Big Data Practical

W3.CSS

Big Data Practical

Topics for practical
1. Matrices and Gaussian Elimination: Install, configure and run Hadoop and HDFS Implement word count / frequency program using Map Reduce
2. Vector: Implement an Mapreduce program that process a weather dataset
3. Matrices Transformation Exploring Hadoop Distributed File System (HDFS)
4. Determonant of matrix: Implement an application that store big data in Hbase/ Mongodb/ Pig using Hadoop/R
5. Eigen Vectors & Vectors: Implement a program in Pig
6. Testing of Matrices: Configure the Hive and implement the application in Hive
7. Simplex Method: Illustrate the working of Jaql
8. Dual Problem Solving: Implement Decision tree classification technique Implement SVM Classification technique
9. Regression Model: REGRESSION MODEL: Import a data from web storage. Name the dataset and do Logistic Regression to find out relation between variables that are affecting the admission of a student in an institute based on his or her GRE score, GPA obtained and rank of the student. Also check the model is fit or not require (foreign), require (Mass) MULTIPLE REGRESSION MODEL: Apply multiple regressions, if data have a continuous independent variable. Apply on above dataset.
10. Modelling and its Types: CLASSIFICATION MODEL: a. Install relevant package for classification. b. Choose classifier for classification problem. c. Evaluate the performance of classifier. CLUSTERING MODEL: Clustering algorithms for unsupervised classification. b. Plot the cluster data using R visualizations.