These stages are involved in creating a predictive model in Python:

Describe the issue: Determine the target variable and the objective.
The process of gathering, cleaning, handling missing values, and preprocessing features is known as data preparation.
EDA, or exploratory data analysis: Choose attributes, visualize correlations, and analyze data trends.
Split Data: Separate the data into test and training sets.
Choose a Model: Select an appropriate algorithm, such as decision trees or regression.
Develop the Model: Use the training set to fit the model.
Evaluate: Use metrics like as accuracy or RMSE to gauge performance.
Optimize: Adjust hyperparameters to enhance functionality.
Deploy: Put the model into action.
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