Data analytics intern with experience in data cleaning, preprocessing, analysis, and visualization using Python, SQL, and Tableau. Has worked on machine learning and data science projects, including COVID-19 analysis, Ti…
Data analytics intern with experience in data cleaning, preprocessing, analysis, and visualization using Python, SQL, and Tableau. Has worked on machine learning and data science projects, including COVID-19 analysis, Titanic survival prediction, and fake news detection.
Conducted data cleaning and preprocessing tasks to improve data quality, including identifying and addressing missing values, outliers, and inconsistencies, resulting in a 15% increase in data accuracy.
Utilized Python and SQL to analyze and extract actionable insights from large datasets, helping the marketing team optimize their campaigns and increase customer engagement by 10%.
Created interactive data visualizations in Tableau to effectively communicate complex data findings, aiding management in making informed decisions and reducing data interpretation time by 20%.
Collaborated with cross-functional teams to define project requirements and implement data-driven solutions, resulting in a 25% reduction in operational costs and a 12% increase in customer satisfaction scores.
Identified and analyzed 52% critical periods in the pandemic's progression.
Conducted comprehensive analysis of COVID-19 data, resulting in a 15% improvement in data preprocessing efficiency.
Recommended resource allocation strategies, leading to a 20% increase in the efficiency of the pandemic response.
Presented findings and visualizations, facilitating data-driven decision-making upto 100%.
Designed and developed a classification system using machine learning for predicting the likelihood of a person surviving the Titanic sinking.
Analyzed various factors such as socio-economic status, age, gender, and more to identify the most influential factors contributing to survival.
Utilized Python programming, Pandas, Scikit-Learn, and other data science tools to preprocess and statistically analyze the Titanic dataset.
Presented findings and insights from the Titanic classification model to stakeholders, enabling data-driven decision-making for safety measures.
Mentored SWE students.
Gave career advice to SWE students.
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