{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":false,"toc_position":{},"toc_section_display":true,"toc_window_display":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"https://github.com/AdityaAS/Fashion-Recommendation-using-ML","metadata":{}},{"cell_type":"markdown","source":"# Import Data","metadata":{"heading_collapsed":true}},{"cell_type":"code","source":"# RUN THESE CELLS TO START THE PROJECT!\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-11-27T18:27:39.903773Z","iopub.execute_input":"2022-11-27T18:27:39.90425Z","iopub.status.idle":"2022-11-27T18:27:40.534152Z","shell.execute_reply.started":"2022-11-27T18:27:39.904214Z","shell.execute_reply":"2022-11-27T18:27:40.532944Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"articles = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv',dtype={'article_id': str})\ntransactions = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/transactions_train.csv',dtype={'article_id': str})\ncustomers = pd.read_csv(\"/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv\")","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-11-27T18:27:43.355276Z","iopub.execute_input":"2022-11-27T18:27:43.355673Z","iopub.status.idle":"2022-11-27T18:29:13.158347Z","shell.execute_reply.started":"2022-11-27T18:27:43.355641Z","shell.execute_reply":"2022-11-27T18:29:13.154632Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"**<span style=\"color:#023e8a;\"> This table contains all h&m articles with details such as a type of product, a color, a product group and other features.</span>**  \n**<span style=\"color:#023e8a;\"> Article data description: </span>**\n\n> `article_id` **<span style=\"color:#023e8a;\">: A unique identifier of every article.</span>**  \n> `product_code`, `prod_name` **<span style=\"color:#023e8a;\">: A unique identifier of every product and its name (not the same).</span>**  \n> `product_type`, `product_type_name` **<span style=\"color:#023e8a;\">: The group of product_code and its name</span>**  \n> `graphical_appearance_no`, `graphical_appearance_name` **<span style=\"color:#023e8a;\">: The group of graphics and its name</span>**  \n> `colour_group_code`, `colour_group_name` **<span style=\"color:#023e8a;\">: The group of color and its name</span>**  \n> `perceived_colour_value_id`, `perceived_colour_value_name`, `perceived_colour_master_id`, `perceived_colour_master_name` **<span style=\"color:#023e8a;\">: The added color info</span>**  \n> `department_no`, `department_name`: **<span style=\"color:#023e8a;\">: A unique identifier of every dep and its name</span>**  \n> `index_code`, `index_name`: **<span style=\"color:#023e8a;\">: A unique identifier of every index and its name</span>**  \n> `index_group_no`, `index_group_name`: **<span style=\"color:#023e8a;\">: A group of indeces and its name</span>**  \n> `section_no`, `section_name`: **<span style=\"color:#023e8a;\">: A unique identifier of every section and its name</span>**  \n> `garment_group_no`, `garment_group_name`: **<span style=\"color:#023e8a;\">: A unique identifier of every garment and its name</span>**  \n> `detail_desc`: **<span style=\"color:#023e8a;\">: Details</span>**  ","metadata":{"hidden":true}},{"cell_type":"code","source":"articles.nunique()","metadata":{"hidden":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Remove useless columns and other with same unique counts within the same group, if not the same, choose the greater one\ndf = articles[['article_id', 'product_code', 'product_type_name', 'product_group_name',\n               'graphical_appearance_name', 'colour_group_name','department_no', \n               'index_name','index_group_name','section_name','garment_group_name']]","metadata":{"hidden":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"df.nunique()","metadata":{"hidden":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":["article_id                   105542\n","product_code                  47224\n","product_type_name               131\n","product_group_name               19\n","graphical_appearance_name        30\n","colour_group_name                50\n","department_no                   299\n","index_name                       10\n","index_group_name                  5\n","section_name                     56\n","garment_group_name               21\n","dtype: int64"]},"metadata":{}}]},{"cell_type":"code","source":"df.head()","metadata":{"hidden":true,"scrolled":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>article_id</th>\n","      <th>product_code</th>\n","      <th>product_type_name</th>\n","      <th>product_group_name</th>\n","      <th>graphical_appearance_name</th>\n","      <th>colour_group_name</th>\n","      <th>department_no</th>\n","      <th>index_name</th>\n","      <th>index_group_name</th>\n","      <th>section_name</th>\n","      <th>garment_group_name</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0108775015</td>\n","      <td>108775</td>\n","      <td>Vest top</td>\n","      <td>Garment Upper body</td>\n","      <td>Solid</td>\n","      <td>Black</td>\n","      <td>1676</td>\n","      <td>Ladieswear</td>\n","      <td>Ladieswear</td>\n","      <td>Womens Everyday Basics</td>\n","      <td>Jersey Basic</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0108775044</td>\n","      <td>108775</td>\n","      <td>Vest top</td>\n","      <td>Garment Upper body</td>\n","      <td>Solid</td>\n","      <td>White</td>\n","      <td>1676</td>\n","      <td>Ladieswear</td>\n","      <td>Ladieswear</td>\n","      <td>Womens Everyday Basics</td>\n","      <td>Jersey Basic</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0108775051</td>\n","      <td>108775</td>\n","      <td>Vest top</td>\n","      <td>Garment Upper body</td>\n","      <td>Stripe</td>\n","      <td>Off White</td>\n","      <td>1676</td>\n","      <td>Ladieswear</td>\n","      <td>Ladieswear</td>\n","      <td>Womens Everyday Basics</td>\n","      <td>Jersey Basic</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0110065001</td>\n","      <td>110065</td>\n","      <td>Bra</td>\n","      <td>Underwear</td>\n","      <td>Solid</td>\n","      <td>Black</td>\n","      <td>1339</td>\n","      <td>Lingeries/Tights</td>\n","      <td>Ladieswear</td>\n","      <td>Womens Lingerie</td>\n","      <td>Under-, Nightwear</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0110065002</td>\n","      <td>110065</td>\n","      <td>Bra</td>\n","      <td>Underwear</td>\n","      <td>Solid</td>\n","      <td>White</td>\n","      <td>1339</td>\n","      <td>Lingeries/Tights</td>\n","      <td>Ladieswear</td>\n","      <td>Womens Lingerie</td>\n","      <td>Under-, Nightwear</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   article_id  product_code product_type_name  product_group_name  \\\n","0  0108775015        108775          Vest top  Garment Upper body   \n","1  0108775044        108775          Vest top  Garment Upper body   \n","2  0108775051        108775          Vest top  Garment Upper body   \n","3  0110065001        110065               Bra           Underwear   \n","4  0110065002        110065               Bra           Underwear   \n","\n","  graphical_appearance_name colour_group_name  department_no  \\\n","0                     Solid             Black           1676   \n","1                     Solid             White           1676   \n","2                    Stripe         Off White           1676   \n","3                     Solid             Black           1339   \n","4                     Solid             White           1339   \n","\n","         index_name index_group_name            section_name  \\\n","0        Ladieswear       Ladieswear  Womens Everyday Basics   \n","1        Ladieswear       Ladieswear  Womens Everyday Basics   \n","2        Ladieswear       Ladieswear  Womens Everyday Basics   \n","3  Lingeries/Tights       Ladieswear         Womens Lingerie   \n","4  Lingeries/Tights       Ladieswear         Womens Lingerie   \n","\n","  garment_group_name  \n","0       Jersey Basic  \n","1       Jersey Basic  \n","2       Jersey Basic  \n","3  Under-, Nightwear  \n","4  Under-, Nightwear  "]},"metadata":{}}]},{"cell_type":"markdown","source":"# EDA","metadata":{"heading_collapsed":true}},{"cell_type":"code","source":"f, ax = plt.subplots(figsize=(15, 7),dpi=200)\nax = sns.countplot(data=df, y='index_name', order = df['index_name'].value_counts().index, \n                   palette = 'Set2')\nax.set_xlabel('count by index name',fontsize=15,fontweight='bold')\nax.set_ylabel('index 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size 3000x1400 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"**Explanation:**\n1. Ladieswear absoutialy has the most indexes\n2. Menswear is less the Divided\n3. Sportswear has the least portion, which makes sense since H&M is a fashion brand|","metadata":{"hidden":true}},{"cell_type":"code","source":"l = list(df['garment_group_name'].value_counts().index)\ndf['garment_group_name'] = pd.Categorical(df['garment_group_name'], l)\n\nf, ax = plt.subplots(figsize=(15, 7),dpi=200)\nax = sns.histplot(data=df, y='garment_group_name', \n                  hue='index_group_name', multiple=\"stack\", palette = 'Set2')\nax.set_xlabel('count by garment group')\nax.set_ylabel('garment 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size 3000x1400 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"**Explanation**\n1. Jersey Fancy is the biggest portion of garment group, ladieswear and baby/children are the top 2 within it\n2. Many different kinds of accessories\n3. Menswear is a small portion in almost all garment group except shirts","metadata":{"hidden":true}},{"cell_type":"code","source":"l = list(df['index_group_name'].value_counts().index)\ndf['index_group_name'] = pd.Categorical(df['index_group_name'], l)\n\nf, ax = plt.subplots(figsize=(15, 7),dpi=200)\nax = sns.histplot(data=df, y='index_group_name', \n                  hue='index_name', multiple=\"stack\", palette = 'Set2')\nax.set_xlabel('count by garment group')\nax.set_ylabel('garment 