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Jung-Pu-Chang/README.md

💻 Tech Stack

Python R

📃: Experience

  • 2022.9 ~ now - Data Analyst at Datarget

    • Using GNN + SNA + Collaborative Filtering to build recommandation system and sale to famous retail enterprises. Recommandation system promote customer's demand and take huge benefit to famous retail enterprises.
    • Using Binning + Feature Selection + Regression to build member rating system and sale to famous retail enterprises. From the system, we can figure out the most suitable member in every activities and explain the reason.
    • Using Prophet + SARIMAX + Feature Creation to build Inventory Management System. POC in famous semiconductor manufacturing enterprise. It reduced 70% deviation.
    • Using GA optimization to build retail map and sale to famous retail enterprises. Retail map can optimize teant place, bring confortable shopping experience to customer.
  • 2022.4 ~ 2022.7 - Data Analyst Intern at Yulon Finance Corporation

    • Using Feature Selection(IV、Fisher、GINI、AIC.etc) to figure out useful feature for default model. Finally, we promote 20% KS value and avoid many clients default.
  • 2020.9 ~ 2022.6 - Research (Feature Engineering) Assistant & Master Student in DS.SCU

  • 2018.8 ~ 2020.6 - Data Analyst at CashBox

    • Using associate analysis to build recommandation system and import to KTV. After customer choose the song, system would recommand food or drink to customer.
    • Using xgb to predict consumer spending power and caculate suitable coupon.
  • 2014.9 ~ 2018.6 - Bachelor student in AM.NDHU

Pinned Loading

  1. Member_Classification Member_Classification Public

    Caculate LRFM、NES、CAI

    Python

  2. UsefulML UsefulML Public

    ML useful package

    Python

  3. stock_feature_selection stock_feature_selection Public

    R 1 1

  4. Recommandation_System Recommandation_System Public