A data scientist for an online retailer is given the assignment of predicting what a customer will be interested in based on past purchases. The data scientist is given access to six months of purchasing history from 1000 customers. What task does the data scientist do next? Give a presentation of the results to the targeted advertising team. Run classification models to predict which product characteristics are likely to appeal to each customer. Create scatter plots of each pair of variables to determine which are likely to be correlated. Reformat the data into a csv file and remove rows with missing data. The task is an example of which step in the data science lifecycle? Pick î

The data science lifecycle is a systematic process that data scientists follow to extract valuable insights from raw data. It involves stages such as problem definition, data collection, cleaning, exploratory analysis, feature engineering, modeling, evaluation, deployment, monitoring, and communication of results. The lifecycle is iterative, allowing for revisiting stages based on new insights or changes. Flexibility and adaptability are crucial, as data scientists navigate through the lifecycle to drive informed decision-making.The correct option is:Run classification models to predict which product characteristics are likely to appeal to each customer.Explanation:Run Classification Models:This is the most suitable option. Given past purchases, the data scientist would likely want to build predictive models (such as classification models) to understand patterns and predict which product characteristics are likely to appeal to each customer. Classification models can be trained on historical purchase data to identify patterns and preferences.Incorrect options:1. Presentation of Results:Not the next step. The data scientist usually doesn't jump directly into presenting results without analyzing and deriving insights from the data first.2. Create Scatter Plots:While scatter plots are useful for visualizing relationships between variables, they don't necessarily address the specific task mentioned. The task is about predicting product characteristics that are likely to appeal to customers, which requires more advanced analysis than scatter plots.3. Reformat Data into a CSV File:Data formatting and cleaning are important, but this step usually comes after the initial analysis. Moreover, the task mentioned is more about deriving insights and predictions from the data, not just preparing the data for storage or analysis.The task "Run classification models to predict which product characteristics are likely to appeal to each customer" is an example of the Modeling the data step in the data science lifecycle.Conclusion:The correct option involves running classification models to predict customer preferences based on past purchases. This aligns with the goal of leveraging data science techniques to gain insights and make predictions, which is a key step in the data science lifecycle. The other options may be relevant at different stages of the process but are not the immediate next step based on the provided scenario.The task of running classification models to predict customer preferences aligns with the Modeling step in the data science lifecycle, where predictive models are developed to extract insights and make predictions based on the available data.