1. Data Pre-processing on Isurance.csv dataset(Data_Mining.ipynb).
2. Handling missing values in dataset(Missing_values.ipynb).
3. Binning and scaling in dataset.

1. Insurance.csv is the datset for pre-processing.
2. Wholesale customers data
3. Pima-indians-diabetes.data.csv (https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv")

1. KNN (Lazy learning and Non Parametric Learning)
2. HMM (Hidden Markov Model and Viterbi)
3. K_Means Clustering (Hierarchial CLustering)
4. Random Forest Classifier

1. Accuracy
2. Confusion Matrix
3. Precision
4. recall (Sensitivity/ True Positive Rate)
5. f1 score
6. ROC curve
7. Precision -recall curve
8. Average Precision

Internal goodness metrices:- Internal metrics do not use any external information and assess the goodness of clusters based only on the initial data.

External metrics:- They use the information about the known true split.

1. Adjusted Rand Index(ARI):-
2. Adjusted Mutual Information(AMI)
3. Homogenity, Completeness, V-Measure
4. Silhouette