb+3NQ/6FbVv+/tp/8fo/tzUP+hW1b/v7af8Ax+jkf9NBzI2aKxv7c1D/AKFbVv8Av7af/H6P7c1D/oVtW/7+2n/x+jkf9NBzI2aKxv7c1D/oVtW/7+2n/wAfo/tzUP8AoVtW/wC/tp/8fo5H/TQcyNmisb+3NQ/6FbVv+/tp/wDH6P7c1D/oVtW/7+2n/wAfo5H/AE0HMjZorG/tzUP+hW1b/v7af/H6P7c1D/oVtW/7+2n/AMfo5H/TQcyNmisb+3b0cyeGdWRfXfbN+izE1JZ+I9Pu7hLZzNZ3T/dgvIWhdvZdww3/AAEmlyMfMjVoooqRhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVgaprHh3R4rv7bHHmxWHzIorQyP++crGqqqksWYEALk5+tb9edeJ5rVPiZp80WkNcyWslkt3cPfNFGpmkkjgPkgESuhMjAtjbngk4wAdxo+oWWq6Pb3ulHNpMu6MeWUI55BUgFSDkEEZBBrC+JFtNe+B7m3hhvLhZJoBNDYoWmkiEqmRUwDhioOD29R1F7wfJbS+G0exgaCH7VdAI0m85FxIGOcDqcnHbOKg8eC8PhGf7AbwDzoftP2DP2j7P5q+d5e3nd5e77vzdcc4oAzvBUdimpXBs4fF0beTyddnunixkfdErEbvpzjNdpXnngIabH4mu4/B51ZtAFmpm+3m5MYuC/y+Ubj5s7d27HH3O9dlrV9qNhbRvpOjvqsjPhokuEiKDH3suQD6YoA5o/EiMXGpM2kXK2NnbXE8Ny0ig3PkSiKTanVRuPBOM4ziu2rw+aGK4vZ/EcvhHXYtNjvHhuIE1mIWm7zx5rGMHPl+auWwdpKsSMZJ9woAKKKKACiiigCnqWof2bbLN9kurvc4XZaxb2HBOSM9OP1FYE2vf2vqmn2f8AZt7YbbpZPMv4vKV9oJ2r13N7cV1dZWu/8w7/AK/4v61tScb2a1M5p9zVrK0j/kKa7/1/r/6TQVq1laR/yFNd/wCv9f8A0mgqY7S9P1RUt0atFFFZlBRRRQBW1KW4g0m7lsVje5jgdoVkOFLhSVBPYZxXmHgjXNN1LX7C6g+Jlnf3d8oebSorG0iadthJU7V8zg5P3iePSvTdXtFv9FvrOTbsuLeSJt7FRhlI5I5A5615/wCBfFt3f6jZ6HH4j8M6glrDsdLOCaOSZEXG6Mk7G5wTtBH0oA6rxV/x9aB/2EW/9JZ6RlDKVYAgjBBHWl8Vf8fWgf8AYRb/ANJZ6K6o/CjF/EzNm0WGS+0yWPZFb6dvaK3SMBdxXap9AFBbjHcdMVpVS1q9l03QdQvreMSy21tJMkZzhmVSQOPUiuXv9Q1LS/Cv9oJ4h+1zTR2suHihGxWmQOyYA+QqxHzBiOPmpxgunUqpWnNJSd7f5t/m2drRXJ6jqWsXOua5p2h3KNNa2dtJFGNmUdncuASCN5QDbu+XJUngmsqV5dW1rw95esarDNHeXVvL58VsJYpFhckfLGUJxxkZGD681fKZ3PQaK4TUNc1UaVruswaoYW0q8eCLTxFGY3CMAA+V37nzxtYfeXHuzUNY11Y/EV9b6oYV0u+jit7Y28bIykRFlkONxHzHG1lIyeTxg5GLmO+org9Z13WvDsupWf219QZbe1linlhiVoTLM0TcDapA25G7ueTim6hqninTdLuy32q3Q3Fqtrdagls8oLyhJEKwnaVxgg8Hk89CDkYcx31FQ2kEltapFNdS3brnM0wQM3OediqvtwB0rnvEF7qWm6xHdz3F3BoirGrPZLCxWQyYPmiRSxU7lA8vn72e1SldlXOnorhINZ8S6hqN1Pp9veSJbam1sbcfZVtvKR9rbizeaH25bI4yRwR1t2NzqGs6fPfza89iZbm4thYpFFiPYXUICy7vMwgckkjr8uMYrlFc6uW6t4Joopp445J2KxI7gGQgZIUdzgZ4qWvOtAM1po3gUG5Nyty44nhiYwr9mY7UIQFeR1zu7E1astc1WTSNC1x9TaU6pfJBLp/lR+UiuzKVUhQ+5AMklj91uOwbgLmO7org9O1fWzp2l6tcaq0y3WrNZvamCNY/K82SMHIXduG0HOQOMEHknvKlqw07hUng7/kXm/6/73/0qlqOpPB3/IvN/wBf97/6VS1nU+D5/wCZUfiN2s3xHeTad4V1W9tWCz21lNNGxGcMqEg478itKsbxj/yIuvf9g24/9FNWENZI1lsw/sjVf+hku/8AwGg/+Io/sjVf+hku/wDwGg/+IrZop87/AKSFyoxv7I1X/oZLv/wGg/8AiKP7I1X/AKGS7/8AAaD/AOIrZoo53/SQcqMb+yNV/wChku//AAGg/wDiKhh8PahbzXEkXiK8DXEgkkP2eA5YKqZ+5xwi1v0Uc7/pIOVGN/ZGq/8AQyXf/gNB/wDEUf2Rqv8A0Ml3/wCA0H/xFbNFHO/6SDlRjf2Rqv8A0Ml3/wCA0H/xFH9kar/0Ml3/AOA0H/xFbNFHO/6SDlRjf2Rqv/QyXf8A4DQf/EUf2Rqv/QyXf/gNB/8AEVs0Uc7/AKSDlRjf2Rqv/QyXf/gNB/8AEUf2Rqv/AEMl3/4DQf8AxFbNFHO/6SDlRjf2Rqv/AEMl3/4DQf8AxFH9kar/ANDJd/8AgNB/8RWzRRzv+kg5UY39kar/ANDJd/8AgNB/8RR/ZGq/9DJd/wDgNB/8RWzRRzv+kg5UY39kar/0Ml3/AOA0H/xFH9kar/0Ml3/4DQf/ABFbNFHO/wCkg5UY39kar/0Ml3/4DQf/ABFH9kar/wBDJd/+A0H/AMRWzRRzv+kg5UY39kar/wBDJd/+A0H/AMRR/ZGq/wDQyXf/AIDQf/EVs0Uc7/pIOVGN/ZGq/wDQyXf/AIDQf/EUf2Rqv/QyXf8A4DQf/EVs0Uc7/pIOVGN/ZGq/9DJd/wDgNB/8RR/ZGq/9DJd/+A0H/wARWzRRzv8ApIOVGN/ZGq/9DJd/+A0H/wARR/ZGq/8AQyXf/gNB/wDEVs0Uc7/pIOVGN/ZGq/8AQyXf/gNB/wDEUf2Rqv8A0Ml3/wCA0H/xFbNFHO/6SDlRjf2Rqv8A0Ml3/wCA0H/xFH9kar/0Ml3/AOA0H/xFbNFHO/6SDlRjf2Rqv/QyXf8A4DQf/EUf2Rqv/QyXf/gNB/8AEVs0Uc7/AKSDlRjf2Rqv