Based on the contents of the `readme.md` and the `Raw-Data.csv` file, here are the identified issues related to the hint "unclear dataset target":

1. Lack of clear target variable definition in dataset and `readme.md`.
   - **Issue**: The dataset seems to aim at predicting whether a person has coronavirus disease or not, based on the description provided in the `readme.md`. However, there is no explicit mention or presence of a target variable (such as a "Diagnosis" column with positive or negative cases) in both the `readme.md` content and the `Raw-Data.csv` that would serve as the output for model prediction or analysis.
   - **Evidence**: The `readme.md` mentions, "These data will help to identify whether any person is having a coronavirus disease or not based on some pre-defined standard symptoms..." However, the `Raw-Data.csv` does not include any explicit target variable like Diagnosis or COVID-19 result.
   - **Description**: For supervised learning applications, it is crucial to have a clear definition of the target variable for modeling purposes. The absence of such information can lead to ambiguity in understanding what the dataset is supposed to predict. The dataset needs an explicit target variable indication, either in the dataset itself or clearly described in the readme file, guiding users on how to interpret or create the target variable from the existing features.

2. Discrepancy between gender specifications and implications for dataset target clarity.
   - **Issue**: The `Raw-Data.csv` includes a `Gender` column with 'Male', 'Female', and 'Transgender' options, but there are missing values for this variable without clear guidance in the `readme.md` on how to handle such cases, especially concerning the dataset's target of predicting COVID-19 status.
   - **Evidence**: The `Raw-Data.csv` shows missing values for the `Gender` column, such as in rows indexed 3 and 4 for the countries 'Republic of Korean' and 'France', respectively.
   - **Description**: The inclusion of gender data with missing values but without explicit handling instructions may introduce bias or inaccuracies in analyses aimed at understanding the impact of gender on COVID-19 infection rates. This omission complicates the dataset's objective of analyzing COVID-19 spread or severity among different demographic groups without clear instructions on handling or interpreting these missing values within the context of the dataset's target.

3. Ambiguity around the "Severity" and "Contact" columns in relation to the dataset target.
   - **Issue**: The dataset includes "Severity" and "Contact" columns, which are crucial for understanding COVID-19 impact but lacks explanation on how these factors relate to the central aim of diagnosing COVID-19 status.
   - **Evidence**: The `Severity` column has values like 'Mild', 'Moderate', 'Severe', and 'None', and the `Contact` column has 'Yes', 'No', 'Dont-Know', and missing values. However, there's no clear guideline in the `readme.md` on how these variables contribute to determining someone's COVID-19 status.
   - **Description**: For effective modeling or analysis, understanding the relationship between variables such as 'Severity' of symptoms and having 'Contact' with a COVID-19 patient to the prediction of COVID-19 status is essential. The lack of explicit instruction or explanation in the `readme.md` leaves potential users of the dataset guessing about how to utilize these variables in their analysis, affecting the dataset's overall clarity and utility for its intended purpose.

These issues, especially the lack of a defined target variable, could hinder the effective use of the dataset for predictive modeling, research, or analysis in its current form, suggesting the need for revisions or additional documentation to clarify its intended applications and how to navigate its content.