Sonali S.

Data Collection: Sourcing data from various internal and external databases, APIs, or through manual collection. Data Cleaning: Processing and preparing data for analysis, which may include handling missing values, removing duplicates, or normalizing formats. Exploratory Data Analysis (EDA): Analyzing datasets to summarize their main characteristics, often using visual methods to identify patterns and outliers. Model Development: Creating predictive models using machine learning techniques, including supervised and unsupervised learning algorithms. Data Visualization: Presenting data and analysis results in a clear and visually appealing manner to stakeholders, using tools like Tableau, Matplotlib, or Power BI. Communication: Translating complex data-related findings into actionable insights for non-technical stakeholders.