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ExMTOR7+uWXX1i1alW6/m3btvH6668D8Ne//jVDyDHNeeedx1/+8hcA3nvvPRISEo5at6TcE62g49ojXmtSX0e2ZzZubZRqlCRJkiRJkiRJkiRJ0imkdOnS3HTTTVn2169fH4AffvghQ9+MGTOAlBBe2rjMdO3aNcu+9evXs3TpUgDuuOOObGu9+OKLOeusswBYuHBhhv7Dr6XeuXMn7du35+677yYMQypVqhQJ5+WW9u3bZ9l3+Pfz448/HnWtHTt28P3337Ny5Uq++uorvvrqq8hV4qtWreLgwYNZzq1Tp07kuuwjXXbZZZH3v//97ylbtmyWa6Q58rf/5JNPSExMBI7+mzVt2hSAgwcPRn5nSXkjKldXh2FYLRr7SJIkSZIkSZIkSZIk6cxRo0YNChTI+pyvtCDc7t2707UnJiby3XffASnXXGenQYMGWfYtWbIk8v6uu+7irrvuOmrNAJs2bcq0vX79+vz1r3/lscceY968eZH20aNHU758+WNaO6cuuuiiLPsODxQe+V2m+fLLLxk0aBBTpkzJ8vMBHDp0iB07dlChQoVM+7O7mjstLHk8446s9/Df7Oyzz85yjSNl95kk5b5onegoSZIkSZIkSZIkSZIknVSZXW98uLQQZNoV1Wl27txJGIYAWQbu0lSsWDHLvi1bthxLmRlkdw1yv3790oX4unfvzg033JCjfY5Hdt/l4WHS5OTkDP0jR46kXr16jB49+pgCgfv27TvhOnJab278ZpJyX1ROdJQkSZIkSZIkSZIkSZLyi7SQI0AQBDle5/AQ3bhx49JdrZydMmXKZNk3depUVq9eHfnzvHnz2LdvH0WKFMlxnbnp66+/pmfPniQlJVGhQgUefvhhrrnmGqpVq0aJEiUoVKgQAKNGjaJbt25A+u8/2tJ+s9jY2OO6jrpKlSq5VZKkY2DQUZIkSZIkSZIkSZIkSWeUw4OGmzdvznZsdv3lypWLvA+CgFq1ap1QXVu3bqVr164AlCxZkl27dvH111/Tt29fXnnllRNaO7eMGTOGpKQkYmJiiI+P5+KLL8503I4dO6JcWebSfrMDBw5Qrly547q+WlLeybOrq4MgiAmC4KwgCKoGQXBudq+8qlGSJEmSJEmSJEmSJEmnn8KFC1OjRg0AFi9enO3Y7Prr1q0beT9t2rQTrqtbt25s3ryZAgUK8NFHH9GuXTsAhg4dypQpU054/dywcuVKAOrUqZNlyBFgyZIl0SopWyf7N5MUHVENOqYGG58JgmA5kAhsBn4Cfszm9UM0a5QkSZIkSZIkSZIkSdLp77rrrgPgyy+/5Isvvshy3KhRo7Lsu/DCC7nkkksAePfdd1m7dm2O6xk+fDgTJ04EoE+fPjRv3pxhw4ZRtWpVALp06cLWrVtzvH5uSUpKAiAhISHLMZs2bWLChAnRKilbLVu2jFynPWjQoEj9kvK3qAUdgyC4EvgKeBKoDcQAwTG+JEmSJEmSJEmSJEmSpJOmR48eBEFKLOXee+9l7969GcaMGzeOyZMnZ7vOk08+CUBiYiJt27bNNoy4f/9+hg4dSmJiYrr21atX06dPHyDlZMTnnnsOgNKlS/PGG29QoEABNm/ezD333HPsHzBK0k7GXL16NZ999lmG/oSEBP7whz+wb9++aJeWqcqVK9OlSxcAli9fTo8ePbINO27ZsoXXX389WuVJykJUgo5BEJQDJgAVgL3AYKB/ancIdAP6Au8C+1Lb5gFdgK7RqFGSJEmSJEmSJEmSJElnjjp16nDfffcBKdcqx8XFMWbMGJYuXcqsWbPo1asXnTp1Ii4uLtt17rrrLu6++24Ali5dyiWXXMKTTz7J9OnTWbZsGfPnz2fs2LHcc889nHPOOdx3333pgnUHDx6kffv2JCQkULhwYcaNG0dsbGykv3nz5jz00EMATJgwgddee+1kfxUnpGPHjgAcOnSIG2+8kRdeeIFPP/2URYsWMWzYMC6//HJmz57NVVddlceV/sfAgQOpVasWkHJiZ506dRgyZAjz5s1j2bJlxMfH88orr9CmTRuqVq3K8OHD87hiSQWjtM/9QDlgP9AoDMOVQRBcSmrYMQzD0WkDgyCoBLwNXA0sDMPwkSjVKEmSJEmSJEmSJEmSpDPIiy++yIYNG/jf//1fvv7668hJf2mqV6/O+++/z/nnn5/tOiNHjqRixYoMHDiQbdu28fzzz/P8889nOrZYsWLExMRE/ty/f3+WLFkCwN///ncuvfTSDHOef/55pk+fzvLly/nzn/9M8+bNufDCC4/34+aKK664gmeeeYann36aHTt28Nhjj2UY06dPH2rVqsX8+fPzoMKMihcvzpw5c2jfvj1Tp05l1apV9O7dO8vxJUuWjGJ1kjITraurW5JySuOoMAxXZjcwDMNNwE3A90DfIAiuiUJ9kiRJkiRJkiRJkiRJOsMUKlSIDz74gDfffJMmTZpQqlQpihYtysUXX8zjjz/O0qVLqV69+lHXiYmJ4e9//zurVq2iT58+1K1blzJlyhATE0OJEiW49NJLad++PW+88QYbN26kSJEiAMybN48XXngBgOuvv54HHngg0/VjY2MZN24chQsXZu/evXTo0CHb65aj7amnnmLSpElcf/31lClThtjYWKpUqULbtm2ZNm0a//jHP/K6xAzKli3LlClTmDlzJl26dKFGjRoUL16cggULUrZsWa644gruu+8+Jk+ezPTp0/O6XOmMF4RhmPubBME2oAxwexiGH6a2XQJ8RUoAMjYMw+Qj5vQCXgHGh2F4R64XKem4BUFQBfj5WMe/+eabXHDBBblYkSRJkiRFx/PPP8+kSZNO2npdu3Zl5MiRJ209SZIkSdKp79tvvyUpKYmCBQtSo0aNvC5HkiSdoXLyb5J169ZRtWrVtD9WDcNw3YnWEa0THdPOb11zWFviYe9LZDJnSeqzYa5UJEmSJEmSJEmSJEmSJEmS8r1oBR33pD4LHta2/bD31TKZUzj1WSE3CpIkSZIkSZIkSZIkSZIkSflftIKO36U+z01rCMNwJ7Ap9Y/NM5lzZepzby7WJUmSJEmSJEmSJEmSJEmS8rGCRx9yUvwfUB+4Ahh/WPtUoDPQLwiCSWEYrgYIgqAB0A8IgcWu+zVFAAAgAElEQVRRqlGSJEmSJEmSJEmSJEk6JRw8eJBvvvkmR3OrV69OsWLFTnJFkpR7ohV0/AS4D2gLPHJY+4tAB1Kup/4qCILlQFGgJhBDStBxSJRqlCRJkiRJkiRJkiRJkk4J69evp3bt2jmaO3v2bJo1a3ZyC5KkXBStq6s/AcYCnwVBUD2tMQzDr4BeQDIpocv6wMWkhBwB+odhODVKNUqSJEmSJEmSJEmSJEmSpHwmKic6hmF4kJQrqjPrGxkEwbzU/ktTa/oWeDMMwyXRqE+SJEmSJEmSJEmSJEk6lVSrVo0wDPO6DEmKimhdXZ2tMAy/AR7L6zok5a4ffviB/fv353UZkiRJ0jErUqQIxYoVy+sylA8lJibmdQmSJEmSJEmSJJ0x8kXQUdKZ4emnn87rEiRJkiRJkiRJkiRJkiSdYgrkdQGSJEmSJEmSJEmSJEmSJElZicqJjkEQnJuDaSGQCPwahuGBk1ySJEmSJEmSJEmSJEmSJEk6BUTr6uofT2RyEATrgM+AMWEYTjk5JUmSJEmSJEmSJEmSJEmSpPwuWldXByf4qgrcDvwrCIJPgiAoE6W6JUmSJEmSJEmSJEmSJElSHorWiY5dUp89gYakXEn9CbAE2JraVx6IA1oAvwEWASOAkkAt4FbgLOA64CPg6ijVLkmSJEmSJEmSJEmSJEmS8khUgo5hGL4RBMFwoAEwAegRhuGWzMYGQVCBlIDjzcDKMAy7p7Y/AAwHOgGNgyC4MwzD96JRvyRJkiRJkiRJkiRJkiRJyhtRubo6CILWwL3AQqBtViFHgNS+NsBnQJcgCO5IbU8EugKfpw5tl6tFS5IkSZIkSZIkSZIkSZKkPBeVoCPwRyAEhoRhGB5tcOqYwUBASkAyrf0Q8Fpqe1zulCpJkiRJkiRJkiRJkiRJkvKLaAUdL0t9fnccc9LG1j6ifUXqs9wJVSRJkiRJkpRPNG7cOK9LkCRJkiRJkiQp3yoYpX1Kpj7LH8ectLEljmjfl/o8eEIVSZIkSZIk5bFy5coxc+ZM6tSpk9elSJIkSZIkSZKUb0XrRMefU5/tj2NOx9Tn2iPaK6Q+t55QRZIkSZIkSXnIkKMkSZIkSZIkSccmWkHHj4EA6BAEQe+jDQ6C4M+khCLD1LmHa5j6XHNSK5QkSZIkSYoSQ46SJEmSJCmnqlWrRhAEdO7cOUNffHw8QRAQBAHx8fFRr02SpNwSraurXwDuBsoBA4MgaA+MBZYCW1LHVADiSDnJsV5q29bUuYdrR0oAcnou1yxJkiRJknTSGXKUJEmSJJ1scXFxbNq0Ka/LyHWVKlViyZIlubZ+fHw8zZs3B+Dpp5+mf//+ubaXJEk6PlEJOoZh+EsQBL8HpgIVSQky1stmSgBsAm4Iw3B7pDEIzgcWpb4+yL2KJUmSJEmSTj5DjpIkSZKk3LBp0ybWr1+f12VIkiTlmmid6EgYhsuDILgYeAboBJTKYuivpJz22D8Mwx1HrPED0CVXC5UkSZIkScoFhhwlSZIkSbktKFCAkmeVzesyTrpd27YTHjqU12WcEpo1a0YYhnldhiRJJ13Ugo4AYRjuBP4UBEE/oD5QCyiT2r0DWAksCcNwfzTrkiRJkiRJyk2GHCVJkiRJ0VDyrLL8Zfo7eV3GSffs7+/i1y3b8roMSZKUh6IadEyTGmRckPqSJEmSJEk6bRlylCRJkiRJkiTpxBTI6wIkSZIkSZJOV4YcJUmSJEk6M+zdu5f33nuP7t27c/nll1OqVCkKFSpE+fLlufrqq/nHP/7Bnj17jmmtyZMn07JlS8qXL0/RokWpWbMmDz30EBs2bDjq3Pj4eIIgIAgC4uPjsx07ffp0OnToQPXq1SlSpAglS5akTp069OvXj40bN2Y7d8OGDTz66KPUq1ePUqVKERsbS6VKlahduzZ33XUXY8aMYdeuXZHxixcvjtT1ySefZLrmddddFxkzcODATMf07t2bIAioUKFClld0r1u3jscee4x69epRpkwZChcuzLnnnsudd97J7Nmzs/1cO3bsYPTo0XTo0IFLLrmE4sWLRz5bixYtGDFiBAcOHMhy/k8//RT5DGPGjAHgf//3f7nxxhs555xzKFiwIM2aNcu2BklS5vLkREdJZ6bRPf9OrZqX5nUZkiRJEpMWz6T/O4NO2noDBw7kjjvuOGnr6fRRokQJSpUqlddlSJIkSZKkXHbTTTcxZ86cDO3btm3j008/5dNPP2Xo0KFMnjyZiy66KMt1evfuzZAhQ9K1ffvttwwaNIhx48YxefLkE6517969dOzYkQ8//DBde2JiIitWrGDFihUMGzaMd955h1atWmWYP3fuXFq1apUuyAiwefNmNm/ezFdffcW7777LWWedFZlfr149SpYsya5du5g9ezYtWrRIN/fAgQMsXLgw8ufZs2fTp0+fDHunhTebNm1KEAQZ+keOHMkDDzzAvn370rX//PPP/Pzzz7z//vt069aN4cOHU7BgxshM3bp1WbNmTYb2zZs3M23aNKZNm8bw4cOZPHkylSpVyjDucGEY0qlTJ958881sx0mSjo1BR0lRU6HUWZx/9rl5XYYkSZJE2ZKlT+56ZctSpUqVk7qmJEmSJEmSTh1JSUnUrl2bW265hbi4OM455xzCMGTNmjV8+OGHvP/++/z444+0bt2aZcuWUbhw4QxrDBw4MBJyPOecc3jsscdo0KABiYmJTJo0icGDB3P77beTkJCQ4zqTk5O5+eabmT17NkEQ0K5dO9q2bUv16tU5ePAgixYtYuDAgaxdu5bbbruNBQsWUL9+/cj8/fv3065dO3bt2kWJEiXo1asXzZs3p0KFChw8eJA1a9awcOFCPvjgg3T7xsTEcNVVVzFlypRMT5pctGhRus81b948kpOTiYmJibTt2LGDL7/8EiDTUxFHjRpF9+7dAahVqxY9evSgbt26FC1alB9//JGRI0cyefJkRo4cSalSpTI9NTI5OZmGDRvSqlUr6tatS8WKFTlw4AA//vgjb731FlOnTuWLL76gXbt2Rz0xc/DgwaxYsYImTZrQq1cvatasyc6dO/npp5+ynSdJypxBR0mSJEmSJEmSJEmSpBMwevRoatSokaG9YcOG3HHHHXTr1o0WLVrwzTffMG7cOLp165Zu3ObNm3nqqacAOO+88/jss8/SnRjYtGlTWrRoQYsWLUhKSspxnYMHD2b27NkUKlSICRMm0LJly3T9v/vd7+jYsSNNmjRh5cqV9O7dm7lz50b658+fH7lC++23385w4mPa5x0wYECGQGazZs2YMmUKS5cuZc+ePRQvXjzSlxYa/P3vf8+8efP49ddf+eKLL4iLi4uMmTNnDocOHQLg6quvTrf2zz//zAMPPADA3Xffzeuvv57uxMa6devStm1bnnjiCf7rv/6LwYMH06NHD2rWrJlunVmzZmX6O1555ZW0b9+e0aNH07VrV+bMmcPMmTO59tprM/mWU6xYsYJOnToxZsyYTE+flCQdnwJ5XYAkSZIkSZIkSZIkSdKpLLNw3OGuu+46brnlFgA++uijDP1vvPFGJBg4cODATK9Fvuaaa7jnnntyXOPBgwcjpxjef//9GUKOacqUKcOAAQOAlJMVv/vuu0jfpk2bIu+bNm2a5V4FCxakZMmS6drSwolJSUnMmzcvXV/atd/XX389jRo1AshwYmLamHLlylGrVq10fUOGDCEhIYFzzjkny2upAZ555hkqV67MoUOHGDt2bIb+o/2OXbp0oW7dukDmv+PhSpcuzcsvv2zIUZJOEoOOkiRJkiRJkiRJkiRJJ9HWrVv59ttv+eqrryKv8uXLA7B8+fIM42fMmAGkhAxvvfXWLNft2rVrjmtatGgRGzduBOCOO+7IduzhIcaFCxdG3p999tmR96NHjz6u/evXrx85xfHwEOPBgwdZsGABkHLqY9q11EcGHdP+3LRp0wzhwQkTJgBw8803Z3oteJqCBQtGgpSHf67MhGHIpk2bWL16dbrf8ZxzzgEy/x0Pd/PNN1OiRIlsx0iSjp1XV0uSJEmSJEmSJEmSJJ2g+fPn89JLLzFjxgy2b9+e5bht27ZlaPvyyy+BlCuWszqNEODyyy8nNjaWAwcOHHd9S5YsibxPC/sdi8NPcWzcuDHnn38+P/zwA71792bcuHG0adOGq6++mri4OGJjY7Ncp2DBglx11VV88skn6UKMixYtIiEhgZIlS1K3bl327dsHwNy5c0lOTiYmJoadO3eyYsUKgEgQMs2vv/4aOXXy1Vdf5dVXXz3uz3W4SZMmMWzYMD799FN2796d5fzMfsfDXXbZZcdUhyTp2HiioyRJkiRJkiRJkiRJ0gno378/jRs35v3338825AhEgnyH27FjBwAVKlTIdm7BggUpW7ZsjmrcsmVLjualXakNUKhQISZOnMjFF18MwOLFi3n88ce56qqrKF26NC1btuTtt98mOTk507XSQopLly5lz549wH9OamzSpAkxMTE0bNiQIkWKsGvXLr744gsg5drqQ4cOAf+5Avtkfi5IOcGxe/futGrVikmTJmUbcoTMf8fDlSlTJkd1SZIy54mOkiRJkiRJkiRJkiRJOTRz5kyeeeYZAM4//3z69u1L48aNOffccylevDgxMTEAPPXUUzz77LPZrnXklcyZCcMwR3UeHj6Mj4+nXLlyxzTvyPDlJZdcwpdffsnEiROZOHEic+bM4fvvv2ffvn1MnTqVqVOn8uKLLzJ58uQMc9NCiklJScybN48bbriBOXPmAP8JQcbGxtKoUSNmzZpFfHw8cXFxkTFlypTJcFLi4Z+rd+/edOvW7Zg+15GnT44aNYqRI0cCKSdn9u7dm4YNG1K5cmWKFi0a+R07derEm2++edTfIW28JOnkMOgoSZIkSZIkSZIkSZKUQ6+99hoApUuXZuHChVmeyph2amNmypQpw6ZNm9i8eXO2eyUlJWW7TnYODzbGxsZSq1atHK0DKSG+1q1b07p1awA2btzIlClTGDp0KEuXLmXp0qX06NGDDz/8MN28K664gmLFirF3717i4+O59tprWbBgAZD+SupmzZpFgo59+/aNnPrYtGnTDGHQwz9XQkJCjj9X2u94wQUXsGDBAooUKZLpuJx+/5KkE+PV1ZIkSZIkSZIkSZIkSTm0cuVKAK655ppsr55esmRJln21a9cGYNmyZSQlJWU5bvny5Rw4cCBHddatWzfyftq0aTlaIytnn302Xbt2ZeHChdSrVw+Af/3rXxmudy5YsCBXXnklkHKq5OLFi9m7dy8lS5ZMV19a6HHu3Ln88ssvLF++PF374cqXL0/lypUBmDFjRo5PvEz7HW+99dYsQ45hGPL555/naH1J0okx6ChJkiRJkiRJkiRJkpRDacHEhISELMcsW7aMzz77LMv+6667DoDt27czceLELMeNGjUqh1VC48aNKVu2LADDhw9n165dOV4rK4UKFUp3PfXOnTszjEkLKy5dupR//etfADRp0iTdVc8NGzakSJEi7Nq1iyFDhnDo0CHgP1dfH+mWW24B4IcffmD8+PE5qv1YfsePP/6YDRs25Gh9SdKJMegoSZIkSZIkSZIkSZKUQzVq1ABg3rx5/PDDDxn6t27dSocOHbJd4+67746cIvjQQw9leoX1nDlzGDFiRI7rLFy4MH379gVg06ZNtGvXjr1792Y5fvfu3bz88svp2ubOnct3332X5ZwDBw4wZ84cAIoXL0758uUzjDk8CDl06FAg40mNsbGxNGrUCICXXnoJSLkavE6dOpnu+/DDD/Ob3/wGgJ49e2Z7eibA5MmTWbFiRbq2tN9x4sSJmV5P/f333/PHP/4x23UlSbmnYF4XIEmSJEmSJEmSJEmSlJ8sW7aMMWPGHHVc48aN6dSpExMnTmTPnj1cffXVPPLII9SvX58wDFmwYAEvvvgimzZtolGjRixcuDDTdSpWrMizzz5L3759+emnn6hfvz6PPfYYDRo0IDExkcmTJzNo0CAqV65MQkICW7duzdHn6tevHzNnzmTmzJlMmTKFSy65hJ49e9KoUSNKly7N7t27+eabb4iPj+ejjz6icOHC3H///ZH5M2fO5Nlnn6VJkybcdNNNXHbZZZQvX559+/axevVqhg8fHrnauXv37hQsmDGW0qBBA4oWLUpCQgK//vorkPmV1M2aNWPWrFmRMU2aNKFAgczP86pevTrDhw+nS5cubN++nauuuoqOHTvSqlUrzj33XJKSkli3bh2LFi1i/PjxfP/990ycOJHLLrssskanTp14+OGHWb9+PVdeeSX9+vXj0ksvJTExkVmzZjF48GD2799PvXr1vL5akvKAQUdJkiRJkiRJkiRJkk4Du7Zt59nf35XXZZx0u7Ztj/qeEyZMYMKECUcdN3r0aDp37kyXLl0YPXo069at44EHHkg3JiYmhkGDBrFjx44sg44Affr0Ye3atbz00kusX78+XcAQ4KyzzmL8+PHcfvvtOftQqbVMnDiRnj17MnbsWNauXcvjjz+e5fgKFSpkaDt06BBz5syJnNyYmbZt2/K3v/0t075ChQrRqFEjZs6cCUCpUqWoW7duhnFHhh8zC0MernPnzhQpUoR7772XXbt2MXLkSEaOHJnp2AIFClCsWLF0bX/605+YPn0606ZN4+uvv6Zr167p+osUKcLYsWOZNGmSQUdJygMGHSVJkiRJkiRJkiRJOg2Ehw7x65ZteV3GGWnUqFFcc801jBgxgmXLlnHgwAEqVapE06ZNuf/++2nQoAH9+/c/6jpDhgyhRYsWvPTSSyxevJiEhASqVKnCjTfeyMMPP0yVKlVOuNYiRYrwxhtv8OCDDzJy5Eg+/fRT1q1bx969eylevDjVqlWjfv36t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\n","text/plain":["<Figure size 3000x1400 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"**Explanation**\n1. Supplement for the second graph","metadata":{"hidden":true}},{"cell_type":"markdown","source":"## Take a closer look at each index_group","metadata":{"hidden":true}},{"cell_type":"code","source":"df.groupby(['index_group_name', 'index_name']).count()['article_id']","metadata":{"hidden":true,"scrolled":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":["index_group_name  index_name                    \n","Ladieswear        Baby Sizes 50-98                      0\n","                  Children Accessories, Swimwear        0\n","                  Children Sizes 134-170                0\n","                  Children Sizes 92-140                 0\n","                  Divided                               0\n","                  Ladies Accessories                 6961\n","                  Ladieswear                        26001\n","                  Lingeries/Tights                   6775\n","                  Menswear                              0\n","                  Sport                                 0\n","Baby/Children     Baby Sizes 50-98                   8875\n","                  Children Accessories, Swimwear     4615\n","                  Children Sizes 134-170             9214\n","                  Children Sizes 92-140             12007\n","                  Divided                               0\n","                  Ladies Accessories                    0\n","                  Ladieswear                            0\n","                  Lingeries/Tights                      0\n","                  Menswear                              0\n","                  Sport                                 0\n","Divided           Baby Sizes 50-98                      0\n","                  Children Accessories, Swimwear        0\n","                  Children Sizes 134-170                0\n","                  Children Sizes 92-140                 0\n","                  Divided                           15149\n","                  Ladies Accessories                    0\n","                  Ladieswear                            0\n","                  Lingeries/Tights                      0\n","                  Menswear                              0\n","                  Sport                                 0\n","Menswear          Baby Sizes 50-98                      0\n","                  Children Accessories, Swimwear        0\n","                  Children Sizes 134-170                0\n","                  Children Sizes 92-140                 0\n","                  Divided                               0\n","                  Ladies Accessories                    0\n","                  Ladieswear                            0\n","                  Lingeries/Tights                      0\n","                  Menswear                          12553\n","                  Sport                                 0\n","Sport             Baby Sizes 50-98                      0\n","                  Children Accessories, Swimwear        0\n","                  Children Sizes 134-170                0\n","                  Children Sizes 92-140                 0\n","                  Divided                               