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Technologies:

• Programming Language: Python
• Libraries: Pandas, Scikit-learn, Matplotlib, Numpy

Approach

1. Filling missing values
2. Perform necessary cleaning and EDA.
3. One hot Encoding
4. Performing necessary transformation.
5. Scaling the dataset.
6. Adding more minority samples.
7. Selecting most accurate model.

Model

• Logistic Regression Model
  • f1-score : 0.60
• SVM
  • f1-score : 0.65
• Random Forest
  • f1-score : 0.86
• Decision_Tree
  • f1-score : 0.77