Want to get head start with Retail Data Analytics? This tutorial is all you need. скачать в хорошем качестве

Want to get head start with Retail Data Analytics? This tutorial is all you need.

With using actual 4500+ Walmart stores locations to develop the actual geo traceable consumers profiles with family, kids, cars, credit cards and various other details. You will also develop the consumers purchase history from each Walmart store location in last 3 years for every customer which can be extended to several years as needed. Finally you will be building retail data analytics dashboard with consumer and stores maps. [What you will learn] You will learn the complex usage of Pandas, matplotlib, Faker, numpy, datetime libraries in Python along with applied data analytics into the Retail. We have used plotly mapping extensively so you will learn a lot on showing multi layers map using plotly and pandas dataset. [Future Extensions] There are several future possibilities with this project. You can use Streamlit, Dask, H2O wave and other python based application to extend this project. You can build full stack applications using python, java quarkus as backend and React, next.js etc as front end with the dataset we generate in this project. ▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬ (00:00) Tutorial Starts (00:06) Content Introduction (05:26) Phase 1: Starts (07:54) Retail Data Management (11:08) Gnerating User Profile Master Data (12:26) Faker Tutorial (12:48) User Profile - Personal Info (15:17) User Profile - Job Info (15:45) User Profile - Credit card Info (16:30) User Profile - Income group Info (17:16) User Profile - Marital Status (19:44) User Profile - Vehicle Info (21:04) User Profile - Geo based Address (21:37) Accessing Walmart Locations (31:31) Show Walmart and user address in Map (35:38) Combining everything so far (47:25) Master Function to generate data (51:00) Saving master profile data (53:03) Phase 2: Product List Creation (53:44) Instacart Product Data (59:12) Phase 3: Master Store List Creation (01:03:21) Phase 4: Purchase History Creation (01:12:38) Important concept for Geo Proximity (01:22:08) Finalizing Data Creation (01:22:48) Phase 5: Data Viz and Retail analytics (01:23:22) Store and user proximity map (01:37:43) Phase 6: Retail/Consumer Analytics (01:39:47) Retail data analytics (02:11:24) Future Extensions (02:14:10) Recap Full datasets, source code, notebooks at GitHub: https://github.com/prodramp/python-pr... Please visit: ------------------ Prodramp LLC | https://prodramp.com | @prodramp   / prodramp   Content Creator: Avkash Chauhan (@avkashchauhan)   / avkashchauhan   Tags: #python #dataengineering #walmart #ml #ai #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #modelbias #modeldeployment #modelregistery #modelpipeline #neptuneai #streamlit #pythonapps #deepchecks #modeltesting #codeartifact #dataartifact #onnx #aws #supervisord #kaggle #keplergl #mapbox #lightgbm #xgboost #classification #regression #dataengineering #pandas #keras #tensorflow #tensorboard #prodramp #avkashchauhan