How can you ensure that your predictive analytics project is based on sound reasoning?

In the world of predictive analytics, it's crucial to consider the human element often overshadowed by data. Remember the people behind the numbers, understand data origin, address ethics, involve stakeholders, consider cultural context, and build adaptable models. Collaborate with experts from various fields for a well-rounded approach. This human-centric perspective complements the data-driven aspects, enhancing the accuracy and relevance of predictions. By putting people at the center, we not only gain insights but also make data-driven decisions that resonate with the real world and its complexities.

In my experience, the best way to start an analysis is to follow the following: - List down the problem that you want to solve. - Develop questions that can be asked from Data. - Develop a hypothesis with the questions. - Take every stakeholder's opinion and input into consideration. - Develop a score model and run the predictive model.

Clearly define the objectives of your predictive analytics project. Understand what specific problem you are trying to solve or what questions you are trying to answer. Having a well-defined goal will guide your analysis in the right direction.

When carrying a predictive analysis it is very important to have we'll defined and very clear task collect data with relevant feature so as not to make your model unnecessary complex and also make sure that the data cut across all categories of the subject so as to avoid bias. The model is also assumed that the features of your data has linear relationship and does not depends on one another so you have to be aware of this and also consider limitations of the models like overffing and underfitting. You have to validate your model regularly and also test on divers dataset, consider model interpretability and choice of model are some of the approaches you can use to check all the assumptions and limitations

The task at hand determine a lot of steps you take in doing your predictive analysis, the size of your dataset and the relationship of the variables as well as interpretability just to mention a few, are some of the factors that affect the choice of your model. Characteristics of the data will also determine the type of model to adopt for your task, if your target variable is continuous you'll need a regression model, if the labels are binary then a classification task is ahead of you and if the data is without label the you cluster. Strength and weakness of model also need to be factored in some model will overfit on a certain data while another one may underfit on another dataset

There's a need to validate your data and test it's performance to be sure it is going to generalize on unseen data. You can use cross-validation, which hides a part of the data and train on the remaining part and test with the hidden set, you can use train test split to define a training, validation and testing set. Another method that could be used is the bootstrapping method Ta access the performance of your data you can use different metrics RMSE which is method that bases it's computation on residual for regression task and accuracy for classification task, this bases it's computation on the number of correctly predicted vs wrongly predicted values

Feature importance, decision explanation, intercept and coefficient are so of those measures we use to interpret our model . Model importance tells us which features our model think is the best while training the model. Decision boundaries also go a long way in interpreting our model, it tell us the factors our model are using create division among the records of the dataset

When deciding between Narita and Osaka as an air hub for a Japanese airline, it is important to consider various factors such as Airport Infrastructure and Capacity Location and Accessibility Passenger Demand and Market Potential Airline Operations and Cost Considerations Connectivity and Network Opportunities Stakeholder Engagement By considering these factors and conducting a comprehensive analysis, a Japanese airline can make an informed decision between Narita and Osaka as an air hub. This case contain 2 airports like an A-B testing would let insight based on sound reasoning.