Context This data set deals with the financial distress prediction for a sample of companies. Content **First column**: **Company** represents sample companies. **Second column**: **Time** shows different time periods that data belongs to. Time series length varies between **1** to **14** for each company. **Third column**: The target variable is denoted by "**Financial Distress**" if it is greater than -0.50 the company should be considered as **healthy** (**0**). Otherwise, it would be regarded as **financially distressed** (**1**). **Fourth column to the last column**: The features denoted by **x1** to **x83**, are some financial and non-financial characteristics of the sampled companies. These features belong to the previous time period, which should be used to predict whether the company will be financially distressed or not (classification). Feature **x80** is a **categorical variable**. For example, company 1 is financially distressed at time 4 but company 2 is still healthy at time 14. This data set is *imbalanced* (there are 136 financially distressed companies against 286 healthy ones i.e., 136 firm-year observations are financially distressed while 3546 firm-year observations are healthy) and *skewed*, so **f-score** should be employed as the performance evaluation criterion. It should be noted that **30%** of this data set should be randomly assigned as **hold-out test set** so the remaining **70%** is used for feature selection and model selection i.e., **train set**. Note: 1- This data could be viewed as a classification problem. 2- This data could also be considered as a regression problem and then the result will be converted into a classification. 3- This data could be regarded as a multivariate time series classification. Inspiration Which features are most indicative of financial distress? What types of machine learning models perform best on this dataset?