/QyXf/gNB/8AEUf2Rqv/AEMl3/4DQf8AxFbNFHO/6SDlRjf2Rqv/AEMl3/4DQf8AxFQw+HtQt5riSLxFeBriQSSH7PAcsFVM/c44Ra36KOd/0kHKijpulRafo0emuxuYlVlYyqP3gJJOR071nxyP4ZlWC5dn0dyFhnc5NoT0Rz/c7Kx6dDxg1vVHceT9ml+1eX5Gw+Z5mNu3HOc8YxSUtdQt2JKKw/C/nfZp/L8z+yt4/s/z8+Z5eOevOzP3c8469q3KUlZ2GndXCiiikMK4DxzeXFn430F7TXNM8PyNaXStfajH5qSrmL9ztLoOSN+dwPyccbhXf1xXjNLrVPEmk6DZ3On6c91bz3H227skuXPltGPKjV/lyfMyepwvA7gA3fDFzPd6QZLrXNP1x/NI+1afCI4wMD5cCR+R/vd+lYui/wDHrd/9hG9/9Kpat+A7qSfRbu3mNlLJY30tq1zYQiKK524O8KCQDztYAn5laqmi/wDHrd/9hG9/9Kpa3pbMznujRrJ1q+0q2ls49U8ySXzfOt4YYZJnLKPvbIwSQM9SMAkd8VrVh6lpuor4it9Y0lbWeRbZ7WWC5laIFSwYMrqrYIK9Mcg9sVrZPRkqUou8XZlqHxBpdxDdTQXayR2kCXEzqjELGyb1Ycc5UZ4zTf8AhI9Na7S2ie4mleNJP3FpLIqK4yu5lUhMgZwxBxWLfaBrzzaq1q2mudYso4biSR5E8mRUZWKqAdykNwCwI96c3h3U11Cwks0t7JoBbrcXkN7IHnSMfMjQ7Aj5+ZQWJIByPSqsiLstaZ4zsL7Tr68uUnsobKaSN2mglUMFcouCyDLEgfIMsCcYzVn/AISzRVtLq4mu2t0syguFuIJInj3kBSUdQ2CT1xjrzwayH8M6t9iureM2QMeqtqdnK0jHexmMmyRdvyjkjcCfXFJqfhjVdb/tC7vDZ213cR29vDDFM8kaxxzeaWZygJYkkAbcDA5OTh2jcLs0j430FRLuuZw0K75UNlMHjTGd7LsyEwfvkbferN74n0nT5fLuLpmIiE7GGF5ljjPR3KKQi9eWwOD6VXuNDuZtR1+dXiC6lYxW0IJOVZRKCW44H7wdM96xH8GX0U7OqR3sdxZwQTxf2rc2YVo02H/Vgh1I9QMfjRaIanTDxFpj6v8A2ZFO8t2ArFIoXcKrDIYsqkBSD1JxWhNNHbwPNO6xxxqXd2OAoAySawtM0tPD19qd/dS21rYSQWyJmY4hWJCp3MwHHIwc/Wpn8SaDfxvaWeraRfTzKUjtmvYyJiRwhxk4PTofoam3Yd+5InibS20179pLiG2Tb889pLEXLfdCBlBcnsFBzmgeJ9J+xyXLXDoIpVheKS3kWYSNgqvlFd5JBBAxyORXOv4O1SfTZYWmS3SK5gns7JdQnlSPywQyiYhXQMDgBRhdoI6kVMPCl2EjvLe1t7fUYb2K6Al1Oe7W4CIybWkkTcnyyNjAPOKq0RXZen8a2EepafbQw3c6XhkDOtpNuiKfwlNmc56g4IGCeCKmtvEsENpczapcwHZqEtpCtpDK7NtYgJswWZwBztBHBI4qO70/XLm90rUjHp5urOSbfbee6psddoxJsJJGAfugHOOMZNFPC+qWd0l/ZvZy3MOp3V0kMrsqPFNxgsFJVgMdiKLRDU138W6LHYJeSXhWJ7g2oVoZBIJgCfLMe3cGwpwCATkYzkZu6bqtnq9u81hIzrHIYpFeNo3jcdVZWAYHkHBHcVzq+FdQlvodRuZLZLmTWEv7iKN2KRosLRKqnaCzYKkkhc8+nOxo+lTafqWs3ErRlL67E8YQnIHlInPHXKn1pNR6DVzWrO1r/j1tP+wjZf8ApVFWjWdrX/Hraf8AYRsv/SqKlHdA9jsqxtc/5DHhz/sJP/6SXFbNY2uf8hjw5/2En/8ASS4rlhv9/wCRtLY2aKKKgoKKKKACiiigAooooAKKKKACiiigAooooAKgvLK21C1a3voI7iFuqSKCPr9fep6KNgMGzkuNC1KDS7uWS4sbjK2dzK2543AJ8lz1PAJVjycEHnBO9Wdr1i2o6FdQRHbPs3wP/clX5kb8GANT6ZerqWk2d8g2rdQJMB6BlB/rVy1XMStHYtUUUVBQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBBeyXMVjM9hBHc3KoTFDJL5au3YFsHaPfB+leY+I9M1jxRq0aar4R0sajbRLLm08WT28vlByVLeXACVD5K56HOO9eq15lr/iS2t9c/tjw34lisZb+0SKeK80W5uo3WN5Ajrs2lWBZwQSQeDgdwDtfCZiPhLTfs1raWkIgAjhsrn7REq9tsm1d3GDkjOSawfGOsWmraPe6dp2rw6fqVlqdrArz5ws++KRAyryUO5Qc4BGfrW34NgtrbwjYpY3cl7EVZzcyQmJpXZ2Z22EArli3HaoPE/hTwjq6f2j4q0rS5RbFZHu7yGP5VU5wzsPu8cgnBHBoAk0C41+a7kGt3miTxBMounLIHDZHJ3MeMVvVynhP/hX32+b/AIQb/hGftflfvv7H+z+Z5eR97y+ducdeM4rq6APD9SurU6xdJb2HiRvD73M9zLaDUoI7ORUuQk0mzBlWPzXLFdwBGTtxxXuFeTXOkXvii687S/B2nnTrXUroK8+uzQySN5+Jt0aIQEd49xjJIJAJAOa9ZoAKKKKACiiigArK13/mHf8AX/F/WtWsrXf+Yd/1/wAX9aun8SJlsatZWkf8hTXf+v8AX/0mgrVrK0j/AJCmu/8AX+v/AKTQU47S9P1QS3Rq0UUVmUFFFFAFDXobe48OalDe3H2a2ktJUmn/AOeSFCGb8Bk1xlnaa/JqHheHxFDoljZabcD7NcWU7u95J9nkRFRDGBEpQsSNzdMDrmui8c6TZax4K1SDUbae6ijtpZVht5mjd2CNgAjPPbkEex6Vwvhs+HYvF2l3Xh/RILvTFmWyi1ZtSlnZLlrdpD5aNlCoX5C4IILEY4NAHc+Kv+PrQP8AsIt/6Sz0UeKv+PrQP+wi3/pLPRXVH4UYvdhVCDQtItYLiG20uyhiuhieOO3RVlHPDAD5up6+tX6KoRmjw5ogtWtho2niBgA0QtU2kAlgCMY4JJ+pNPk0PSZdOj0+XS7J7KJt0ds1uhjQ88hcYB5P5mr9FF2BSk0XS5