Customer Relationship Management System in python
Customer Relationship Management System in python
PROJECT ABSTRACT:
The Customer Relationship Management System is built to control the company interactions with current and future customers. The system can be used to organize, automate, and synchronize the sales information. The sales manager needs to keep track of the current and future customers to synchronize sales. The Customer Relationship Management System is built upon the latest technologies of java and hibernate and thus is very strong and communicative to use.
Through this Customer Relationship Management System application the customers get all the updates of the company products and events too. This is a great system for an organization to interact with their clients and customers. This relationship handling system between the customers and the company members would be a great bond creator and of a great profit app for both of them.
Existing System
The user needs an Android mobile to access this application. Manually it is very difficult to keep record of all the leads. Using the app, the manager can easily schedule meeting with their customers and keep track of their targets.
Existing work such as giving updates to the clients, telling about new products and new services, upcoming events and everything are done via telephonic calls mainly. This may sometimes lead to loss of important information.
Proposed System
The user can send emails or SMS to the customers from the app. When the employee logins to the system, a notification will be send about the upcoming meetings with the lead. The admin have the rights to add/modify employee, provide ID and password to the employee to access the system.
The proposed Customer Relationship Management System is totally automated and admin just need to update once in the module and all the information is distributed among the clients automatically. The customers does not require to install this system in order to get update related to company, daily via mail or sms.
Customer Relationship Management System
Modules
Given are the modules for android customer relationship management system:
1.) Customer Module: What all details need to be send to the customers and clients and their names, address and other relevant details related to the customers are added into this module. Also via the contact details saved into this module, customers are contacted by the managers of the company for regular updates.
2.) Management Team Module: This module is mainly a management module for all the task done in this system. Task such as activating new technologies, submitting up of queries by customers, answering their questions, forwarding them to relevant query head and so on is done in this module.
3.) Company Head Module: This module is basically designed for the head of the company and he/she can perform their jobs using this module by making their account in the module.
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.
• 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 .
• Tweepy: It is used in accessing the Twitter API by establishing the connection and to gather tweets from Twitter. 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.
• scikit-learn: It is a simple and efficient tool for data mining and data analysis.
• matplotlib python library which generates plots, histograms, power spectra, bar charts, etc.
In this work matplotlib.pyplot module is used to plot the metrics.
• 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.
• 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 a system of supervised learning and anomaly detection system a system of unsupervised learning. What is the difference between supervised learning and unsupervised learning? The answer is that supervised learning studies labeled datasets. They use labeled datasets to train and to render it accurate by changing the parameters of the learning rate. After that, they apply parameters of learning rate to the dataset, the techniques that implement supervised learning such as multilayerperceptron (MLP) to build the model based on the history of the database. This supervised learning has a disadvantage, since if new fraud transactions happen that do not match with the records of the database, then this transaction will be considered genuine. While, unsupervised learning acquires information from new transactions and finds anomalous patterns from new transaction. This unsupervised learning is more difficult than supervised learning, because we have to use appropriate techniques to detect anomalous behavior.
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
Other Information
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