Experienced Data Analyst with 1 year of hands-on experience in the construction industry. Proficient in analyzing construction project data, identifying trends, and providing actionable insights to optimize project workflows and improve decision-making processes. Conducted regular audits of construction data to ensure compliance with regulatory standards and company policies. Effective communicator with a track record of creating informative reports and visualizations that facilitate clear communication with stakeholders.

During my internship at CodSoft, I had the opportunity to work on multiple hands-on data science projects, where I strengthened my skills in data analysis, machine learning, and data visualization.Key Responsibilities:-Developed machine learning models, including Credit Card Fraud Detection and Iris Flower Classification, to gain insights and enhance predictive accuracy.-Conducted data cleaning, feature engineering, and exploratory data analysis (EDA) using Python, Pandas, and visualization libraries like Matplotlib and Seaborn.-Improved my understanding of model selection, performance evaluation, and metrics analysis, including precision, recall, and F1-score.-Collaborated with team members to apply data science techniques to solve real-world challenges.This internship provided me with a solid foundation in data science practices and equipped me with skills to tackle data-driven problems confidently.

During my internship at Prodigy InfoTech, I gained hands-on experience in data science and machine learning by working on four key projects. These projects involved data analysis, predictive modeling, and visualizations, allowing me to apply various techniques and tools in real-world scenarios.Key Responsibilities:-Developed and implemented machine learning models for predictive analytics.-Performed data cleaning, exploratory data analysis (EDA), and visualizations using tools like Jupyter Notebook, Pandas, and Matplotlib.-Collaborated with cross-functional teams to interpret and present findings.-Applied decision tree models and other algorithms to enhance marketing strategies in the banking sector.Key Projects:Project 1 – Using Jupyter Notebook, Pandas, Numpy, and Matplotlib & Seaborn, I visualized data distribution by creating a bar chart/histogram.Project 2 – I performed data cleaning and exploratory data analysis (EDA) on a dataset of my choice. I chose the Titanic dataset from Kaggle for this analysis.Project 3 –, I developed machine learning models to predict whether clients would subscribe to a term deposit based on data from direct marketing campaigns by a Portuguese banking institution.Project 4 – analyzing and visualizing sentiment patterns in social media data to understand public opinions on various topics and brands.This internship allowed me to strengthen my technical and analytical skills while providing valuable insights into real-world data science applications.

Performed as a freelance bassist for a range of bands and musical projects spanning different genres. Demonstrated the ability to quickly adapt to diverse musical styles, contributing creatively to improvisations and arrangements. Leveraged strong interpersonal and communication skills to build positive working relationships and enhance musical synergy with bandmates and clients.