tRXUJtNs5L1SCty0CmQEdMNjPFSNp1k6To9nbstwweZTEpErDGC3HJ4HJ9BVmii7AgksrWZ5XmtoZGmi8mRmjBLx8/IfVfmPHTk+tVbfw/o1pC8VrpFjBHIyu6R2yKGZTlSQByQeQe1aNFF2Bm3kGtvcs1hqGnwQYG1J7B5WHr8wmUH8qbDodtLPHe6vaabeanGQVvI7IIwwflxuLMMf71alFF2FihNoek3GoLf3Gl2Ut4rKy3L26NICv3SGIzkYGPSpE0nTk1JtRTT7Vb1hhrkQqJDxjlsZ6cVboouwKNvomlWjK1rplnAyyGVTHbqpDkYLcDrgkZ64pYtF0u31B7+DTbOK8cktcpAokYnrlgMnNXaKLsCuun2SQxwpZwLFHJ5qIIlCo+S24DHBySc9cmrFFFABUng7/kXm/6/wC9/wDSqWo6k8Hf8i83/X/e/wDpVLUVPg+f+Y4/EbtZviOzm1HwrqtlaqGnubKaGNScZZkIAz25NaVFc6dnc1eqsY39r6r/ANC3d/8AgTB/8XR/a+q/9C3d/wDgTB/8XWzRVcy7fmKz7mN/a+q/9C3d/wDgTB/8XR/a+q/9C3d/+BMH/wAXWzRRzLt+YWfcxv7X1X/oW7v/AMCYP/i6hh8Q6hcTXEcXh28LW8gjkH2iAYYqr4+/zw6/nW/WVpH/ACFNd/6/1/8ASaCqjZpu35id01qR/wBr6r/0Ld3/AOBMH/xdH9r6r/0Ld3/4Ewf/ABdbNFTzLt+Y7PuY39r6r/0Ld3/4Ewf/ABdH9r6r/wBC3d/+BMH/AMXWzRRzLt+YWfcxv7X1X/oW7v8A8CYP/i6P7X1X/oW7v/wJg/8Ai62aKOZdvzCz7mN/a+q/9C3d/wDgTB/8XR/a+q/9C3d/+BMH/wAXWzRRzLt+YWfcxv7X1X/oW7v/AMCYP/i6P7X1X/oW7v8A8CYP/i62aKOZdvzCz7mN/a+q/wDQt3f/AIEwf/F0f2vqv/Qt3f8A4Ewf/F1s0Ucy7fmFn3Mb+19V/wChbu//AAJg/wDi6P7X1X/oW7v/AMCYP/i62aKOZdvzCz7mN/a+q/8AQt3f/gTB/wDF0f2vqv8A0Ld3/wCBMH/xdbNFHMu35hZ9zG/tfVf+hbu//AmD/wCLo/tfVf8AoW7v/wACYP8A4utmijmXb8ws+5jf2vqv/Qt3f/gTB/8AF0f2vqv/AELd3/4Ewf8AxdbNFHMu35hZ9zG/tfVf+hbu/wDwJg/+Lo/tfVf+hbu//AmD/wCLrZoo5l2/MLPuY39r6r/0Ld3/AOBMH/xdH9r6r/0Ld3/4Ewf/ABdbNFHMu35hZ9zG/tfVf+hbu/8AwJg/+Lo/tfVf+hbu/wDwJg/+LrZoo5l2/MLPuY39r6r/ANC3d/8AgTB/8XR/a+q/9C3d/wDgTB/8XWzRRzLt+YWfcxv7X1X/AKFu7/8AAmD/AOLo/tfVf+hbu/8AwJg/+LrZoo5l2/MLPuY39r6r/wBC3d/+BMH/AMXR/a+q/wDQt3f/AIEwf/F1s0Ucy7fmFn3Mb+19V/6Fu7/8CYP/AIuoYfEOoXE1xHF4dvC1vII5B9ogGGKq+Pv88Ov51v1laR/yFNd/6/1/9JoKqNmm7fmJ3TWpPpuqxaho0epOptomVmYSsP3YBIOT07Vnxxv4mlWe5Rk0dCGhgcYN4R0dx/c7qp69TxgVm+HNNutU0i1j1SIw6bAS0ds3W6beSHf/AGBxhe/U8YFdhRK0G7AryWoUUUVkWFFFFABXn3i19U1bxINIv/ASa9oiQPKr3EkBRpAyYYF/uEAsMHBPJ6A16DXn3xIjtRf2ZuNY/s5Lu0msbpfsU07SWzvGZNjR/cfCbQTn72ccUAdD4Lgurfw/5dzptnpUAmf7HY2bIywQ8YUlAFJJ3Hj16k5NZui/8et3/wBhG9/9Kpam+Hi6N/Yt9J4XmhfSJb+R7aKCBoY4BtUMgUgY+cMeBjLH3qLReLa7B6jUb3P/AIEyH+tb0tmZz3RoUUUVoQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWdrX/Hraf9hGy/8ASqKtGs7Wuba0Hc6jZY/8CYzVR3QnsdlVLU9Jt9WSBbh5o2t5fOieCUxsrbWTqP8AZdh+NXaK402ndHRuY3/CNQ/9BLVv/A+T/Gj/AIRqH/oJat/4Hyf41s0VXPLuTyoxv+Eah/6CWrf+B8n+NH/CNQ/9BLVv/A+T/Gtmijnl3DlRjf8ACNQ/9BLVv/A+T/Gj/hGof+glq3/gfJ/jWzRRzy7hyoxv+Eah/wCglq3/AIHyf40f8I1D/wBBLVv/AAPk/wAa2aKOeXcOVGN/wjUP/QS1b/wPk/xo/wCEah/6CWrf+B8n+NbNFHPLuHKjG/4RqH/oJat/4Hyf40f8I1D/ANBLVv8AwPk/xrZoo55dw5UY3/CNQ/8AQS1b/wAD5P8AGj/hGof+glq3/gfJ/jWtNPFbxGW4kSKMdXdgoH4mqv8AbOl/9BK0/wC/6/40c02FomXa2jab4wt7aO9vZoZrCaRkuLlpBuWSIAjJ44Y/nXQ1gC8trvxzZ/ZbiKfbptxu8tw2P3sPXFb9E76XCPUKxvB//IlaN7WUQH/fIrZrm/CWq6fF4O0mOW/tUdbSMMrTKCDtHBGaEm4v+u4P4jpKKqJq2nSSKkeoWrOxwqrMpJPsM1bqbNFBRRRSAKKKKACiiigAooooAKKKKACiiigArz2K88byePtbsLXUNDIht7eZIJ4ZyI42aYKRhsbjt59cDgV6FXl3j8adN4wQjw7f6nfRRW1tJcw67NYJGLiVljjARxnLKSTj05PYA9B0KyvdP0S3ttVvft14oJmnClQzFi2ACSQozgZJOAKy/H2nyah4QmWD+zw9vNDdhtSnMVuPKlWTMjbW+X5eRj8R1qfwVPFceDrCS3tpbVNrKbea6e5eJg7BlaR/mYhgQc/QcVV8d28eo+HZLNJ7D7RDNb3gtr6YJHOIplfY/orFducEAkcHpQBU8CeIJ/EL3Fwg8LyWsa7TLoeotcsHz91wYk2jGT1/CuyrhvCby6t4xudYksNO0oLYi2FvbXsVxLP8+7e/l8AL0Xkn526V3NAHkdnoUFvquoSaz4G8S6jez6lNM2oQXiLHMpkPltt+0rtATauNvRRXrlcTpvxItFkubTxBa6hbXsN5NAot9Iu5YpIw5Ebh1jIOV255HOeMV2rsERnbOFGTgZoAWivItU8RC88Y/wBrWHizXLG1Fs8KQL4UunMe5kOBug5Hyk5PI6Diu48C/Zm0Oee21C/1F7i7eWe4vrJ7R2kIHAiZE2qAFAwMHnknNAHS0UUUAFZWu/8AMO/6/wCL+tatZWu/8w7/AK/4v61dP4kTLY1aytI/5Cmu/wDX+v8A6TQVq1laR/yFNd/6/wBf/SaCnHaXp+qCW6NWiiisygooooAgvnEen3LtdLZhYmJuXxiHg/Oc8YHXnjivM9L1pB4t0Gy0rx9pviO3nuZBPYWUFqzxjynYS5i5UAjBPH3xz2PpOqC6Oj3g05Y3vDA/kLJ90ybTtB9s4rh9G8QPfal4fsdHsdWiuoWK6w15pbW6iMRMDvcoFLeYEI8skHntQB0XjBTHY2Wof8s9PvFnmOPuxlHjZvoBJk+wNICCAQcg9CK6B0WRGR1DKwwVIyCPSuZfw5qOm/LoVzDLa8bLW9Zh5Q9EkAJ2+xBx644renJWszKUXe6JqKr/AGTxN/0DtJ/8GUn/AMYo+yeJv+gdpP8A4MpP/jFaadydSxRVf7J4m/6B2k/+DKT/AOMUfZPE3/QO0n/wZSf/ABijTuGpYoqv9k8Tf9A7Sf8AwZSf/GKPsnib/oHaT/4MpP8A4xRp3DUsUVX+yeJv+gdpP/gyk/8AjFH2TxN/0DtJ/wDBlJ/8Yo07hqWKKr/ZPE3/AEDtJ/8ABlJ/8Yo+yeJv+gdpP/gyk/8AjFGncNSxRVf7J4m/6B2k/wDgyk/+MUfZPE3/AEDtJ/8ABlJ/8Yo07hqWKKr/AGTxN/0DtJ/8GUn/AMYo+yeJv+gdpP8A4MpP/jFGncNSxRVf7J4m/wCgdpP/AIMpP/jFH2PxN/0D9JH/AHEpP/jFGncNSS4uIrS2kuLmRY4YlLu7HhQOpq74Utprbw1bi5QxyzPLcMhGCplkaTBHYjfiqdr4burueObxBcxyJGQy2VsCIiwOQXY8vgjgcD1BrpKzqSVuVFxi73YUUUVgaBRRRQAUUUUAFZWkf8hTXf8Ar/X/ANJoK1aytI/5Cmu/9f6/+k0FaR2l6fqiZbo1aKKKzKCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACsrSP+Qprv/X+v/pNBWrWVpH/IU13/AK/1/wDSaCtI7S9P1RMt0atFFFZlBRRRQAUUUUAFc54hvdbGs2Gm+HdQ0y2uLiKWVo76xluMqhUFspIgUAuBznJIx0NdHWHr/ho6zdWt7ZareaRqNqrxx3dn5ZJjcqWRlkVlZSUU8jII4PXIBX8EQGDTb/z75Ly9k1GZ7zyrZreOKbIyiRsSQuADkk7t27PzVW1OFtA1O5vHRm0u7bzZJEXP2WXgMWA52N1z2Oc4BFbPh/QovD+nPbJdXF7NNK09xdXTAyTyNjLNgADgAAAAAACtSrhLlZMo3RzcM8VxGJLeVJUPRkYMD+Ip9XJvC3h+5lMlxoWmyyMcl5LONifxIqP/AIQ/wz/0Luk/+AMf/wATWvtIEcsivRVj/hD/AAz/ANC7pP8A4Ax//E0f8If4Z/6F3Sf/AABj/wDiaftIByyK9FWP+EP8M/8AQu6T/wCAMf8A8TR/wh/hn/oXdJ/8AY//AImj2kA5ZFeirH/CH+Gf+hd0n/wBj/8AiaP+EP8ADP8A0Luk/wDgDH/8TR7SAcsivRVj/hD/AAz/ANC7pP8A4Ax//E0f8If4Z/6F3Sf/AABj/wDiaPaQDlkV6Ksf8If4Z/6F3Sf/AABj/wDiaP8AhD/DP/Qu6T/4Ax//ABNHtIByyK9FWP8AhD/DP/Qu6T/4Ax//ABNH/CH+Gf8AoXdJ/wDAGP8A+Jo9pAOWRXoqx/wh/hn/AKF3Sf8AwBj/APiaP+EP8M/9C7pP/gDH/wDE0e0gHLIqTTxW8ZkuJUiQdWdgoH4mq2mQtr+p214iMul2jebHI64+1S8hSoPOxeue5xjIBrYh8LeH7aUSW+habFIpyHjs41I/ECtWpdRW90ag+oUUUVgaBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFADJYo54XinjWSN1KujrlWB6gg9RXNy+C7a0lM+gLb2xJy1pcQCW3b6A8x/wDASB/smunoqoycdhNJ7nOWeqWOl3Aj1fS4tEuX+QTBVMEuccLMAByccNtJ9K6MHIyORTZI0ljaOVFdGGGVhkEehFYh8OSWP/Iu6hJpkZ4a2ZBNAB6ohPyHHQKQvqpp+7LyFqjUv9Rs9MtvP1C5jt4sgBpGxknoB6k+g5rnvssmr8aVolpp1qf+Xu+tF8xh/sQ8EfVyMf3T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Customers keep the same taste
\nReasoning: If customer previously bought clothers in white, he/she will prefer the same colour / pattern for their next purchase
\nImplementation: Using matrix factorization I filter articles according customer colour and pattern preference\n","metadata":{}},{"cell_type":"markdown","source":"![2logic.jpg](attachment:ef5a70ec-6b1d-4e4e-88b9-1eac5b3abc10.jpg)","metadata":{},"attachments":{"ef5a70ec-6b1d-4e4e-88b9-1eac5b3abc10.jpg":{"image/jpeg":"/9j/4AAQSkZJRgABAQEAYABgAAD/4RD6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAQAAAISodpAAQAAAABAAAIWpydAAEAAAAgAAAQ0uocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFRvbWFzeiBQb3J6eWNraQAABZADAAIAAAAUAAAQqJAEAAIAAAAUAAAQvJKRAAIAAAADNzcAAJKSAAIAAAADNzcAAOocAAcAAAgMAAAInAAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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Newer articles should be promoted
