The HeartDiseaseDataset contains data across 14 columns, with 1025 rows. The columns include:

- `age` (age in years)
- `sex` (sex; where 1 = male, 0 = female)
- `cp` (chest pain type)
- `trestbps` (resting blood pressure)
- `chol` (serum cholestoral in mg/dl)
- `fbs` (fasting blood sugar > 120 mg/dl)
- `restecg` (resting electrocardiographic results)
- `thalach` (maximum heart rate achieved)
- `exang` (exercise induced angina)
- `oldpeak` (ST depression induced by exercise relative to rest)
- `slope` (the slope of the peak exercise ST segment)
- `ca` (number of major vessels colored by flourosopy)
- `thal` (3 = normal; 6 = fixed defect; 7 = reversable defect)
- `target` (presence of heart disease in the patient)

Next step is to assess the issue mentioned in the hint - '723 duplicated entries related to health conditions'. Let's identify these duplicated entries.### Issue 1

**Issue**: "Duplicated entries in dataset"

**Evidence**: The HeartDiseaseDataset.csv file contains 723 duplicated entries. Multiple entries with identical information exist in several rows (e.g., rows for patient age 29, sex 1, cp 1, trestbps 130, chol 204, fbs 0, restecg 0, thalach 202, exang 0, oldpeak 0.0, slope 2, ca 0, thal 2, and target 1 repeated several times).

**Description**: According to the hint provided, there are 723 duplicated entries within the HeartDiseaseDataset.csv file. Upon validation, it was confirmed that there exists a considerable number of duplicate rows, which could impact the integrity, reliability, and performance of analyses conducted using this dataset. In a medical dataset such as this, each entry is expected to represent a unique patient or a unique condition state for patient-specific entries. Duplicates can lead to biased results in diagnostic analysis, predictive modeling, and statistical assessment, thereby misrepresenting the real incidence and distribution of heart disease conditions within the analyzed population. Removing or flagging these duplicate entries is essential for maintaining the dataset's accuracy and utility for medical research and clinical application.