The steps involved in a typical data science project can be summarized as follows:

Problem Definition : Identify the problem to be solved and clearly define the objectives and goals of the project.

Data Collection : Gather the necessary data from various sources, such as databases, APIs, or web scraping.

Data Cleaning and Preprocessing : Clean and preprocess the data to handle missing values, outliers, and other issues that may affect the results.

Exploratory Data Analysis (EDA) : Perform an exploratory analysis of the data to gain insights into the underlying structure and relationships, and identify potential challenges and biases.

Feature Engineering : Create new features or transform existing features to improve the performance of the models.

Model Selection : Choose the appropriate machine learning model based on the problem definition and the results of the EDA.

Model Training : Train the model on the cleaned and preprocessed data.

Model Evaluation : Evaluate the performance of the model using appropriate metrics, such as accuracy, precision, recall, or AUC.

Hyperparameter Tuning : Optimize the model's performance by adjusting its hyperparameters.

Deployment : Deploy the model in a production environment and monitor its performance.

Model Maintenance : Regularly update and maintain the model to ensure that it continues to perform well and reflect changes in the underlying data.

These steps are not always performed in a strict sequence and may involve iteration and refinement throughout the project. Additionally, some steps may be omitted or added depending on the specific requirements of the project.