battery pa report.docx
- 1. Project report on Literacy Trend Analysis Submitted by: Submitted to: Abhishek Khanna Name:Bhanu Rathi Roll No.: 19BCS3851 (Designation) Chandigarh University, Gharuan
- 2. Certificate I hereby certify that the work which is being presented in this thesis entitled “Literacy Trend Analysis”, in partial fulfillment of the requirement for the award of degree of “B.Tech-CSE” submitted in APEX-CSE, Chandigarh University, Gharuan is an authentic record of my own work carried based on the extracted dataset from Kaggle dataset through R software. The matter presented in this report has not been submitted/available online. Name: Bhanu Rathi Uid:19BCS3851
- 3. Acknowledgement I want to express our special thanks of gratitude to our Planning Analytics in charge Mr. Abhisekh Khanna for his able guidance and support in completing our project. Along with our teacher, we would also like to thank IBM for providing us the wonderful and necessary material required for this project and emerging field of Planning Analytics at last, I would like to thank Chandigarh University for giving us the platform to learn and present our insights for this project. Ghauan November 2022 Bhanu Rathi
- 4. Abstract The given study is an analysis of the Literacy Trends dataset which consists of information of the trends in various aspects of Indian Education. The given dataset has vast data for years 2015-16 concerning the Indian Literacy and education, which has been divided state wise and further district wise. We perform analysis on the given dataset and get meaningful insights about the various trends in Indian education. In this study we make use of graphs which effectively show us information without having to read much making it easily understandable. The study isn’t very tech heavy and hence is suitable for any age group. All the visualizations have been possible because of IBM Cognos as a platform as it provides all the necessary tools to perform the tasks required. In the end we conclude our analysis, knowing the fields and areas where the Indian Education lacks behind.
- 5. List of Tables Sr. No Caption for table Page No. 1. Metadata 2. Index 3.
- 6. Table of Contents Certificate………………………………………………………………………………………….i Acknowledgement…...……………………………………………………………………………ii Abstract……………………………………………………………………………………...……iii List of Tables...……………………………………………………………………………………iv 1. Introduction to Planning Analytics 2. Introduction to IBM Cognos 3. Introduction to dataset 4. Evaluation criteria for creation of cubes and dimensions 5. Evaluation of dataset 6. Results 7. Conclusion and Future scope
- 7. Introduction to Planning Analytics Planning Analytics integrates business planning, performance measurement, and operational data to enable companies to optimize business effectiveness and customer interaction regardless of geography or structure. Planning Analytics provides immediate visibility into data, accountability within a collaborative process and a consistent view of information, allowing managers to quickly stabilize operational fluctuations and take advantage of new opportunities. IBM Planning Analytics helps you answer three critical business questions from a single platform: Flexible and timely planning answers "What should we be doing?“ World-class score carding and dash boarding answers "How are we doing?" Accurate reporting and analysis answers "Why are we doing it?" Use TM1 Architect and TM1 Performance Modeler to build models that use dimensions, cubes, and rules. You can create applications from cube views, assign workflow, and set up security. You can then deploy, administer, and maintain your applications. End users have many options to select from, depending on their requirements. IBM Planning Analytics clients include two Microsoft Excel offerings: TM1 Perspectives and IBM Planning Analytics for Microsoft Excel. IBM Planning Analytics Workspace is a Web-based interface that provides you with exciting ways to plan, create, and analyze your IBM Planning Analytics content. IBM Cognos Insight is a personal analysis tool that you can use to analyze almost any set of data, including data in your IBM Planning Analytics system. Thin clients are TM1 Web and TM1 Application Web. TM1 Performance Modeler, IBM Cognos Insight, and TM1 Application Web work with the TM1 Applications portal to interact with the underlying TM1 server. TM1 Operations Console is a Web-based operations tool that is designed to facilitate the monitoring, support, and management of TM1 servers, providing greater insight into day-to-day server operations.