\nReasoning: Fasion changes, so the company releases new products to attract customers. I assume H&M targets to attract customers with the new articles.
\nImplementation: I assume the article_id is created subsequently for every new product. In other words, the higher article_id the newer article. Therefore I use the first 2 digits of article_id as a factor to identify new products.\n","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(20,3))\nplt.scatter(transactions.groupby('article_id_2digit', as_index=False).t_dat.min()['article_id_2digit'], transactions.groupby('article_id_2digit', as_index=False).t_dat.min()['t_dat'])\nplt.xticks(ticks=[x/100 for x in range(10, 100, 5)], labels=[l for l in range(10,100,5)])\nplt.xlabel('First 2 digits of article_id')\nplt.ylabel('First date the article was purchased')\nplt.title('Article_id (first 2 digits) vs first date the article was bought')\nplt.text(0.65, 18350, 'Higher article_id -> newer product', fontsize=16, c='red')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:34:07.707324Z","iopub.execute_input":"2022-05-05T14:34:07.70819Z","iopub.status.idle":"2022-05-05T14:34:10.699944Z","shell.execute_reply.started":"2022-05-05T14:34:07.708145Z","shell.execute_reply":"2022-05-05T14:34:10.698874Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![3logic.jpg](attachment:f184108d-d60e-4e88-b9af-8937434c29f0.jpg)","metadata":{},"attachments":{"f184108d-d60e-4e88-b9af-8937434c29f0.jpg":{"image/jpeg":"/9j/4AAQSkZJRgABAQEAYABgAAD/4RD6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAQAAAISodpAAQAAAABAAAIWpydAAEAAAAgAAAQ0uocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFRvbWFzeiBQb3J6eWNraQAABZADAAIAAAAUAAAQqJAEAAIAAAAUAAAQvJKRAAIAAAADNDEAAJKSAAIAAAADNDEAAOocAAcAAAgMAAAInAAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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Fasion is seasonable
\nReasoning: People wear different clothes depending on the season: dresses in summer, jackets in winter, etc
\nImplementation: The latest recorded transaction date is end of September. I target to recommend articles popular in October. Therefore, I check in transactions which articles have significantly higher sales in October and assign higher seasonality factor to them
","metadata":{}},{"cell_type":"code","source":"articles_seasonality = transactions.groupby(['product_type_no', 'month'], as_index=False).article_id.count()\nfig, axs = plt.subplots(1,3, figsize=(20,4))\ntype_no1 = 1\naxs[0].bar(articles_seasonality[articles_seasonality['product_type_no'] == type_no1].month, articles_seasonality[articles_seasonality['product_type_no'] == type_no1].article_id)\naxs[0].set_title('Article with high sales in October')\ntype_no2 = 2\naxs[1].bar(articles_seasonality[articles_seasonality['product_type_no'] == type_no2].month, articles_seasonality[articles_seasonality['product_type_no'] == type_no2].article_id)\naxs[1].set_title('Article with low sales in October')\ntype_no3 = 