• Orchestrated the management of customer data from various sources, overseeing a user base exceeding 10 million individuals.• Spearheaded a RFM segmentation project using Python, resulting in the creation of hundreds of targeted segments. This endeavor led to a 67% increase in Open Rate.• Directed the launch of multi-channel campaigns, integrating email, push notifications, and chatbots with personalized content to every customers. This approach yielded a 2.5X increase in revenue share.• Conducted A/B tests with an automatically rotated control group and analyzed results to uncover optimal segments and approaches.• Leveraged Python, including Pandas and SQL, to conduct campaign analysis, identifying crucial growth opportunities and informing strategic decision-making processes.

-- Cloud-based web storage project --• Engineered a comprehensive cloud web storage solution utilizing Python (Django) for server-side architecture and Javascript (React) for frontend-side.• Orchestrated seamless integration of Django with React to craft a dynamic and user-friendly interface, enhancing user experience.• Implemented authentication and authorization, along with encryption, ensuring secure user data storage and privacy protection within PostgreSQL databases.

-- Face mask detection and recognition in real-time project --• Developed a real-time face mask detection and recognition system aimed at addressing public health and security concerns by accurately classifying images based on the presence or absence of masks.• Implemented real-time functionality, enabling the classifier to perform detection and classification using the device's camera, facilitating swift identification of individuals wearing masks.• Leveraged Python alongside TensorFlow, Pandas, NumPy, and Matplotlib to construct a robust machine learning model and conduct comprehensive data analysis and accuracy evaluation. Evaluated classifier performance using a diverse set of metrics including accuracy, loss, precision, recall, and F-score, ensuring thorough assessment and optimization of model efficacy.• Successfully deployed a neural network architecture that achieved exceptional results, surpassing performance expectations with an accuracy rate exceeding 99%. This accomplishment underscores the effectiveness and reliability of the developed solution in real-world applications.

-- Backing up photos from a social network to cloud storage --• Developed a program to backup photos from user profiles (avatars) on the VK social network to Yandex.Disk cloud storage, utilizing Python and REST API technologies.• Implemented functionality to retrieve profile photos from the VK API and save them to Yandex.Disk with maximum resolution.• Utilized the number of likes to name the photos, ensuring unique identification, and if likes were the same, appended the upload date to the file name.• Saved information about the backed-up photos to a JSON file, providing a comprehensive record of the saved images and their attributes.• Ensured compliance with mandatory requirements, including the use of the REST API of Yandex.Disk with a key obtained from Polygon, creating a dedicated folder for uploaded photos, and handling errors gracefully.• Implemented progress tracking mechanisms, such as a progress bar or logging, to monitor the execution of the program and provide real-time feedback on its progress.--Code repository on Github: https://github.com/lulzseq/netology-full-stack-python/tree/master/04.%20Coursework

-- ML-research «Sales forecast and identification of factors affecting sales» --• Conducted a comprehensive machine learning research project focused on sales forecast and identification of key factors influencing sales for a chain of retail stores, utilizing a dataset comprising 8523 sales transactions.• Employed Python alongside TensorFlow, XGBoost, Pandas, NumPy, Matplotlib, and Seaborn to construct and evaluate the predictive models, ensuring analysis and visualization of the data.• Developed predictive models to forecast sales for individual products within specific store locations, leveraging advanced machine learning algorithms and techniques based on historical data analysis and model performance evaluation.• Identified and analyzed the factors that significantly impact sales performance, uncovering hidden patterns and insights within the sales data to inform strategic decision-making and optimize business operations.• Utilized advanced analytical techniques to reveal hidden patterns and correlations within the sales data, providing valuable insights into consumer behavior, market trends, and product performance.