- 8. Introduction to IBM Cognos A web-based comprehensive business intelligence package from IBM is called Cognos Business Intelligence. It offers a suite of tools for analytics, scorecarding, reporting, and keeping track of events and data. The software is made up of a number of parts that are made to satisfy the various information needs of a business. For example, IBM Cognos Framework Manager, IBM Cognos Cube Designer, and IBM Cognos Transformer are all parts of IBM Cognos. The elements described below are web-based components that can be accessed from most popular browsers Cognos Connection-Cognos Connection is the Web portal for IBM Cognos BI. It is the starting point for access to all functions provided with the suite. Query Studio-Query Studio allows simple queries and self-service reports to answer basic business questions. The report layout can be customized and data can be filtered and sorted. Formatting and creation of diagrams is also supported Report Studio-The Report Studio is used to create management reports Analysis Studio-Users can create analyses of large data sources and search for background information about an event or action Event Studio-The Event Studio is a notification tool that informs about events within the enterprise in real time Workspace- It is a web-based interface that allows business users to use existing IBM Cognos content (report objects) to build interactive workspaces for insight and collaboration Workspace Advanced-It is a web-based interface that allows business users to author/create reports and analyze information.
- 9. Introduction to dataset The Dataset we took is 2015-2016 dataset of our education system from Kaggle which has 616 columns and 38 rows. The Meta data of the dataset is given below: Field Name Description statcd State code ac_year Academic year statname State name area_sqkm Area tot_population Total Population urban_population Urban Population grwoth_rate Growth Rate sexratio Sex Ratio sc_population % SC Population st_population % St Population literacy_rate literacy rate male_literacy_rate literacy rate- Male female_literacy_rate literacy rate- Female distcd Districts: Districts blkcd Blocks: Blocks villages Villages: Villages clusters clusters: clusters schools schools: schools sch_1 Number of Schools: Primary with upper primary and secondary and higher secondary sch_2 Number of Schools: Upper Primary with secondary and higher secondary sch_3 Number of Schools: Primary with upper primary and secondary sch_4 Number of Schools: Upper Primary with secondary sch_5 Number of Schools: Secondary only sch_6 Number of Schools: Secondary with Hr. Secondary sch_7 Number of Schools: Hr. Secondary only sch_r_1 Number of Schools- Rural: Primary with upper
- 10. primary and secondary and higher secondary Evaluation criteria for the creation of cubes and dimensions CUBES IBM Planning Analytics with Watson stores the data that you need for planning and analysis in cubes. Each cube typically has a specific purpose. Suppose that you are analyzing sales; you have a cube that measures the sales for Sedan cars over time. The cube contains three dimensions: Measures, Product, and Month. Each measure, such as Sales, is organized by a product and a month. For example, the cell value 300000 represents the sales of Sedan-1 in the month of January (Jan). A cube has two or more dimensions. The number of dimensions that there are in a cube depends on the purpose of a cube. For example, a two-dimensional cube is useful as a lookup table; you can store exchange rates in a lookup table. DIMENSIONS Dimensions are lists of related members. Two or more dimensions are used to make a cube that can be used for planning and analysis. Typical dimensions a cube might contain are time, versions, regions, products, departments, measures. A member is an item in a dimension, so in a time dimension, you can have months, years, quarters. Each month, year, and quarter is a member. Dimensions can be a simple list with all members at the same level, or a dimension can be structured with members at different levels and with multiple hierarchies. How a dimension is structured depends on how you want the data to be represented.
- 11. Evaluation of dataset This dataset was taken considering the need of the analysis as we had to cover as many aspects of factors affecting literacy as we could. So the dataset is a vast one and consists of 16 columns with 680 rows. It consists of categorical and continuous values, though the categorical are very less in number i.e. 2 and rest 14 are continuous. It consists of 6 flag values which are either 0 or 1. The dataset had to be very clean in order for the analysis to be done successfully and get good results hence proper cleaning and transformation was done. At the end, there were no missing values making it a lot easier in the process. Any outlier or missing value would have resulted in absurd outputs which would have affected the results as a whole.
- 12. Results
- 15. Conclusion and Future scope We have successfully completed the analysis of the Indian Education Dataset and have derived meaningful insights. Some of these are given below. Every state has seen an increase in literacy, with some states seeing increases of more than 30%. States that previously had very high literacy rates are the ones that haven't made any progress. There are several underlying causes for this pleasing change, including increased awareness among the socially backward, government policies, general progress, and many others. India's literacy rate has risen overall, but there is still a long way to go before we can claim to be a fully literate nation. Similar number of years, and the goal of having a totally literate nation is not far away. The results that we get after analysis of this dataset can be used in the future to deal with problems relating to Indian Education. One can clearly see the fields where we lag behind and hence start working on it. We cover nearly every aspect of Indian Education and get insights using visualizations making it easy to understand for any nonprofessional.