12\naxs[2].bar(articles_seasonality[articles_seasonality['product_type_no'] == type_no3].month, articles_seasonality[articles_seasonality['product_type_no'] == type_no3].article_id)\naxs[2].set_title('Article with relativel stable sales')\n\nfor ax in axs.flat:\n ax.set(xlabel='Months', ylabel='Sales')","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:48:18.310225Z","iopub.execute_input":"2022-05-05T14:48:18.310531Z","iopub.status.idle":"2022-05-05T14:48:20.684673Z","shell.execute_reply.started":"2022-05-05T14:48:18.310495Z","shell.execute_reply":"2022-05-05T14:48:20.683715Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def score_seasonality(sales, month=10):\n if sales[month] >= sales.mean() * 1.5:\n return 1.1\n elif (sales[month] < sales.mean() * 1.5) & (sales[month] > sales.mean()):\n return 1\n elif (sales[month] < sales.mean()) & (sales[month] >= sales.mean() * 0.5):\n return 0.9\n else:\n return 0.8\narticles_seasonality_pivot = pd.pivot_table(articles_seasonality, values='article_id', index='product_type_no', columns='month', aggfunc='sum', fill_value=0)\narticles_seasonality_pivot['seasonal_factor'] = articles_seasonality_pivot.apply(score_seasonality, axis=1)\narticles_seasonality_pivot.reset_index(inplace=True)\narticles = articles.merge(articles_seasonality_pivot[['product_type_no', 'seasonal_factor']], on='product_type_no', how='left')\narticles.loc[articles['seasonal_factor'].isna(), 'seasonal_factor'] = 1","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:52:23.18171Z","iopub.execute_input":"2022-05-05T14:52:23.182179Z","iopub.status.idle":"2022-05-05T14:52:23.286475Z","shell.execute_reply.started":"2022-05-05T14:52:23.182137Z","shell.execute_reply":"2022-05-05T14:52:23.285488Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![4logic.jpg](attachment:f42ce833-1ea3-403b-b0bd-3569c37011a7.jpg)","metadata":{},"attachments":{"f42ce833-1ea3-403b-b0bd-3569c37011a7.jpg":{"image/jpeg":"/9j/4AAQSkZJRgABAQEAYABgAAD/4RD6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAQAAAISodpAAQAAAABAAAIWpydAAEAAAAgAAAQ0uocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFRvbWFzeiBQb3J6eWNraQAABZADAAIAAAAUAAAQqJAEAAIAAAAUAAAQvJKRAAIAAAADNjEAAJKSAAIAAAADNjEAAOocAAcAAAgMAAAInAAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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code summarizes the logic","metadata":{}},{"cell_type":"code","source":"transactions_original = transactions.copy()\n\n#Create a dataframe to collect prediction for all groups\ntotal_recommendation = pd.DataFrame(columns=['customer_id', 'r1', 'r2', 'r3', 'r4', 'r5', 'r6', 'r7', 'r8', 'r9', 'r10', 'r11', 'r12'])\n\n#To reduce the size of the matrix I iterate through the customers in groups of size 10000 customers\n#The number of customers to calculate in each iteration\ngroup_size = 10000\n#How many iterations\nnumber_iterations = ceil(customers['customer_id'].max() / group_size)\n\nfor i in range(number_iterations):\n #Filter the transactions for the customer \n transactions = transactions_original[transactions_original['customer_id'].isin(range(i*group_size, (i+1)*group_size))]\n \n #Encode customer_ids to reduce the matrix size\n le_customer_id2 = preprocessing.LabelEncoder()\n