This project showcases the Classification using various ML Algorithms and Visualization of the data. It includes:
Visualizing the data
BoxPlot
Histogram
ScatterPlot
Cross Tabulations
Training and modeling the data
Splitting a dataset into training and testing
Classification using Supervised Algorithms
KNN
Decision Tree
Random forest
Support Vector Machine
Logistic Regression
Naive Bayes
Evaluating Performance of the Model
Confusion Matrix
Kappa Score
F - measure
ROC Curve
Accuracy
Recall
AUC
Error Rate
Precision
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