0\n","                  Ladies Accessories                    0\n","                  Ladieswear                            0\n","                  Lingeries/Tights                      0\n","                  Menswear                              0\n","                  Sport                              3392\n","Name: article_id, dtype: int64"]},"metadata":{}}]},{"cell_type":"code","source":"articles.groupby(['product_group_name', 'product_type_name']).count()['article_id']","metadata":{"hidden":true,"scrolled":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":["product_group_name   product_type_name\n","Accessories          Accessories set         7\n","                     Alice band              6\n","                     Baby Bib                3\n","                     Bag                  1280\n","                     Beanie                 56\n","                                          ... \n","Underwear            Underwear corset        7\n","                     Underwear set          47\n","Underwear/nightwear  Sleep Bag               6\n","                     Sleeping sack          48\n","Unknown              Unknown               121\n","Name: article_id, Length: 132, dtype: int64"]},"metadata":{}}]},{"cell_type":"markdown","source":"# Data Cleaning","metadata":{"heading_collapsed":true}},{"cell_type":"markdown","source":"After group discussion, we decide to use the following features from the article.csv","metadata":{"hidden":true}},{"cell_type":"code","source":"articles.columns","metadata":{"hidden":true,"scrolled":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":["Index(['article_id', 'product_code', 'prod_name', 'product_type_no',\n","       'product_type_name', 'product_group_name', 'graphical_appearance_no',\n","       'graphical_appearance_name', 'colour_group_code', 'colour_group_name',\n","       'perceived_colour_value_id', 'perceived_colour_value_name',\n","       'perceived_colour_master_id', 'perceived_colour_master_name',\n","       'department_no', 'department_name', 'index_code', 'index_name',\n","       'index_group_no', 'index_group_name', 'section_no', 'section_name',\n","       'garment_group_no', 'garment_group_name', 'detail_desc'],\n","      dtype='object')"]},"metadata":{}}]},{"cell_type":"code","source":"cleaned_df = articles[['article_id', 'product_type_name','index_group_name',\n               'graphical_appearance_name', \n              'perceived_colour_master_name','department_name']]","metadata":{"hidden":true},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":"We beleiever **gender** is an important feature to have, but since there is no such feature in all three datasets, we will create one based on **index_group_name**","metadata":{"hidden":true}},{"cell_type":"code","source":"# mapped index_group_name to a new column named gender\nmp = {'Ladieswear':1, 'Baby/Children':0.5, 'Menswear':0, 'Sport':0.5, 'Divided':0.5}\ncleaned_df['gender'] = cleaned_df['index_group_name'].map(mp)\ncleaned_df.head(20)","metadata":{"hidden":true},"execution_count":120,"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>article_id</th>\n","      <th>product_type_name</th>\n","      <th>index_group_name</th>\n","      <th>graphical_appearance_name</th>\n","      <th>perceived_colour_master_name</th>\n","      <th>department_name</th>\n","      <th>gender</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>108775015</td>\n","      <td>Vest top</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Black</td>\n","      <td>Jersey Basic</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>108775044</td>\n","      <td>Vest top</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>White</td>\n","      <td>Jersey Basic</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>108775051</td>\n","      <td>Vest top</td>\n","      <td>Ladieswear</td>\n","      <td>Stripe</td>\n","      <td>White</td>\n","      <td>Jersey Basic</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>110065001</td>\n","      <td>Bra</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Black</td>\n","      <td>Clean Lingerie</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>110065002</td>\n","      <td>Bra</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>White</td>\n","      <td>Clean Lingerie</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>110065011</td>\n","      <td>Bra</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Beige</td>\n","      <td>Clean Lingerie</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>111565001</td>\n","      <td>Underwear Tights</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Black</td>\n","      <td>Tights basic</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>111565003</td>\n","      <td>Socks</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Beige</td>\n","      <td>Tights basic</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>111586001</td>\n","      <td>Leggings/Tights</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Black</td>\n","      <td>Tights basic</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>111593001</td>\n","      <td>Underwear Tights</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Black</td>\n","      <td>Tights basic</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>111609001</td>\n","      <td>Underwear Tights</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Black</td>\n","      <td>Tights basic</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>112679048</td>\n","      <td>Sweater</td>\n","      <td>Baby/Children</td>\n","      <td>All over pattern</td>\n","      <td>Grey</td>\n","      <td>Baby basics</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>112679052</td>\n","      <td>Sweater</td>\n","      <td>Baby/Children</td>\n","      <td>All over pattern</td>\n","      <td>Blue</td>\n","      <td>Baby basics</td>\n","      <td>0.5</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>114428026</td>\n","      <td>Bra</td>\n","      <td>Ladieswear</td>\n","      <td>Stripe</td>\n","      <td>White</td>\n","      <td>Casual Lingerie</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>114428030</td>\n","      <td>Bra</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Grey</td>\n","      <td>Casual Lingerie</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>116379047</td>\n","      <td>Top</td>\n","      <td>Ladieswear</td>\n","      <td>Solid</td>\n","      <td>Blue</td>\n","      <td>Jersey Basic</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>118458003</td>\n","      <td>Trousers</td>\n","      <td>Menswear</td>\n","      <td>Melange</td>\n","      <td>Grey</td>\n","      <td>Jersey Basic</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>118458004</td>\n","      <td>Trousers</td>\n","      <td>Menswear</td>\n","      <td>Melange</td>\n","      <td>Grey</td>\n","      <td>Jersey Basic</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>118458028</td>\n","      <td>Trousers</td>\n","      <td>Menswear</td>\n","      <td>Solid</td>\n","      <td>Black</td>\n","      <td>Jersey Basic</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>118458029</td>\n","      <td>Trousers</td>\n","      <td>Menswear</td>\n","      <td>Melange</td>\n","      <td>Grey</td>\n","      <td>Jersey Basic</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    article_id product_type_name index_group_name graphical_appearance_name  \\\n","0    108775015          Vest top       Ladieswear                     Solid   \n","1    108775044          Vest top       Ladieswear                     Solid   \n","2    108775051          Vest top       Ladieswear                    Stripe   \n","3    110065001               Bra       Ladieswear                     Solid   \n","4    110065002               Bra       Ladieswear                     Solid   \n","5    110065011               Bra       Ladieswear                     Solid   \n","6    111565001  Underwear Tights       Ladieswear                     Solid   \n","7    111565003             Socks       Ladieswear                     Solid   \n","8    111586001   Leggings/Tights       Ladieswear                     Solid   \n","9    111593001  Underwear Tights       Ladieswear                     Solid   \n","10   111609001  Underwear Tights       Ladieswear                     Solid   \n","11   112679048           Sweater    Baby/Children          All over pattern   \n","12   112679052           Sweater    Baby/Children          All over pattern   \n","13   114428026               Bra       Ladieswear                    Stripe   \n","14   114428030               Bra       Ladieswear                     Solid   \n","15   116379047               Top       Ladieswear                     Solid   \n","16   118458003          Trousers         Menswear                   Melange   \n","17   118458004          Trousers         Menswear                   Melange   \n","18   118458028          Trousers         Menswear                     Solid   \n","19   118458029          Trousers         Menswear                   Melange   \n","\n","   perceived_colour_master_name  department_name  gender  \n","0                         Black     Jersey Basic     1.0  \n","1                         White     Jersey Basic     1.0  \n","2                         White     Jersey Basic     1.0  \n","3                         Black   Clean Lingerie     1.0  \n","4                         White   Clean Lingerie     1.0  \n","5                         Beige   Clean Lingerie     1.0  \n","6                         Black     Tights basic     1.0  \n","7                         Beige     Tights basic     1.0  \n","8                         Black     Tights basic     1.0  \n","9                         Black     Tights basic     1.0  \n","10                        Black     Tights basic     1.0  \n","11                         Grey      Baby basics     0.5  \n","12                         Blue      Baby basics     0.5  \n","13                        White  Casual Lingerie     1.0  \n","14                         Grey  Casual Lingerie     1.0  \n","15                         Blue     Jersey Basic     1.0  \n","16                         Grey     Jersey Basic     0.0  \n","17                         Grey     Jersey Basic     0.0  \n","18                        Black     Jersey Basic     0.0  \n","19                         Grey     Jersey Basic     0.0  "]},"metadata":{}}]},{"cell_type":"code","source":"cleaned_df['article_id'] = cleaned_df['article_id'].astype('int')\nmerge_df = transactions.merge(cleaned_df,on='article_id',how='left')\ng = merge_df.groupby('customer_id').gender.mean().reset_index()\ng.head()\n\nfemale = g.loc[g.gender>0.5].customer_id.values\nmale = g.loc[g.gender<0.5].customer_id.values","metadata":{"hidden":true},"execution_count":115,"outputs":[]},{"cell_type":"code","source":"f, m = len(female), len(male)\nprint(f'% of female {round(f*100/(f+m),2)}')\nprint(f'% of female {round(m*100/(f+m),2)}')     ","metadata":{"hidden":true,"scrolled":true},"execution_count":116,"outputs":[{"name":"stdout","output_type":"stream","text":"% of female 94.77\n\n% of female 5.23\n"}]},{"cell_type":"code","source":"#g.to_csv(r'/Users/jerrywang/A_Desktop/AML/AML Group Project/customer_gender_mapping.csv', index=False)","metadata":{"hidden":true},"execution_count":119,"outputs":[]},{"cell_type":"code","source":"len(female)","metadata":{"hidden":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":["1159145"]},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{"hidden":true},"execution_count":68,"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":["63934"]},"metadata":{}}]},{"cell_type":"code","source":"df = pd.get_dummies(raw_df, columns=['product_type_name', 'graphical_appearance_name', \n              'perceived_colour_master_name','department_name'])","metadata":{"hidden":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"len(df.columns)","metadata":{"hidden":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":["432"]},"metadata":{}}]},{"cell_type":"code","source":"df.head()","metadata":{"hidden":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>article_id</th>\n","      <th>product_type_name_Accessories set</th>\n","      <th>product_type_name_Alice band</th>\n","      <th>product_type_name_Baby Bib</th>\n","      <th>product_type_name_Backpack</th>\n","      <th>product_type_name_Bag</th>\n","      <th>product_type_name_Ballerinas</th>\n","      <th>product_type_name_Beanie</th>\n","      <th>product_type_name_Belt</th>\n","      <th>product_type_name_Bikini top</th>\n","      <th>...