Vlad Kolomiiec
Offer Vlad work on your next project.
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Kyiv,
Ukraine
10 days 21 hours back
available for hire
1 Safe completed
1 year back
1 client
age 29 years
on the service 6 years
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Portfolio
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1500 USD Microservice for order processing Python
Web ProgrammingA microservice has been developed in Python using FastAPI, which manages orders for an online store. FastAPI was chosen for its performance and support for asynchronous operations, allowing for faster request processing and improving system scalability. All data about orders, customers, and transactions is stored in PostgreSQL. To optimize queries, table indexing has been configured, ensuring high speed when working with large volumes of data.
The service supports order processing, calculation of delivery costs, and integration with external APIs for payment processing and logistics management. For example, integration with Stripe API and PayPal API has been implemented for interaction with payment systems, allowing payments to be accepted through various methods, including bank cards and electronic wallets. All user authorization and authentication is provided using JWT tokens, ensuring the security of data transmission between the client and the server.
… For executing background tasks, such as sending notifications to customers and integrating with logistics services, Celery is used. It allows for asynchronous task processing without overloading the main execution thread. Redis is used as a task broker and for data caching, which also speeds up order processing and reduces the load on the database. For example, information about order status and delivery cost calculations is cached for faster access.
API documentation is provided through the built-in Swagger UI, simplifying testing and usage of the service by other developers. The microservice has been containerized using Docker for easy deployment in different environments, including local servers and cloud platforms. The entire system also supports scaling in a cluster using Kubernetes, allowing it to adapt to increasing loads.
A feature of the service is asynchronous order processing using asyncio. This allows for efficient management of wait times for responses from external APIs, such as payment and logistics services. The system has implemented error handling and retry mechanisms for requests in case of network failures. API Rate Limiting has also been configured using Redis to limit the number of requests and protect the system from overloads and attacks. Webhooks are used to track order statuses and notify customers in real-time about changes.
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900 USD Creating a REST API for managing product data in Django
Web ProgrammingA REST API has been developed for CRUD operations with data about products in the online store (create, read, update, delete). Django REST Framework has been used to build the API.
The API supports authorization using OAuth2, which allows connecting authorization through Google and Facebook. JWT tokens are used for session management and secure access.
… Integration with the Stripe API has been implemented for payment processing: through the API, you can initiate payments, check transaction statuses, and work with the cart. The API also includes filtering, sorting, and pagination for handling a large number of product records. CORS support has been implemented for cross-domain requests.
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550 USD Automation of data collection from real estate websites (OLX.ua and DOM.R)
Data ParsingAs part of the project, a script was developed in Python that uses Selenium to automatically parse pages with dynamic content. The script processes data about real estate objects, such as description, price, location, photos, and seller contacts. It is configured for regular execution, collects new data, and updates information on already existing objects.
The collected data is automatically saved to Google Drive using the Google Drive API, which allows easy access to the data from any device and sharing it with colleagues. Each new report includes data that is then saved in CSV or Excel format for convenient analysis.
For ease of monitoring and control, the script is set up to automatically send notifications via the Slack API. Each time new data is collected or errors occur during the script execution, the user receives a message in Slack with detailed information. This allows for prompt responses to changes and errors, as well as tracking the system's performance.
A feature of the project is the use of AWS Lambda to run the script. This serverless solution saves resources, as the code execution is triggered only when data needs to be collected, significantly reducing server and infrastructure maintenance costs. AWS Lambda allows scheduling the script execution (for example, every 12 hours or daily), making the system flexible and reliable.
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950 USD Data analysis for business analytics
PythonAnalysis of sales on Ukrainian marketplaces and prediction of seasonal peaks. Sales data was collected both through internal company systems and by parsing public data on products, prices, and transactions from marketplace websites. The data was then cleaned and prepared for analysis using Python and the Pandas library. This stage included removing missing values, normalizing values, and transforming data for convenient processing in time series.
The SARIMA model was chosen for its ability to accurately predict both trends and seasonal changes based on historical data. The model was trained on data from several years, taking into account weekly and monthly sales peaks, as well as the impact of holiday and promotional campaigns. After training, the model was used to forecast future changes in sales, allowing the business to plan its inventory, marketing campaigns, and logistics more accurately.
For clear visualization of the results, an interactive dashboard was created using Dash and Plotly. This dashboard allows not only the analysis of historical data but also obtaining future forecasts in a convenient graphical form. The user can filter data by various parameters such as period, product categories, or geography. The visualization is presented in the form of graphs and charts that display seasonal fluctuations and projected future sales peaks.
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400 USD Data parsing from marketplaces and price analysis
Data ParsingA script has been developed for parsing data from the largest Ukrainian marketplaces Rozetka and Prom.ua. Selenium has been used for processing dynamic pages and BeautifulSoup for parsing HTML. Data about products, such as prices, availability, and reviews, is collected in CSV and uploaded to a MySQL database. The script is automatically run via Cron on the server for regular data updates. Notifications about sharp price changes are also implemented through the Telegram Bot API. Automation via Cron for daily data updates.
Reviews and compliments on completed projects 1
7 February 2025
203 USD
Conversion of RTSP stream from cameras to HLS
Quality
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Positive impressions from the performer! Fulfilled all requirements, responded quickly and reacted to adjustments.
Activity
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POST request
686 USD
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Resume generator website for welders
581 USD
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Vacancy: Prompt Engineer
282 USD
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Python script
316 USD
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Integration of original parts selection catalogs on the opencart site
113 USD
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Bot for HTML game
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Create a connection for call analysis
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Refinement of the existing AI model and integration into the web resource for communication with users (210k UAH)
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Bot for mini-game
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Transcription service
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