Any data analysis problem involves a series of steps such as:

Identifying a business problem.

Understanding the problem domain with the help of a domain expert.

Identifying data sources and data variables suitable for the analysis.

Data preprocessing or a cleansing step, such as identifying missing values, quantitative and qualitative variables and transformations, and so on.

Performing exploratory analysis to understand the data, mostly through visual graphs such as box plots or histograms.

Performing basic statistics such as mean, median, modes, variances, standard deviations, correlation among the variables, and covariance to understand the nature of the data.

Dividing the data into training and testing datasets and running a model using machine-learning algorithms with training datasets, using cross-validation techniques.

Validating the model using the test data to evaluate the model on the new data. If needed, improve the model based on the results of the validation...