Lalith Akula

I am a dedicated Data Analyst with strong skills in Python, SQL, data cleaning, and exploratory analysis. I enjoy transforming raw data into meaningful insights that support decision-making. I have experience building interactive dashboards, automating data workflows, and applying statistical methods to solve business problems. I am detail-oriented, analytical, and comfortable working with large datasets. I continuously improve my skills in data visualization and predictive modeling to deliver clear, actionable solutions. I thrive in collaborative environments and am motivated to contribute to projects that enhance efficiency and drive measurable results for the organization.

In the Book Recommendation project, I built a content-based filtering system where I cleaned the data, extracted features using TF-IDF and Cosine Similarity, and recommended the most relevant books to users.
In the Sentiment Analysis project, I used NLP techniques to clean text, tokenize it, extract features, and build machine-learning models that classify customer feedback into positive or negative sentiment.
In the Alzheimer’s Disease project, I processed MRI image data, extracted features, and trained ML/deep-learning models to classify different stages of Alzheimer’s for early detection.
These projects helped me strengthen my skills in Python, machine learning, NLP, and data preprocessing.”

“I completed my B.Tech from Vignan's Foundation for Science, Technology & Research, where I gained a strong foundation in analytical thinking, mathematics, and problem-solving. Along with this, I enhanced my data analytics skills through specialized training at ExcelR, where I learned Python, SQL, data preprocessing, machine learning, and data visualization tools like Power BI and Tableau. This combination of formal education and hands-on data science training has prepared me well for real-world data analysis projects.”