What's included

• Data Collection and Preparation

  Gathering data from diverse sources like databases, APIs, web scraping, or user-generated data. Cleaning, transforming, and preparing data for analysis to ensure accuracy and completeness.

• Exploratory Data Analysis (EDA)

  Conducting statistical analysis to understand data distribution and relationships between variables. Visualizing data using tools like Matplotlib, Seaborn, Tableau, or Power BI to uncover trends and anomalies.

• Model Development

  Building machine learning models (e.g., regression, classification, clustering) using tools like Python (scikit-learn, TensorFlow, PyTorch) or R. Optimizing models by fine-tuning hyperparameters and validating performance using cross-validation techniques.

• Data-Driven Decision Making

  Providing insights and recommendations based on analysis and model predictions. Communicating findings to stakeholders using dashboards, reports, or presentations.