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Importing and understanding the dataset
Understanding unbalanced data and converting class into factors
Dividing the dataset into equal parts with equal distribution of both classes
Imputing for the null values
Defining cross-validation, metrics, and preprocessing techniques
Understanding and implementing LOGISTIC REGRESSION and selecting important features
Applying Bayesian model along with recursive partitioning
Improve Logistic Results using Random Forest
Implementing boosting like AdaBoost and GradientBoostingClassifier.
Defining AUC-ROC score and getting in-depth knowledge of how it works
Using Gini, AUC, and KS for evaluating model
Understanding Recall, Precision and F1score
Model Improvement with Gaussian RBF kernel
Display Performance Reports and interpreting the same
Visualizing the result of each model via plot
The German credit dataset contains information on 1000 loan applicants. Each applicant is described by a set of 20 different attributes. Of these 20 attributes, seventeen attributes are discrete while three are continuous. The main idea is to use techniques from the field of information theory to select a set of important attributes that can be used to classify tuples. In this data science project, you will train a neural network using these attributes; the neural network is then used to classify tuples.