Student result prediction using SVM algorithm in python

 

Student result prediction using SVM algorithm in python

 

 PROJECT ID: PYTHON27

 

PROJECT NAME: Student result prediction using SVM algorithm in python

 

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.

What is Support Vector Machine?

An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible.

In addition to performing linear classification, SVMs can efficiently perform a non-linear classification, implicitly mapping their inputs into high-dimensional feature spaces.

What does SVM do?

Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

Let you have basic understandings from this article before you proceed further. Here I’ll discuss an example about SVM classification of cancer UCI datasets using machine learning tools i.e. scikit-learn compatible with Python.

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Setting up Software Environment

Python is a high level, interpreted, interactive and object-oriented scripting language.

Python is designed to be highly readable and has fewer syntactical constructions than other languages. Python is used in the development of this model. In this experiment, the following python libraries are used to develop the machine learning models:

• NLTK: It is a python package which works with human language data and provides an easy-to-use interface to different lexical resources like WordNet and text processing libraries. These lexical resources are used for classification, tokenization, stemming, tagging, parsing, and semantic reasoning [23].

• Pandas: It is a python package which acts as a data analysis tool and deals with data structures. Pandas carry out entire data analysis workflow in Python without having to switch to a more domain specific language like R [46].

• Tweepy: It is used in accessing the Twitter API by establishing the connection and to gather tweets from Twitter [24]. This module is used to stream live tweets directly from Twitter in real-time.

• Numpy: NumPy is the fundamental package for computing with Python. It is used to add support to multi-dimensional arrays and matrices, with a large collection of high-level mathematical functions [47].

• scikit-learn: It is a simple and efficient tool for data mining and data analysis [47]. • matplotlib python library which generates plots, histograms, power spectra, bar charts, etc.

In this work matplotlib.pyplot module is used to plot the metrics [47].

• Gensim It is used to automatically extract semantic topics from documents, as efficiently as possible. Gensim is designed to process raw, unstructured text data. The algorithms in Gensim, such as Word2Vec where it automatically discovers the semantic structure of phrase by examining statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation and queried for topical similarity against other documents [48].

• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research [49]

 

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

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