</th>\n","      <th>department_name_Young Girl Jersey Basic</th>\n","      <th>department_name_Young Girl Jersey Fancy</th>\n","      <th>department_name_Young Girl Knitwear</th>\n","      <th>department_name_Young Girl Outdoor</th>\n","      <th>department_name_Young Girl S&amp;T</th>\n","      <th>department_name_Young Girl Shoes</th>\n","      <th>department_name_Young Girl Swimwear</th>\n","      <th>department_name_Young Girl Trouser</th>\n","      <th>department_name_Young Girl UW/NW</th>\n","      <th>department_name_Young boy Swimwear</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0108775015</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0108775044</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0108775051</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0110065001</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0110065002</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 432 columns</p>\n","</div>"],"text/plain":["   article_id  product_type_name_Accessories set  \\\n","0  0108775015                                  0   \n","1  0108775044                                  0   \n","2  0108775051                                  0   \n","3  0110065001                                  0   \n","4  0110065002                                  0   \n","\n","   product_type_name_Alice band  product_type_name_Baby Bib  \\\n","0                             0                           0   \n","1                             0                           0   \n","2                             0                           0   \n","3                             0                           0   \n","4                             0                           0   \n","\n","   product_type_name_Backpack  product_type_name_Bag  \\\n","0                           0                      0   \n","1                           0                      0   \n","2                           0                      0   \n","3                           0                      0   \n","4                           0                      0   \n","\n","   product_type_name_Ballerinas  product_type_name_Beanie  \\\n","0                             0                         0   \n","1                             0                         0   \n","2                             0                         0   \n","3                             0                         0   \n","4                             0                         0   \n","\n","   product_type_name_Belt  product_type_name_Bikini top  ...  \\\n","0                       0                             0  ...   \n","1                       0                             0  ...   \n","2                       0                             0  ...   \n","3                       0                             0  ...   \n","4                       0                             0  ...   \n","\n","   department_name_Young Girl Jersey Basic  \\\n","0                                        0   \n","1                                        0   \n","2                                        0   \n","3                                        0   \n","4                                        0   \n","\n","   department_name_Young Girl Jersey Fancy  \\\n","0                                        0   \n","1                                        0   \n","2                                        0   \n","3                                        0   \n","4                                        0   \n","\n","   department_name_Young Girl Knitwear  department_name_Young Girl Outdoor  \\\n","0                                    0                                   0   \n","1                                    0                                   0   \n","2                                    0                                   0   \n","3                                    0                                   0   \n","4                                    0                                   0   \n","\n","   department_name_Young Girl S&T  department_name_Young Girl Shoes  \\\n","0                               0                                 0   \n","1                               0                                 0   \n","2                               0                                 0   \n","3                               0                                 0   \n","4                               0                                 0   \n","\n","   department_name_Young Girl Swimwear  department_name_Young Girl Trouser  \\\n","0                                    0                                   0   \n","1                                    0                                   0   \n","2                                    0                                   0   \n","3                                    0                                   0   \n","4                                    0                                   0   \n","\n","   department_name_Young Girl UW/NW  department_name_Young boy Swimwear  \n","0                                 0                                   0  \n","1                                 0                                   0  \n","2                                 0                                   0  \n","3                                 0                                   0  \n","4                                 0                                   0  \n","\n","[5 rows x 432 columns]"]},"metadata":{}}]},{"cell_type":"code","source":"# df.to_csv(r'/Users/jerrywang/A_Desktop/AML/AML Group Project/article_v1.csv', index=False)","metadata":{"hidden":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = df.drop('article_id',axis=1)\nfig, ax = plt.subplots(dpi=200)\nsparsity = pd.Series(np.count_nonzero(X, axis=0)*100 / X.shape[0], index = X.columns)\nsparse_filtered = sparsity.sort_values(ascending= False)[:10]\nax = sparse_filtered.plot.barh(title=\"Top 10 populated article features\")\nax.set_xlabel('% populated');","metadata":{"hidden":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1200x800 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Data Sampling","metadata":{"heading_collapsed":true}},{"cell_type":"markdown","source":"**We are creating 1% samples of the customers dataset. Keeping the related articles and customers.**","metadata":{"hidden":true}},{"cell_type":"code","source":"articles = pd.read_csv('articles.csv',dtype={'article_id': str})\ntransactions = pd.read_csv('transactions_train.csv',dtype={'article_id': str})\ncustomers = pd.read_csv(\"customers.csv\")","metadata":{"hidden":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def data_sampler(customers,transactions,articles,frac):\n    '''Input three datasets and fraction of df1.\n       Keeping the related articles and customers.\n       Reference: https://www.kaggle.com/code/paweljankiewicz/hm-create-dataset-samples/notebook'''\n    customers_sample = customers.sample(frac=frac, replace=False, random_state=44)\n    customers_sample_ids = set(customers_sample[\"customer_id\"])\n    transactions_sample = transactions[transactions[\"customer_id\"].isin(customers_sample_ids)]\n    articles_sample_ids = set(transactions_sample[\"article_id\"])\n    articles_sample = articles[articles[\"article_id\"].isin(articles_sample_ids)]\n    \n    print('customers_sample shape:', customers_sample.shape)\n    print('transactions_sample shape:', transactions_sample.shape)\n    print('articles_sample shape:', articles_sample.shape)\n    \n    return customers_sample, transactions_sample, articles_sample\n\n#     customers_sample.to_csv(f\"customers_sample.csv\", index=False)\n#     transactions_sample.to_csv(f\"transactions_sample.csv\", index=False)\n#     articles_sample.to_csv(f\"articles_sample.csv\", index=False)\n    \ncustomers_sample, transactions_sample, articles_sample = data_sampler(customers,transactions,articles,frac=0.01)","metadata":{"hidden":true},"execution_count":16,"outputs":[{"name":"stdout","output_type":"stream","text":"customers_sample shape: (13720, 7)\n\ntransactions_sample shape: (316443, 5)\n\narticles_sample shape: (51894, 25)\n"}]},{"cell_type":"markdown","source":"# Eveluation metric","metadata":{"heading_collapsed":true}},{"cell_type":"markdown","source":"Mean Average Precision at K (popularly called the MAP@K) metric is commonly used for evaluating recommender systems and other ranking related problems. For this kaggle competition, the host use MAP@12. Click [here](https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/overview/evaluation) to see formula explaination\n","metadata":{"hidden":true}},{"cell_type":"markdown","source":"$$M A P @ 12=\\frac{1}{U} \\sum_{u=1}^U \\frac{1}{\\min (m, 12)} \\sum_{k=1}^{\\min (n, 12)} P(k) \\times \\operatorname{rel}(k)$$","metadata":{"hidden":true}},{"cell_type":"markdown","source":"# Content-Based Filtering","metadata":{}},{"cell_type":"markdown","source":"## **Feature selelction and preprocession**","metadata":{}},{"cell_type":"code","source":"# Join transaction and article dataframe\ndf = transactions_sample.merge(articles_sample, on='article_id')\n\n# Feature selection\ndf = df[['t_dat', 'customer_id', 'article_id', 'prod_name', 'product_type_name',\n       'product_group_name', \n       'graphical_appearance_name', 'colour_group_name',\n       'perceived_colour_value_name',\n       'perceived_colour_master_name',\n       'department_name', 'index_name',\n       'index_group_name', 'section_name',\n       'garment_group_name', 'detail_desc']]\nfeature_subset = ['product_group_name', \n       'graphical_appearance_name', 'colour_group_name',\n       'perceived_colour_value_name',\n       'perceived_colour_master_name',\n       'department_name', 'index_name',\n       'index_group_name', 'section_name',\n       'garment_group_name']\n\n# One hot encoding\nfeatures = feature_subset\ndf1 = df[['customer_id', 'article_id'] + features]\ndummies_df = pd.get_dummies(df1, columns=features)\ndummies_df","metadata":{},"execution_count":193,"outputs":[{"execution_count":193,"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" 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Nightwear</th>\n","      <th>garment_group_name_Unknown</th>\n","      <th>garment_group_name_Woven/Jersey/Knitted mix Baby</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00402f4463c8dc1b3ee54abfdea280e96cd87320449eca...</td>\n","      <td>0507909001</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9b28cfa53d4adff4836e9f03499683a3c69e5081eedd8f...</td>\n","      <td>0507909001</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      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fc03d3c84fd371a67c22fc2cb86692210187e15aada320...  0853570002   \n","316440  fc03d3c84fd371a67c22fc2cb86692210187e15aada320...  0908356001   \n","316441  fdcd7d55c497c620e7ffa7c557ec58da969cac10d3df36...  