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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. 
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