There are various statistical and machine learning modelling techniques that can be applied in solving a Data Science Problems. Few of these techniques can be listed as:
- Classification : Type of Supervised Learning. Dividing items into predefined categories. There are various algorithm available for clustering are Navie Bayes, Decision Tree, Logistic Regression (If a decision boundary/thershold is defined) and Support Vector Machine. For example predicting if there will be a rain tomorrow or not.
- Regression/Scoring: Type of Supervised Learning .This is task for predicting numeric value or score. For example predicting the rain fall that might occur tomorrow in-terms of inches. Different algorithms used for regression analysis are logistic regression (to predict probabilities) and linear regression.
- Clustering : Type of Unsupervised Learning This is a task of grouping items into most similar groups. Different algorithm available for clustering are K-Means, Hierarchical.
- Recommendations: Producing a list of recommendations in either of way a) based upon user's past experience and on similar experience faced by some other user. b) based on a comparison between the content of items and a user profile. For example Nearest Neighbour Alogrithm.
- Association Rules: This is about finding relationship among the variables. Finding correlations or reasons behind the effects observed in the data. Algorithm available for association rules mining are APRIORI.
