Concepts of Data Mining : Predictive analytics, Data Miners, Automation. CRISP DM :The Six Phases - Business/ Research Understanding , Data Understanding , Data Preparation, Modeling, Evaluation and deployment phase Machine learning :Machine learning systems : Supervised & Unsupervised Learning Techniques. Applications of Data Mining :Training & Testing, Modeling Window Concepts, Target Variable, Applications of Data Mining, Challenges

Visualization : Types of Variables, Distributions and Summary Statistics, Combining data files. Data Preprocessing :Data Integrity Check, Variable Standardization and Normalization, Extent of Missing Data, Segmentation. Automated Data Preparation :Outlier detection : Collective Outliers, Outlier Detection Methods. Sampling :Combining data files, Sampling for Static Data, Reservoir Sampling for Data Streams.

- k-means :Understanding clustering : Machine learning task, Algorithm, Assign and Update clusters, Choosing the appropriate number of clusters. Example – finding teen market segments using k-means clustering :Collecting, Exploring and preparing of data, Training a model on the data, Evaluating and improving model performance. Market Basket Analysis Using Association Rules :Understanding association rules : The Apriori algorithm, Measuring rule interest – support and confidence, Building a set of rules with the Apriori principle. Example – identifying frequently purchased groceries with association rules :Collecting, Exploring and preparing of data, Training a model on the data, Evaluating and improving model performance.

Program to predicting medical expenses using linear regression Program for correlation matrix Program to train a model on the data,

Implement the PageRank Algorithm Google Page rank calculation Program for Teleportation adjustments,