A typical data science project lifecycle involves the following key steps:

1. Problem definition: Clearly defining the problem that needs to be solved and setting the goals and objectives of the project.
2. Data collection: Gathering raw data from various sources such as databases, APIs, web scraping, or collecting it manually.
3. Data cleaning and preprocessing: Removing inconsistencies, dealing with missing values, and transforming data into a suitable format for analysis.
4. Exploratory Data Analysis (EDA): Summarizing and visualizing data to understand its underlying structure, patterns, and relationships.
5. Feature engineering: Creating new variables and selecting the most important features that contribute to the predictive power of the model.
6. Model selection and training: Choosing the appropriate machine learning algorithm, training the model, and optimizing its hyperparameters.
7. Model evaluation: Assessing the performance of the model using metrics such as accuracy, precision, recall, and F1-score.
8. Model deployment: Implementing the trained model into a production environment, making it available for users or other systems.
9. Model maintenance and monitoring: Continuously monitoring the model's performance, updating it with new data, and fine-tuning it as needed.

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