This is a very strong breakdown of your projects. Translating this for a CV requires using “action verbs” (Developed, Engineered, Optimized) and ensuring the business impact is front and center.

Here is the professional translation:

Project Portfolio
10+ End-to-End Machine Learning Projects completed through Yandex Practicum and independent initiatives. Each project addresses real-world business challenges with measurable outcomes.

Key Implementations:
Production Discord Anti-Toxicity Bot

Deployed an automated moderation bot for Discord communities.

Processes 2,000+ messages daily, reducing toxic content by 20%.

Supports bilingual classification (EN/RU) with 79% accuracy.

Stack: Flask API, systemd, automated deployment.

Impact: Currently active across multiple live communities.

Telecom Churn Prediction Model

Developed an ML system to predict customer churn for a telecom provider.

Achieved 92.6% Recall and 91.8% Precision in identifying at-risk customers.

Utilized Ensemble Learning (XGBoost + TensorFlow) and advanced feature engineering.

Business Value: Potential to prevent 15% of unplanned churn.

Vehicle Valuation Regression System

Created a regression model for automated used-car appraisal across 50,000+ listings.

Achieved a Mean Prediction Error of $1,928 through rigorous outlier handling and categorical encoding.

Use Case: Streamlining trade-in valuations for dealerships.

Taxi Demand Forecasting

Built a time-series model for hourly order volume prediction.

Accounted for seasonality and trends, achieving an accuracy of ±29 orders/hour.

Impact: Optimized driver distribution, reducing idle time by 10%.

Computer Vision: Age Detection from Images

Trained a CNN (ResNet50) to estimate customer ages in retail environments.

Achieved a Mean Absolute Error (MAE) of 6.7 years on a dataset of 10,000+ images.

Applied Data Augmentation and Transfer Learning.

Use Case: Automating age verification at point-of-sale terminals.

HR Analytics: Attrition Prediction

Developed a predictive system for employee turnover with a 6-month horizon.

Achieved a 93.36% ROC-AUC.

Implemented model interpretability using SHAP for transparent reporting.

Business Value: Early identification of high-risk talent.

Sports Analytics: Automated Video Slicing

Engineered a pipeline for automated athlete detection and tracking.

Combined YOLO11 for detection with OpenCV for post-processing.

Impact: Reduced video preparation time from 4 hours to 15 minutes.

Technical Skills (Project-Proven)
Machine Learning:

Classification: Logistic Regression, XGBoost, Random Forest.

Regression: LightGBM, Gradient Boosting.

Time Series: Seasonality, trends, feature engineering.

Metrics: ROC-AUC, RMSE, MAE, F1-score.

Deep Learning:

Computer Vision: CNN (ResNet, Transfer Learning).

NLP: Toxicity detection, TF-IDF, Word Embeddings.

Frameworks: TensorFlow, Keras, PyTorch.

Production & DevOps:

API Development: Flask, REST.

Deployment: systemd, service automation.

Orchestration: Basic Containerization (Docker).

Tools:

Python: Pandas, NumPy, Scikit-learn, Matplotlib.

Computer Vision: OpenCV, PIL, YOLO.

Databases: SQL for data analysis.

Version Control: Git, GitHub.