The contents of the two files are as follows:

- The first file seems to be a text documentation of the dataset, possibly `readme.md`, judging by its descriptive nature regarding the dataset.
- The second file appears to contain CSV-formatted data about student performance, likely the `student_performance_prediction.csv`, as it includes various attributes like 'Student ID', 'Study Hours per Week', 'Attendance Rate', 'Previous Grades', etc.

To address the hint provided, I will specifically look for the issues mentioned:
- Negative study hours
- Attendance rates over 100%
- Grades above 100

I'll start by examining the `student_performance_prediction.csv` for the mentioned issues.Based on the analysis of the `student_performance_prediction.csv`, the following issues have been identified:

1. **Negative Study Hours**
   - **Issue**: Negative values in "Study Hours per Week".
   - **Evidence**: Rows with 'Study Hours per Week' such as -3.1, -0.1, -6.2, etc.
   - **Description**: The dataset contains entries with negative values for the 'Study Hours per Week' column, which is not feasible in a real-world context. Study hours should always be a non-negative number, indicating the amount of time a student spends studying.

2. **Attendance Rates Over 100%**
   - **Issue**: Attendance rates exceed 100%.
   - **Evidence**: Rows with 'Attendance Rate' such as 101.5, 113.5, etc.
   - **Description**: Several entries in the dataset include attendance rates that are over 100%, which is illogical as it exceeds the maximum possible rate of attendance.

3. **Grades Above 100**
   - **Issue**: Grades exceeding the maximum score of 100.
   - **Evidence**: Rows with 'Previous Grades' such as 102.0, 104.9, 200.0, etc.
   - **Description**: The dataset contains instances where the 'Previous Grades' are listed higher than the maximum score of 100. This contradicts the conventional grading system, where 100 is typically the maximum achievable grade.

The evidence provided points directly to data entries in the CSV file, highlighting values that contradict logical and expected conditions based on the hint provided. These issues indicate a need for data cleaning and validation to ensure the dataset accurately represents real-world scenarios and stays within logical bounds.