0904026001   \n","316442  fdf6647f3c5d53dadaa182b79fc480c22e9a1bf7a5fcdd...  0916256003   \n","\n","        product_group_name_Accessories  product_group_name_Bags  \\\n","0                                    0                        0   \n","1                                    0                        0   \n","2                                    0                        0   \n","3                                    0                        0   \n","4                                    0                        0   \n","...                                ...                      ...   \n","316438                               0                        0   \n","316439                               0                        0   \n","316440                               0                        0   \n","316441                               0                        0   \n","316442                               0                        0   \n","\n","        product_group_name_Cosmetic  product_group_name_Furniture  \\\n","0                                 0                             0   \n","1                                 0                             0   \n","2                                 0                             0   \n","3                                 0                             0   \n","4                                 0                             0   \n","...                             ...                           ...   \n","316438                            0                             0   \n","316439                            0                             0   \n","316440                            0                             0   \n","316441                            0                             0   \n","316442                            0                             0   \n","\n","        product_group_name_Garment Full body  \\\n","0                                          0   \n","1                                          0   \n","2                                          0   \n","3                                          0   \n","4                                          0   \n","...                                      ...   \n","316438                                     0   \n","316439                                     0   \n","316440                                     0   \n","316441                                     0   \n","316442                                     0   \n","\n","        product_group_name_Garment Lower body  \\\n","0                                           0   \n","1                                           0   \n","2                                           0   \n","3                                           0   \n","4                                           0   \n","...                                       ...   \n","316438                                      0   \n","316439                                      0   \n","316440                                      0   \n","316441                                      0   \n","316442                                      0   \n","\n","        product_group_name_Garment Upper body  \\\n","0                                           1   \n","1                                           1   \n","2                                           1   \n","3                                           1   \n","4                                           1   \n","...                                       ...   \n","316438                                      1   \n","316439                                      1   \n","316440                                      1   \n","316441                                      1   \n","316442                                      0   \n","\n","        product_group_name_Garment and Shoe care  ...  \\\n","0                                              0  ...   \n","1                                              0  ...   \n","2                                              0  ...   \n","3                                              0  ...   \n","4                                              0  ...   \n","...                                          ...  ...   \n","316438                                         0  ...   \n","316439                                         0  ...   \n","316440                                         0  ...   \n","316441                                         0  ...   \n","316442                                         0  ...   \n","\n","        garment_group_name_Shorts  garment_group_name_Skirts  \\\n","0                               0                          0   \n","1                               0                          0   \n","2                               0                          0   \n","3                               0                          0   \n","4                               0                          0   \n","...                           ...                        ...   \n","316438                          0                          0   \n","316439                          0                          0   \n","316440                          0                          0   \n","316441                          0                          0   \n","316442                          0                          0   \n","\n","        garment_group_name_Socks and Tights  \\\n","0                                         0   \n","1                                         0   \n","2                                         0   \n","3                                         0   \n","4                                         0   \n","...                                     ...   \n","316438                                    0   \n","316439                                    0   \n","316440                                    0   \n","316441                                    0   \n","316442                                    0   \n","\n","        garment_group_name_Special Offers  garment_group_name_Swimwear  \\\n","0                                       0                            0   \n","1                                       0                            0   \n","2                                       0                            0   \n","3                                       0                            0   \n","4                                       0                            0   \n","...                                   ...                          ...   \n","316438                                  0                            0   \n","316439                                  0                            0   \n","316440                                  0                            0   \n","316441                                  0                            0   \n","316442                                  0                            0   \n","\n","        garment_group_name_Trousers  garment_group_name_Trousers Denim  \\\n","0                                 0                                  0   \n","1                                 0                                  0   \n","2                                 0                                  0   \n","3                                 0                                  0   \n","4                                 0                                  0   \n","...                             ...                                ...   \n","316438                            0                                  0   \n","316439                            0                                  0   \n","316440                            0                                  0   \n","316441                            0                                  0   \n","316442                            0                                  0   \n","\n","        garment_group_name_Under-, Nightwear  garment_group_name_Unknown  \\\n","0                                          0                           0   \n","1                                          0                           0   \n","2                                          0                           0   \n","3                                          0                           0   \n","4                                          0                           0   \n","...                                      ...                         ...   \n","316438                                     0                           0   \n","316439                                     0                           0   \n","316440                                     0                           1   \n","316441                                     0                           0   \n","316442                                     0                           0   \n","\n","        garment_group_name_Woven/Jersey/Knitted mix Baby  \n","0                                                      0  \n","1                                                      0  \n","2                                                      0  \n","3                                                      0  \n","4                                                      0  \n","...                                                  ...  \n","316438                                                 0  \n","316439                                                 0  \n","316440                                                 0  \n","316441                                                 0  \n","316442                                                 0  \n","\n","[316443 rows x 461 columns]"]},"metadata":{}}]},{"cell_type":"code","source":"# select users who bought at least 2 items and preprocessing\nminimum_items = 2\ngroupby_customer = dummies_df.groupby('customer_id')\n\nfeature_score = []  \ncutomer_ids = []\narticle_ids = []\nfor key in groupby_customer.groups.keys():\n    temp = groupby_customer.get_group(key) # get sub_dataframe for each customer transactions\n    if temp.article_id.nunique() >= minimum_items:\n        # feature_score is the sum of # of transaction for each feature\n        feature_score.append(temp.drop('article_id', axis=1).sum(numeric_only=True).values)\n        cutomer_ids.append(key)\n        article_ids.extend(temp.article_id.values.tolist())","metadata":{},"execution_count":194,"outputs":[]},{"cell_type":"markdown","source":"## **Create user feature matrix**","metadata":{}},{"cell_type":"markdown","source":"Check [here](https://www.youtube.com/watch?v=C7m2IWUvkys&list=PLTjbQZu8nnshv8H5tV0fGh0ECBIXwuHJb&index=9) for user feature matrix explaination","metadata":{}},{"cell_type":"code","source":"user_feature = pd.DataFrame(feature_score, columns = dummies_df.columns[2:])\nuser_sum_socre = user_feature.sum(axis=1)\nnormalized_user_feature = user_feature.div(user_sum_socre, axis=0)\nnormalized_user_feature.insert(0, 'customer_id', cutomer_ids)\nnormalized_user_feature = normalized_user_feature.set_index('customer_id')\nnormalized_user_feature","metadata":{},"execution_count":195,"outputs":[{"execution_count":195,"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>product_group_name_Accessories</th>\n","      <th>product_group_name_Bags</th>\n","      <th>product_group_name_Cosmetic</th>\n","      <th>product_group_name_Furniture</th>\n","      <th>product_group_name_Garment Full body</th>\n","      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              0.000000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...                   0.000000   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...                   0.000000   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...                   0.000000   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...                   