My projects

When and where: The project was conducted in 2022-23 for a company specializing in IT staffing and body leasing.

Technologies used:

Python, SQL, Spark, Hadoop, Kafka, Flask, PostgreSQL, AWS

When and where: The project was conducted in 2022-23 for an online retail company.

Technologies used: Python, Spark, Flask, AWS, SQL, Hadoop, PostgreSQL, Kafka.

1. The project focused on designing and implementing a recommendation system to enhance the customer shopping experience.
2. It utilized collaborative filtering and content-based algorithms to suggest personalized products to users.
3. The system increased user engagement, boosted sales, and improved customer satisfaction rates.

When and where: The project was conducted in 2022-23 for an e-commerce company.

Technologies used: Python, Spark, Flask, AWS, SQL, Hadoop, PostgreSQL, Kafka.

1. This project focused on designing a system to manage and optimize waiting carts for users on the platform.
2. The system provided personalized recommendations and reminders to encourage customers to finalize their purchases.
3. Its implementation increased conversion rates by addressing cart abandonment issues effectively.

When and where: The project was conducted in 2023 for a fashion e-commerce company.

Technologies used: Python, Spark, Flask, AWS, SQL, Hadoop, PostgreSQL, Kafka.

1. The project involved developing an intelligent search engine to enhance the user experience by allowing personalized and accurate clothing searches.
2. The system used advanced filtering and recommendation algorithms to match customer preferences with available inventory.
3. Its implementation improved search relevance, increased user engagement, and boosted sales.

When and where: The project was conducted in 2021-22 for a government research institute.

Technologies used: Python, Spark, Flask, AWS, SQL, Hadoop, PostgreSQL, Kafka.

1. The project focused on analyzing population behavior to identify trends and patterns using large datasets.
2. It involved advanced data modeling and visualization to support decision-making in public policy and resource allocation.
3. The findings provided actionable insights that helped optimize community support programs and improve service delivery.

When and where: The project was conducted in 2017-2021 for Grupa Azoty, a leading chemical company in Poland.

Technologies used: Python, Spark, Flask, AWS, SQL, Hadoop, PostgreSQL, Kafka.

1. The project aimed to optimize the sulfur flow process within the production facilities to reduce waste and improve efficiency.
2. Advanced data analysis and simulation techniques were applied to model and enhance the flow dynamics.
3. The results contributed to significant cost savings and a more sustainable production process.

1. This project focused on developing a system to detect anomalies in real-time during chemical plant operations, ensuring safety and efficiency.
2. Advanced machine learning algorithms were employed to analyze sensor data and identify deviations from normal operating conditions.
3. The system improved operational reliability by preventing potential failures and optimizing maintenance schedules.

1. The project involved developing a predictive model to forecast gas prices for a 14-day horizon using historical data and market trends.
2. The model utilized machine learning techniques to provide accurate and actionable price predictions.
3. Its implementation supported better decision-making in procurement and inventory management, reducing operational risks.

When and where: The project was conducted in 2014-2018 for Silva, a company specializing in logistics and transportation.

Technologies used: Python, Spark, Flask, AWS, SQL, Hadoop, PostgreSQL, Kafka.

1. This project focused on developing a system to detect anomalies in fuel consumption across Silva’s fleet operations.
2. The system used advanced analytics and machine learning to identify irregular fuel usage patterns and potential inefficiencies.
3. The implementation helped reduce fuel costs and improved the company’s operational efficiency.

1. The project involved building a system to detect fraudulent activities in financial transactions in real-time.
2. Machine learning algorithms were employed to analyze transaction patterns and flag suspicious activities.
3. The system improved fraud detection accuracy, reducing financial losses and enhancing customer trust.

1. The project focused on developing a system to visually identify wood quality based on image analysis and machine learning techniques.
2. The system utilized advanced algorithms to classify wood defects and grade the quality of timber in real-time.
3. Its implementation improved production efficiency and ensured high standards of quality control.

1. The project aimed to streamline the entry process for vehicles transporting timber by eliminating queues at the entry gates.
2. Advanced scheduling algorithms and real-time tracking were implemented to optimize vehicle flow and reduce waiting times.
3. The solution significantly improved logistics efficiency and enhanced driver satisfaction.

1. The project focused on optimizing loading and unloading times for vehicles to enhance operational efficiency and reduce delays.
2. Data analytics and predictive modeling were used to identify bottlenecks and implement solutions for streamlined processes.
3. The outcome resulted in significant time savings and improved resource utilization.

1. This project focused on detecting fraudulent activities within mass transactions using advanced data analysis techniques.
2. Machine learning models were developed to analyze transaction patterns, identify anomalies, and flag suspicious activities in real time.
3. The solution enhanced fraud detection efficiency, reduced financial losses, and improved trust among clients.

1. This project focused on analyzing the effectiveness of various marketing stimuli in driving customer engagement and product adoption.
2. Statistical models and data analytics were applied to evaluate the impact of marketing strategies on customer behavior.
3. The findings helped optimize future marketing campaigns and improve overall customer retention rates.

1. This project aimed to optimize deliveries to the bakery during the second shift to ensure timely supply and reduce delays.
2. Advanced logistics planning and route optimization were employed to streamline the delivery process.
3. The solution improved operational efficiency, reduced costs, and ensured fresh product availability for the bakery.

1. The project focused on repairing and optimizing the automatic order system to ensure its seamless functionality.
2. Key issues, such as order processing delays and system crashes, were addressed through detailed diagnostics and code improvements.
3. The repaired system enhanced order accuracy, reduced downtime, and improved overall customer satisfaction.

1. The project involved developing a notification system to track and alert staff about products nearing expiration dates.
2. The system utilized automated alerts and data analytics to ensure timely removal of expired goods from shelves.
3. Its implementation reduced waste, improved inventory management, and enhanced customer satisfaction by maintaining product freshness.

1. This project focused on designing an automatic ordering system tailored for special periods such as holidays or promotional campaigns.
2. The system used predictive analytics and historical data to optimize inventory levels and prevent stock shortages or overstocking.
3. Its implementation improved operational efficiency, reduced waste, and ensured product availability during high-demand periods.

1. The project aimed to develop a model to optimize the size and scope of promotions to maximize profitability and customer engagement.
2. Advanced data analysis and machine learning algorithms were used to predict the effectiveness of different promotion strategies.
3. The model provided actionable insights, leading to better allocation of promotional budgets and improved sales performance.