\n Hi there 👋
\n This notebook is built with my interest in Fashion (yeah that's right. I'm a fashion freak 👦). This is an analysis on products by H&M, their customers, and overall transactions.
\n This Analysis is made with my passion for Data Science. If you find this notebook interesting, please give an upvote ⬆️. Thank you!💜\n
\n Few weeks back, I built a notebook on some anime dataset which improved my skills on Data Visualization with Plotly Express, then I got into the Kaggle's TPS March Competition where I learnt about some Machine Learning techniques. So basically I haven't touched Plotly since Feb:) Which is why I decided to polish my Plotly Graph Objects skills through this Notebook.
\n Through this, I've analyzed bout-\n
\n Major tools/libraries used : Pandas, Plotly, Seaborn, WordCloud\n
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numpy as np\nimport pandas as pd\nimport warnings\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport plotly.figure_factory as ff\nimport plotly.io as pio\nfrom plotly.subplots import make_subplots\nfrom termcolor import colored\nfrom PIL import Image\nfrom wordcloud import WordCloud, STOPWORDS, ImageColorGenerator","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-04-07T15:52:35.648403Z","iopub.execute_input":"2022-04-07T15:52:35.648752Z","iopub.status.idle":"2022-04-07T15:52:39.169238Z","shell.execute_reply.started":"2022-04-07T15:52:35.648666Z","shell.execute_reply":"2022-04-07T15:52:39.168327Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pio.templates.default = \"simple_white\"\ngraph_font = \"Franklin Gothic\"\nheading_font = \"Brush Script MT, Brush Script Std, cursive\"\nnormal_font = \"Brush Script MT, Brush Script Std\"\ngradient = [\"f72585\",\"b5179e\",\"7209b7\",\"560bad\",\"480ca8\",\"3a0ca3\",\"3f37c9\",\"4361ee\",\"4895ef\",\"4cc9f0\"]\npinks = [\"#f72585\", \"#f966a9\", \"#fcbdda\"]\nviolets = [\"#7209b7\", \"#9a37db\", \"#d799ff\", \"#edd1ff\"]\nblues = [\"#3f37c9\", \"#756fd6\", \"#a7a3f0\", \"#d1cfff\"]\nlight_blues = [\"#4cc9f0\", \"#93def6\", \"#c9eefa\", \"#ebfaff\"]\ngradient_with_hash = [\"#\" + i for i in gradient]\npalette = px.colors.qualitative.Pastel\npalette_r = px.colors.qualitative.Pastel_r\nset1 = px.colors.qualitative.Set1\nset2 = px.colors.qualitative.Set2\nset3 = px.colors.qualitative.Set3\nwarnings.filterwarnings('ignore')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-04-07T15:52:39.170861Z","iopub.execute_input":"2022-04-07T15:52:39.171104Z","iopub.status.idle":"2022-04-07T15:52:39.946995Z","shell.execute_reply.started":"2022-04-07T15:52:39.171075Z","shell.execute_reply":"2022-04-07T15:52:39.946101Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n There are different groups of products and within those groups, there are different types.
\n Here is a visualization summarizing the different categories of products :\n
\n 📌 Among all the 18 groups of product, only 9 groups are having 1000+ products. The rest of the groups are having the number of products below 100.
\n Only the top 4 groups are having 10,000 products. These groups are - Garment Upper body, Garment Upper body, Garment Full body, Accessories.\n
\n The appearance of the products are categorized as : Graphic Appearance, Colour Group, Percieved Colour Master, Percieved Colour Value
\n Here is a visualization summarizing the different categories of products :\n
\n 📌 ost of products here is basically blck with a solid texture 🖤\n
\n