0.000000   \n","...                                                                       ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...                   0.000000   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...                   0.000000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...                   0.000000   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...                   0.000000   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...                   0.006984   \n","\n","                                                    garment_group_name_Socks and Tights  \\\n","customer_id                                                                               \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...                             0.000000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...                             0.004348   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...                             0.000000   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...                             0.000000   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...                             0.000000   \n","...                                                                                 ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...                             0.000000   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...                             0.000000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...                             0.000000   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...                             0.000000   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...                             0.000952   \n","\n","                                                    garment_group_name_Special Offers  \\\n","customer_id                                                                             \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...                           0.000000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...                           0.000000   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...                           0.000000   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...                           0.000000   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...                           0.000000   \n","...                                                                               ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...                           0.000000   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                                                                   ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...                     0.000000   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...                     0.000000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...                     0.086364   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...                     0.011765   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...                     0.005079   \n","\n","                                                    garment_group_name_Trousers  \\\n","customer_id                                                                       \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...                     0.000000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...                     0.000000   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...                     0.017143   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garment_group_name_Under-, Nightwear  \\\n","customer_id                                                                                \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...                              0.000000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...                              0.017391   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...                              0.000952   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...                              0.009756   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...                              0.000000   \n","...                                                                                  ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...                              0.045455   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...                              0.000000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...                              0.004545   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...                              0.000000   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...                              0.002222   \n","\n","                                                    garment_group_name_Unknown  \\\n","customer_id                                                                      \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...                    0.000000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...                    0.000000   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...                    0.002857   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...                    0.007317   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...                    0.000000   \n","...                                                                        ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...                    0.000000   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...                    0.050000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...                    0.000000   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...                    0.000000   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...                    0.000317   \n","\n","                                                    garment_group_name_Woven/Jersey/Knitted mix Baby  \n","customer_id                                                                                           \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...                                          0.000000  \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...                                          0.000000  \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...                                          0.000000  \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...                                          0.000000  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the items been bought by customers\nbought_item = unique_items.article_id.isin(article_ids)\n\nitem_feature = unique_items[bought_item].drop('customer_id', axis=1)\n\nitem_feature = item_feature.set_index('article_id')\nitem_feature","metadata":{"scrolled":true},"execution_count":159,"outputs":[{"execution_count":159,"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>product_group_name_Accessories</th>\n","      <th>product_group_name_Bags</th>\n","      <th>product_group_name_Cosmetic</th>\n","      <th>product_group_name_Furniture</th>\n","      <th>product_group_name_Garment Full body</th>\n","      <th>product_group_name_Garment Lower body</th>\n","      <th>product_group_name_Garment Upper body</th>\n","      <th>product_group_name_Garment and Shoe care</th>\n","      <th>product_group_name_Interior textile</th>\n","      <th>product_group_name_Items</th>\n","      <th>...</th>\n","      <th>garment_group_name_Shorts</th>\n","      <th>garment_group_name_Skirts</th>\n","      <th>garment_group_name_Socks and Tights</th>\n","      <th>garment_group_name_Special Offers</th>\n","      <th>garment_group_name_Swimwear</th>\n","      <th>garment_group_name_Trousers</th>\n","      <th>garment_group_name_Trousers Denim</th>\n","      <th>garment_group_name_Under-, Nightwear</th>\n","      <th>garment_group_name_Unknown</th>\n","      <th>garment_group_name_Woven/Jersey/Knitted mix Baby</th>\n","    </tr>\n","    <tr>\n","      <th>article_id</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","   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columns</p>\n","</div>"],"text/plain":["            product_group_name_Accessories  product_group_name_Bags  \\\n","article_id                                                            \n","0507909001                               0                        0   \n","0665481004                               0                        0   \n","0487052007                               0                        0   \n","0578589001                               0                        0   \n","0567745004                               0                        0   \n","...                                    ...                      ...   \n","0793104018                               0                        0   \n","0853570002                               0                        0   \n","0908356001                               0                        0   \n","0904026001                               0                        0   \n","0916256003                               0                  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                                    0   \n","0578589001                                      1   \n","0567745004                                      0   \n","...                                           ...   \n","0793104018                                      0   \n","0853570002                                      0   \n","0908356001                                      0   \n","0904026001                                      0   \n","0916256003                                      0   \n","\n","            product_group_name_Garment Upper body  \\\n","article_id                                          \n","0507909001                                      1   \n","0665481004                                      0   \n","0487052007                                      0   \n","0578589001                                      0   \n","0567745004                                      1   \n","...                                           ...   \n","0793104018                                      1   \n","0853570002                                      1   \n","0908356001                                      1   \n","0904026001                                      1   \n","0916256003                                      0   \n","\n","            product_group_name_Garment and Shoe care  \\\n","article_id                                             \n","0507909001                                         0   \n","0665481004                                         0   \n","0487052007                                         0   \n","0578589001                                         0   \n","0567745004                                         0   \n","...                                              ...   \n","0793104018                                         0   \n","0853570002                                         0   \n","0908356001                                         0   \n","0904026001                                         0   \n","0916256003                  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0   \n","0916256003                                    0                         0   \n","\n","            ...  garment_group_name_Shorts  garment_group_name_Skirts  \\\n","article_id  ...                                                         \n","0507909001  ...                          0                          0   \n","0665481004  ...                          