This project is a Binary Classification project carried out in Applied Data Science Bootcamp on Kodluyoruz, which is realized under the trainings of Çağlar Subaşı, using the Genetic Variant Classification dataset in Kaggle.
Dataset --> (https://www.kaggle.com/kevinarvai/clinvar-conflicting)
The purpose of this project is the objective is to predict whether a Clinic Variant will have conflicting classifications. This is presented here as a binary classification problem, where each record in the dataset is a genetic variant.
- Inferential Statistics
- Machine Learning
- Data Visualization
- Predictive Modeling
- Python
- Pandas, Numpy
- Matplotlib,Seaborn
- Missingno
- Sklearn
- Logistic Regression
- XGBoost Classifier
- KNeighbors Classifier
- Decision Tree Classifier
- LigthGBM Classifier
- Gradient Boosting Classifier
- Hist Gradient Boosting Classifier
Clinic Variant is a public resource containing annotations about human genetic variants. These variants are (usually manually) classified by clinical laboratories on a categorical spectrum ranging from benign, likely benign, uncertain significance, likely pathogenic, and pathogenic. Variants that have conflicting classifications (from laboratory to laboratory) can cause confusion when clinicians or researchers try to interpret whether the variant has an impact on the disease of a given patient.
Conflicting classifications are when two of any of the following three categories are present for one variant, two submissions of one category are not considered conflicting.
- Likely Benign or Benign
- VUS
- Likely Pathogenic or Pathogenic
Conflicting classification has been assigned to the CLASS column. It is a binary representation of whether or not a variant has conflicting classifications, where 0 represents consistent classifications and 1 represents conflicting classifications.
- data exploration/descriptive statistics
- data processing/cleaning
- statistical modeling
- writeup/reporting
| Name |
|---|
| Sümeyye ÖZTÜRK |
| Mehmet Haliloğlu |
| Mert Yıldırır |