Data Collection: Gathering data from various sources, such as databases, APIs, or web scraping.
Data Cleaning: Preparing the data for analysis by removing errors, handling missing values, and ensuring consistency.
Exploratory Data Analysis (EDA): Analyzing the data to discover patterns, trends, and relationships through visualization and summary statistics.
Modeling: Applying statistical and machine learning techniques to make predictions or classify data.
Evaluation: Assessing the performance of models using metrics like accuracy, precision, and recall.
Deployment: Implementing models in real-world applications, often through APIs or integrated software solutions.