transactions['customer_id'] = le_customer_id2.fit_transform(transactions['customer_id'])\n \n #Add dummy row to allign number of features in transactions and articles dataframes\n if (transactions['product_code'].max() < articles['product_code'].max()) | (transactions['product_type_no'].max() < articles['product_type_no'].max()) | (transactions['graphical_appearance_no'].max() < articles['graphical_appearance_no'].max()) | (transactions['perceived_colour_master_id'].max() < articles['perceived_colour_master_id'].max()) | (transactions['index_group_no'].max() < articles['index_group_no'].max()) | (transactions['section_no'].max() < articles['section_no'].max()) | (transactions['article_id'].max() < articles['article_id'].max()):\n transactions = transactions.append(pd.DataFrame({'t_dat':transactions['t_dat'].max(),\n 'customer_id':transactions['customer_id'].max(),\n 'article_id':articles['article_id'].max(),\n 'article_id_2digit':articles['article_id_2digit'].max(),\n 'product_code':articles['product_code'].max(),\n 'product_type_no':articles['product_type_no'].max(),\n 'graphical_appearance_no':articles['graphical_appearance_no'].max(),\n 'perceived_colour_master_id':articles['perceived_colour_master_id'].max(),\n 'index_group_no':articles['index_group_no'].max(),\n 'section_no':articles['section_no'].max(),\n 'month':transactions['month'],\n 'week':transactions['week'],\n 'item':0}, index = [0]), ignore_index=True)\n \n #Identify indexgroup / sections from which the customers purchased articles\n #Each index group and section gets score depending on how may articles the customers bought articles from\n #Detail to article_id level\n #1) Construct a matrix of customer_id - index_group_no\n customer_indexgroup = coo_matrix((transactions['item'], (transactions['customer_id'], transactions['index_group_no'])))\n #2) Construct a matrix of products index_group - section\n indexgroup_section = coo_matrix((articles['item'], (articles['index_group_no'], articles['section_no'])))\n indexgroup_section_csr = indexgroup_section.tocsr()\n indexgroup_section_csr[indexgroup_section_csr > 0] = 1\n #3) Construct a matrix of customer purchases - customer_id per section_no\n customer_section = customer_indexgroup.dot(indexgroup_section_csr)\n #4) Construct a matrix of customer_id per section_no\n customer_section2 = coo_matrix((transactions['item'], (transactions['customer_id'], transactions['section_no'])))\n #5) Combine the two matrix\n customer_section = customer_section + customer_section2\n #6) Construct a matrix of section_no per article_id\n section_article = coo_matrix((articles['item'], (articles['section_no'], articles['article_id'])))\n section_article_csr = section_article.tocsr()\n section_article_csr[section_article_csr > 0] = 1\n #7) Construct a matrix of customer_id and article\n customer_article = customer_section.dot(section_article_csr)\n \n #Apply customer appearance and colour preferences\n #8) Construct a matrix customer_id appearance\n customer_appearance = coo_matrix((transactions['item'], (transactions['customer_id'], transactions['graphical_appearance_no'])))\n #9) Construct a matrix appearance - article_id\n appearance_article = coo_matrix((articles['item'], (articles['graphical_appearance_no'], articles['article_id'])))\n #10) Combine to obtain customer - article_id matrix\n customer_article_appearance = customer_appearance.dot(appearance_article)\n #11) Construct a matrix customer_id - colour\n customer_colour = coo_matrix((transactions['item'], (transactions['customer_id'], transactions['perceived_colour_master_id'])))\n #12) Construct a matrix colour - article_id\n colour_article = coo_matrix((articles['item'], (articles['perceived_colour_master_id'], articles['article_id'])))\n #13) Combine to obrain customer_id - colour\n customer_article_colour = customer_colour.dot(colour_article)\n #14) Combine apperance and colour\n customer_article_appearance = customer_article_appearance.multiply(customer_article_colour)\n #15) Apply to customer_id - article_id matrix\n customer_article = customer_article.multiply(customer_article_appearance)\n \n #16) Apply the seasonality factor\n customer_article = customer_article.multiply(articles['seasonal_factor'].values)\n #17) Apply the release date factor\n customer_article = customer_article.multiply(articles['article_id_2digit'].values)\n \n #18) Remove the article already bought by the customer\n customer_bought = coo_matrix((transactions['item'], (transactions['customer_id'], transactions['article_id'])))\n customer_bought = customer_bought * 100000\n #19)\n customer_article = customer_article - customer_bought\n \n #Identify the top scored articles for every customer_id\n top_recommendations = []\n while len(top_recommendations) < 12:\n #Identify the highest score for each customer\n top_temp = customer_article.argmax(1).reshape(-1).tolist()[0]\n top_recommendations.append(top_temp)\n #Remove the top recommendations\n if max(top_temp) < customer_article.get_shape()[1]:\n del_max = coo_matrix(([200000]*len(top_temp) + [0], ([i for i in range(len(top_temp))] + [len(top_temp)-1], top_temp + [customer_article.get_shape()[1]-1])))\n else:\n del_max = coo_matrix(([200000]*len(top_temp), ([i for i in range(len(top_temp))], top_temp)))\n customer_article = customer_article - del_max\n\n #Sumarize the recommendation in a dataframe\n result_df = pd.DataFrame({'customer_id':[j for j in range(customer_article.get_shape()[0])],\n 'r1':top_recommendations[0],\n 'r2':top_recommendations[1],\n 'r3':top_recommendations[2],\n 'r4':top_recommendations[3],\n 'r5':top_recommendations[4],\n 'r6':top_recommendations[5],\n 'r7':top_recommendations[6],\n 'r8':top_recommendations[7],\n 'r9':top_recommendations[8],\n 'r10':top_recommendations[9],\n 'r11':top_recommendations[10],\n 'r12':top_recommendations[11]})\n \n #Convert back to the original customer_id coding\n result_df['customer_id'] = le_customer_id2.inverse_transform(result_df['customer_id'])\n \n total_recommendation = total_recommendation.append(result_df[['customer_id', 'r1', 'r2', 'r3', 'r4', 'r5', 'r6', 'r7', 'r8', 'r9', 'r10', 'r11', 'r12']], ignore_index=True)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"I do not include the rest of the code as the main purpose of the notebook was to share the logic.

\n Final comments \n\n","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}