Fu Zhongchen
Brescia
Overview
3
3
years of professional experience
Work History
China Huaneng Group Co., Ltd.
Accountant
10.2019 - 04.2022
● Managed daily financial records and monthly account closings to ensure accuracy of financial data.
● Utilized Excel for financial modeling and data management, enhancing the efficiency and accuracy of financial reporting.
● Assisted in annual financial audits to ensure compliance with regulations and standards.
Education
Master of Science in Data Science for Finance and Banking -
Università Cattolica Del Sacro Cuore
01.2025
Bachelor of Science in Accounting -
Shandong University of Finance and Economics
09.2019
Skills
● Languages/Tools: Python, R, SQL, SAS-viya
● Libraries/Frameworks: Pytorch, Scikit-Learn, Pandas, NumPy, BeautifulSoup, Matplotlib
● Data visualization: Tableau, seaborn
● Machine Learning: Classification (Decision Trees, Random Forest, KNN, SVM), Regression Modeling (linear, sparse, logistic, regularized), clustering (K-means, DBSCAN), Dimensionality Reduction(PCA, SVD, t-SNE)
● Deep Learning: CNN, RNN, LSTM
● Stats & Experimentation: Hypothesis testing, Covariance and correlation modeling
Competition Experience
Kaggle Competition: Learning Agency Lab - Automated Essay Scoring 2.0
Objective: Developed a model to automatically score student essays (24,000 essays, scored 1-6) to improve learning outcomes and reduce teacher workload. Result: Silver Medal (112th out of 2,706 teams) Team: DT SkyCrest Innovators
SELECTED PROJECTS
Amazon User Review Quality Prediction Project
- Analyzed Amazon user reviews using Python, implementing text vectorization, sentiment analysis, and feature engineering.
- Built classification models (Naive Bayes, SVM, KNN, Decision Trees) and enhanced performance with ensemble methods like Bagging and AdaBoost.
- Achieved over 90% accuracy through cross-validation and ensemble strategies, significantly improving review quality predictions.
Credit Repayment Capacity Assessment Project
- Processed 30+ features by handling missing data, encoding categorical variables, and standardizing features.
- Compared model performance (F1 Score, AUC) across logistic regression and RandomForest models, applying custom penalty-weighting.
- Achieved optimal fraud detection accuracy with custom penalty-weighted logistic regression and RandomForest models.
Languages
English
Upper intermediate (B2)
Italian
Beginner (A1)
Timeline
China Huaneng Group Co., Ltd.
Accountant
10.2019 - 04.2022
Bachelor of Science in Accounting -
Shandong University of Finance and Economics
Master of Science in Data Science for Finance and Banking -
Università Cattolica Del Sacro Cuore
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