0                          0   \n","0487052007  ...                          0                          0   \n","0578589001  ...                          0                          0   \n","0567745004  ...                          0                          0   \n","...         ...                        ...                        ...   \n","0793104018  ...                          0                          0   \n","0853570002  ...                          0                          0   \n","0908356001  ...                          0                          0   \n","0904026001  ...                          0                          0   \n","0916256003  ...                          0                          0   \n","\n","            garment_group_name_Socks and Tights  \\\n","article_id                                        \n","0507909001                                    0   \n","0665481004                                    0   \n","0487052007                                    1   \n","0578589001                                    0   \n","0567745004                                    0   \n","...                                         ...   \n","0793104018                                    0   \n","0853570002                                    0   \n","0908356001                                    0   \n","0904026001                                    0   \n","0916256003                                    0   \n","\n","            garment_group_name_Special Offers  garment_group_name_Swimwear  \\\n","article_id                                                                   \n","0507909001                                  0                            0   \n","0665481004                                  0                            0   \n","0487052007                                  0                            0   \n","0578589001                                  0                            0   \n","0567745004                                  0                            0   \n","...                                       ...                          ...   \n","0793104018                                  0                            0   \n","0853570002                                  0                            0   \n","0908356001                                  0                            0   \n","0904026001                                  0                            0   \n","0916256003                                  0                            0   \n","\n","            garment_group_name_Trousers  garment_group_name_Trousers Denim  \\\n","article_id                                                                   \n","0507909001                            0                                  0   \n","0665481004                            0                                  0   \n","0487052007                            0                                  0   \n","0578589001                            1                                  0   \n","0567745004                            0                                  0   \n","...                                 ...                                ...   \n","0793104018                            0                                  0   \n","0853570002                            0                                  0   \n","0908356001                            0                                  0   \n","0904026001                            0                                  0   \n","0916256003                            0                                  0   \n","\n","            garment_group_name_Under-, Nightwear  garment_group_name_Unknown  \\\n","article_id                                                                     \n","0507909001                                     0                           0   \n","0665481004                                     0                           0   \n","0487052007                                     0                           0   \n","0578589001                                     0                           0   \n","0567745004                                     0                           0   \n","...                                          ...                         ...   \n","0793104018                                     0                           0   \n","0853570002                                     0                           0   \n","0908356001                                     0                           1   \n","0904026001                                     0                           0   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columns]"]},"metadata":{}}]},{"cell_type":"markdown","source":"## Calculate customer interest score","metadata":{}},{"cell_type":"markdown","source":"Check [here](https://www.youtube.com/watch?v=HjGTxoYajow&list=PLTjbQZu8nnshv8H5tV0fGh0ECBIXwuHJb&index=10) at 0:28 for formula explaination","metadata":{}},{"cell_type":"code","source":"scores = normalized_user_feature.dot(item_feature.T)\nscores","metadata":{},"execution_count":164,"outputs":[{"execution_count":164,"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>article_id</th>\n","      <th>0507909001</th>\n","      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<td>0.274921</td>\n","      <td>0.250794</td>\n","      <td>0.069524</td>\n","      <td>0.160000</td>\n","      <td>0.154603</td>\n","      <td>0.094603</td>\n","      <td>0.097143</td>\n","      <td>0.076508</td>\n","      <td>0.026667</td>\n","      <td>0.097143</td>\n","      <td>...</td>\n","      <td>0.207302</td>\n","      <td>0.170794</td>\n","      <td>0.245079</td>\n","      <td>0.278730</td>\n","      <td>0.170794</td>\n","      <td>0.146349</td>\n","      <td>0.170794</td>\n","      <td>0.118095</td>\n","      <td>0.223492</td>\n","      <td>0.206349</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>12088 rows × 51792 columns</p>\n","</div>"],"text/plain":["article_id                                          0507909001  0665481004  \\\n","customer_id                                                                  \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...    0.100000    0.100000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...    0.382609    0.282609   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...    0.208571    0.184762   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...    0.248780    0.302439   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...    0.275000    0.300000   \n","...                                                        ...         ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...    0.227273    0.227273   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...    0.150000    0.150000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.222727    0.213636   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...    0.217647    0.164706   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...    0.274921    0.250794   \n","\n","article_id                                          0487052007  0578589001  \\\n","customer_id                                                                  \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...    0.150000    0.350000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...    0.060870    0.130435   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...    0.097143    0.185714   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...    0.063415    0.141463   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...    0.050000    0.150000   \n","...                                                        ...         ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...    0.054545    0.145455   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...    0.050000    0.200000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.054545    0.100000   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...    0.082353    0.182353   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...    0.069524    0.160000   \n","\n","article_id                                          0567745004  0176550020  \\\n","customer_id                                                                  \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...    0.250000    0.150000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...    0.173913    0.073913   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...    0.175238    0.096190   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...    0.170732    0.082927   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...    0.200000    0.050000   \n","...                                                        ...         ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...    0.145455    0.109091   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...    0.200000    0.050000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.095455    0.068182   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...    0.229412    0.088235   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...    0.154603    0.094603   \n","\n","article_id                                          0558513001  0487050001  \\\n","customer_id                                                                  \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...    0.225000    0.150000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...    0.017391    0.069565   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...    0.154286    0.092381   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...    0.065854    0.065854   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...    0.000000    0.050000   \n","...                                                        ...         ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...    0.036364    0.054545   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...    0.050000    0.050000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.018182    0.054545   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...    0.011765    0.082353   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...    0.097143    0.076508   \n","\n","article_id                                          0487053021  0554546003  \\\n","customer_id                                                                  \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...    0.000000    0.225000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...    0.030435    0.017391   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...    0.008571    0.154286   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...    0.012195    0.065854   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...    0.000000    0.000000   \n","...                                                        ...         ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...    0.000000    0.036364   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...    0.000000    0.050000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.031818    0.018182   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...    0.000000    0.011765   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...    0.026667    0.097143   \n","\n","article_id                                          ...  0924040001  \\\n","customer_id                                         ...               \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...  ...    0.150000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...  ...    0.247826   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...  ...    0.146667   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...  ...    0.236585   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...  ...    0.225000   \n","...                                                 ...         ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...  ...    0.281818   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...  ...    0.150000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...  ...    0.150000   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...  ...    0.147059   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...  ...    0.207302   \n","\n","article_id                                          0925813001  0930239001  \\\n","customer_id                                                                  \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...    0.400000    0.175000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...    0.252174    0.265217   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...    0.265714    0.217143   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...    0.241463    0.251220   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...    0.300000    0.275000   \n","...                                                        ...         ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...    0.218182    0.236364   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...    0.400000    0.250000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.131818    0.145455   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\n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.231818    0.131818   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...    0.305882    0.376471   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...    0.278730    0.170794   \n","\n","article_id                                          0793104018  0853570002  \\\n","customer_id                                                                  \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...    0.450000    0.400000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...    0.169565    0.252174   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...    0.256190    0.265714   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...    0.182927    0.241463   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...    0.225000    0.300000   \n","...                                                        ...         ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...    0.145455    0.218182   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...    0.300000    0.400000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.100000    0.131818   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...    0.341176    0.376471   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...    0.146349    0.170794   \n","\n","article_id                                          0908356001  0904026001  \\\n","customer_id                                                                  \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...    0.300000    0.200000   \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...    0.173913    0.278261   \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...    0.229524    0.201905   \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...    0.185366    0.280488   \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...    0.200000    0.425000   \n","...                                                        ...         ...   \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...    0.154545    0.281818   \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...    0.350000    0.300000   \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.095455    0.150000   \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...    0.294118    0.258824   \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...    0.118095    0.223492   \n","\n","article_id                                          0916256003  \n","customer_id                                                     \n","000362878a3904e1fe4927bbfcdb10c64a9d85b12a593a7...    0.200000  \n","0019c05146d30111b7003700c712f9354fb62f9b87e53b1...    0.300000  \n","00227494dd4e87da02bb1ab4afc38f13f2e11c6517b1bbc...    0.166667  \n","0036414a83662a899a2e2c30854d8224932cc2e0d6f9c55...    0.251220  \n","003c57c114c020f3ba8e704a4b99c98129c8fd4950fb8b0...    0.400000  \n","...                                                        ...  \n","ffd8a6ce04a08854dc847409f7b9f1506f4509482ef73c8...    0.227273  \n","ffdae68f247ef0c78da5bf053dd68b7b4e9aeeec8135bb7...    0.250000  \n","ffe44295c63a13498687134a9e6ee5c57e08d84bfffa0d8...    0.222727  \n","ffeb2a74486809e237cd05be056ca2e33b2141172e99d34...    0.264706  \n","fff3573d9131d15da6a46c1ca8f03b5d37e4f6b804171ea...    0.206349  \n","\n","[12088 rows x 51792 columns]"]},"metadata":{}}]},{"cell_type":"code","source":"customer_id = scores.index[1]","metadata":{},"execution_count":171,"outputs":[]},{"cell_type":"code","source":"customer_id","metadata":{},"execution_count":173,"outputs":[{"execution_count":173,"output_type":"execute_result","data":{"text/plain":["'0019c05146d30111b7003700c712f9354fb62f9b87e53b1c9cc9f0d22fb5c62b'"]},"metadata":{}}]},{"cell_type":"code","source":"scores.loc['0019c05146d30111b7003700c712f9354fb62f9b87e53b1c9cc9f0d22fb5c62b']","metadata":{},"execution_count":174,"outputs":[{"execution_count":174,"output_type":"execute_result","data":{"text/plain":["article_id\n","0507909001    0.382609\n","0665481004    0.282609\n","0487052007    0.060870\n","0578589001    0.130435\n","0567745004    0.173913\n","                ...   \n","0793104018    0.169565\n","0853570002    0.252174\n","0908356001    0.173913\n","0904026001    0.278261\n","0916256003    0.300000\n","Name: 0019c05146d30111b7003700c712f9354fb62f9b87e53b1c9cc9f0d22fb5c62b, Length: 51792, dtype: float64"]},"metadata":{}}]},{"cell_type":"code","source":"## Define get_recmnd function\ndef get_rcmnd(customer_id, scores):\n    cutomer_scores = scores.loc[customer_id]\n    customer_prev_items = groupby_customer.get_group(customer_id)['article_id']\n    prev_dropped = cutomer_scores.drop(customer_prev_items.values)\n    ordered = prev_dropped.sort_values(ascending=False)   \n    return print(ordered[:10])","metadata":{},"execution_count":189,"outputs":[]},{"cell_type":"code","source":"get_rcmnd('0019c05146d30111b7003700c712f9354fb62f9b87e53b1c9cc9f0d22fb5c62b',scores)","metadata":{},"execution_count":190,"outputs":[{"name":"stdout","output_type":"stream","text":"article_id\n\n0800862001    0.504348\n\n0736593004    0.495652\n\n0757915002    0.495652\n\n0865587001    0.495652\n\n0759108001    0.495652\n\n0758278001    0.495652\n\n0757482002    0.495652\n\n0780887003    0.495652\n\n0758286001    0.495652\n\n0713183001    0.495652\n\nName: 0019c05146d30111b7003700c712f9354fb62f9b87e53b1c9cc9f0d22fb5c62b, dtype: float64\n"}]},{"cell_type":"markdown","source":"# Draft below","metadata":{"heading_collapsed":true}},{"cell_type":"code","source":"def isValid(s: str) -> bool:\n    stack = []\n    mapping = {'(': ')','{': '}','[': ']' }\n    for char in s:\n        if char in mapping.keys():\n            stack.append (mapping[char])\n            print(stack)\n        elif not stack or stack[-1]!=char:\n            return False\n        else:\n            stack.pop()\n    return len(stack)==0","metadata":{"hidden":true},"execution_count":160,"outputs":[]},{"cell_type":"code","source":"string = '[{[]})'\nisValid(s=string)","metadata":{"hidden":true},"execution_count":162,"outputs":[{"name":"stdout","output_type":"stream","text":"[']']\n\n[']', '}']\n\n[']', '}', ']']\n"},{"execution_count":162,"output_type":"execute_result","data":{"text/plain":["False"]},"metadata":{}}]},{"cell_type":"markdown","source":"## Transactions","metadata":{"hidden":true}},{"cell_type":"code","source":"articles['section_name']","metadata":{"hidden":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"transactions.info()","metadata":{"hidden":true},"execution_count":34,"outputs":[{"name":"stdout","output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\n\nRangeIndex: 31788324 entries, 0 to 31788323\n\nData columns (total 5 columns):\n\n #   Column            Dtype  \n\n---  ------            -----  \n\n 0   t_dat             object \n\n 1   customer_id       object \n\n 2   article_id        int64  \n\n 3   price             float64\n\n 4   sales_channel_id  int64  \n\ndtypes: float64(1), int64(2), object(2)\n\nmemory usage: 1.2+ GB\n"}]},{"cell_type":"code","source":"transactions = pd.read_csv('transactions_train.csv')\ntransactions.nunique()","metadata":{"hidden":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":["t_dat                   734\n","customer_id         1362281\n","article_id           104547\n","price                  9857\n","sales_channel_id          2\n","dtype: int64"]},"metadata":{}}]},{"cell_type":"code","source":"customers = pd.read_csv('customers.csv')\ncustomers.nunique()","metadata":{"hidden":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":["customer_id               1371980\n","FN                              1\n","Active                          1\n","club_member_status              3\n","fashion_news_frequency          4\n","age                            84\n","postal_code                352899\n","dtype: int64"]},"metadata":{}}]},{"cell_type":"code","source":"# import plotly.express as px\n# order = df['index_name'].value_counts().index\n# px.histogram(data_frame=df,y='index_group_name',color='index_name').update_xaxes(categoryorder='total ascending')","metadata":{"hidden":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Min transaction date: ', transactions['t_dat'].min())\nprint('Max transaction date: ', transactions['t_dat'].max())","metadata":{"hidden":true},"execution_count":15,"outputs":[{"name":"stdout","output_type":"stream","text":"Min transaction date:  2018-09-20\n\nMax transaction date:  2020-09-22\n"}]},{"cell_type":"code","source":"def data_sampler(customers,transactions,articles,frac):\n    '''Input three datasets and fraction of df1.\n       Keeping the related articles and customers.\n       Reference: https://www.kaggle.com/code/paweljankiewicz/hm-create-dataset-samples/notebook'''\n    customers_sample = customers.sample(frac=frac, replace=False, random_state=44)\n    customers_sample_ids = set(customers_sample[\"customer_id\"])\n    transactions_sample = transactions[transactions[\"customer_id\"].isin(customers_sample_ids)]\n    articles_sample_ids = set(transactions_sample[\"article_id\"])\n    articles_sample = articles[articles[\"article_id\"].isin(articles_sample_ids)]\n    print('customers_sample_shape:', customers_sample.shape)\n    print('transactions_sample_shape:', transactions_sample.shape)\n    print('articles_sample_shape:', articles_sample.shape)\ndata_sampler(customers,transactions,articles,frac=0.01)","metadata":{"hidden":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Only take data in 2020 due to resource and time constraints**","metadata":{"hidden":true}},{"cell_type":"code","source":"start_date = '2020-01-01'\nend_date = '2020-09-22'\ntransactions_sample = transactions[(transactions['t_dat'] >= start_date ) & (transactions['t_dat'] <= end_date )]\nprint('Sample Min transaction date:', transactions_sample['t_dat'].min())\nprint('Sample Max transaction date:', transactions_sample['t_dat'].max())\nprint('# of unique transactions:', transactions_sample['customer_id'].nunique())","metadata":{"hidden":true},"execution_count":40,"outputs":[{"name":"stdout","output_type":"stream","text":"Sample Min transaction date: 2020-01-01\n\nSample Max transaction date: 2020-09-22\n\n# of unique transactions: 862724\n"}]},{"cell_type":"markdown","source":"## Customers","metadata":{"hidden":true}}]}