Data-driven problem solver with expertise in analytics and forecasting.
I blend statistical rigor with a passion for transforming raw data into impactful predictions, thriving on turning complex data into meaningful insights that drive decisions. On a mission to turn data complexity into clarity with a strong foundation in statistics, I find purpose in translating data into narratives that shape the future of healthcare, sports, and beyond. Driven by data, I constantly pursue innovation and excellence in data science.
Skills
Education
Me as a Statistician has a sound understanding of statistical theories and data organization. Not only extract and offer valuable insights from the data clusters, but also help create new methodologies for the engineers to apply.
Me as a Data analyst,responsible for a variety of tasks including visualisation, munging, and processing of massive amounts of data and also have to perform queries on the databases from time to time. One of the most important skills of a data analyst is optimization just because we have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.
Me as a Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, expected to perform predictive analysis and run a fine-toothed comb through an “unstructured/disorganized” data to offer actionable insights and can also do this by identifying trends and patterns that can help the companies in making better decisions.
1. WORLD BANK GROUP ANALYSIS(EDA):
Analyze and visualize global age and gender distribution using Python, Matplotlib, and World Bank data.
2. MOVIE RATING (ML):
Predict movie ratings using features like genre, director, and actors with machine learning, data preprocessing, and model evaluation.
3. AMAZON REVIEW RATING (SENTIMENT ANALYSIS):
Analyze and visualize sentiment in Amazon reviews using TextBlob and NLTK Vader, with bar plots, word clouds, and insights.
4. STELLAR CLASSIFICATION (ML):
Analyze and classify stars, galaxies, and quasars using SDSS data with EDA, feature engineering, modeling, and advanced visualizations.
THANKING YOU