Hanaa Samara

A versatile professional with expertise in both web development and data analytics, demonstrate proficiency in HTML, CSS, and JavaScript to craft responsive websites using frameworks like React and Angular. Skilled in both front-end and back-end development, employing frameworks such as Django, Flask, and Node.js. In the realm of data analytics, I have expertise in utilizing Python and R, applying statistical techniques and machine learning algorithms. Ensuring data accuracy through SQL, and efficiently managing databases. Known for a results-driven approach, effective communication, and collaboration skills, well-suited for projects requiring a combination of web development and data analytics proficiency.

Project 1: Comprehensive Web Application Testing and Data Scraping – LinkedIn

Utilized Selenium and BeautifulSoup for automation and web scraping, I ensured the quality of dynamic websites through cross-functional collaboration. My expertise in automated testing for front-end web development on LinkedIn includes validating HTML/CSS integrity, JavaScript interactions, cross-browser compatibility, responsive design, load time, security, data consistency, and accessibility. This comprehensive approach ensures a robust and reliable scraping solution, contributing to a thorough assessment of user interface and performance aspects, thereby enhancing the overall quality of the web scraping process.

Project 2: Multi-Brand Watch Website Scraping and Quality Assurance.

In the context of the watch website scraping project, I demonstrated proficient analytical skills by employing advanced techniques. Utilized BeautifulSoup for meticulous HTML parsing and Selenium for automated testing, I ensured the integrity and accuracy of data extraction processes. My approach incorporated manual functional testing techniques, aligning with my extensive experience in user acceptance testing and regression testing for websites. This methodology not only validated the efficacy of automated tools but also enhanced the precision of data extraction, contributing to the overall success of the project.

Project 3: Churn Prediction – Telecommunications Crop.

In spearheading the development of a customer churn model, I undertook the task of predicting customer attrition by analyzing intricate factors such as contract details, customer demographics, service engagement, and usage patterns. The primary objective was to optimize pricing and promotion strategies for effective churn reduction. To achieve this, I meticulously preprocessed the data utilizing Pandas and Plotly, leveraging these tools to gain valuable insights that informed subsequent feature engineering efforts. This comprehensive approach not only facilitated a nuanced understanding of the underlying data but also played a pivotal role in refining the predictive model, contributing to the successful implementation of strategies geared towards reducing customer churn

Project 4: Sentiment Analysis- Twitter

In the execution of a Sentiment Analysis project, I conducted an in-depth analysis of Twitter data using the Twitter API. Raw tweets were efficiently stored in an AWS S3 bucket through Kinesis Firehose, ensuring robust data management. To prepare the data for modeling, I implemented a PySpark pipeline within Databricks, a cloud-based Apache Spark platform. Subsequently, a sentiment prediction model was developed using SparkML, leveraging the scalability and efficiency of the Spark framework. AWS Athena was employed for querying prediction results, providing a seamless and effective means of extracting valuable insights. To enhance the interpretability of results, QuickSight, an AWS visualization tool, was utilized for creating insightful visualizations pertaining to both sentiment analysis outcomes and intricate tweet details. This comprehensive approach not only ensured accurate sentiment predictions but also facilitated a streamlined and informative data analysis process