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A Project Plan
On
Analysis on the growth of Indian Economy
Submitted to
Amity University Uttar Pradesh
Amity of School of Engineering & Technology
Noida, Uttar Pradesh
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF
BACHELOR OF TECHNOLOGY IN COMPUTER SCIENCE AND ENGINEERING
2012- 2016
Under guidance of: Submitted by:
Mrs. Rajni Sehgal Krishna Kumar
Associate Professor A2305212243
4th year, 7th semester
CERTIFICATE
On the basis of declaration submitted by Krishna Kumar of B.Tech CSE, I hereby
certify that the project titled “Predictive Analysis on Indian Economy” which is
submitted to Department of computer Science, Amity University, Uttar Pradesh, Noida,
in partial fulfillment of the requirement for the award of the degree of Bachelor of
Technology in Computer Science and Engineering, is an original contribution with
existing knowledge and faithful record of work carried out by her under my guidance
and supervision.
To the best of my knowledge this work has not been submitted in part or full for any
degree or diploma to this university or any other.
Date: ______________________ ____________________________
Ms. Rajni Sehgal
Department of Computer Science and
Engineering
ASET, Noida
ii
DECLARATION
I, Krishna Kumar, student of B.tech (CSE) hereby declare that the project title
“Predictive Analysis on Indian Economy” which is submitted by me to Department of
CSE, Amity School of Engineering and Technology, Amity University Uttar Pradesh,
Noida, in partial fulfillment of requirement for the award of the degree of Bachelor of
Technology in CSE, has not been previously formed the basis for the award of any
degree, diploma or other similar title or recognition.
Date: _____________________ _____________________
Krishna Kumar
A2305212243
7CSE-4 (2012-2016)
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TABLE OF CONTENTS
S.No Contents Page No.
1. Introduction 1
1.1 Purpose of Plan
1.2 Background Information/ Available Alternatives
1.3 Project Goals and Objectives
2. Scope 3
2.1 Scope Definition
2.2 Project Budget
3. Constraints 4
3.1 Project Constraints
4. Project Management Approach 5
4.1 Project Timeline
4.2 Project Roles and Responsibilities
5. Risk Assessment 6
5.1 Project Risk Assessment
6. Literature Review 7
1. INTRODUCTION.
1.1 Purpose of Plan
In order to apply predictive analysis on the Indian Economy, we would require
necessary data sets in order to pull in the required raw data. Once we retrieve the data
sets, we would require to clean the data in order to be able to perform Exploratory Data
Analysis to get the look and feel of how the data presents itself. Once we’re done with
EDA, we will move on to the more complex part of determining which statistical model
would work best for our given data set. Once we find a suitable data model, we would
able to infer and henceforth predict how the Economy has changed over the past into the
present, and hopefully we will be able to predict how it will be affected in the future.
1.2 Background Information/Available Alternatives
The ‘economic liberalization in India’ refers to the going in the recent times economic
liberalization, which had its start in 1991, regarding the country's economic policies,
with one of its goals of making the economy of the country more oriented towards the
market and expand the role of private and foreign investment in a more rigorous
manner. To the point changes include a reduction in import tariffs, also deregulation of
markets, reducing taxes, and more of foreign investment. Liberalization has been given
credit by its components for the high economic growth recorded by the country in the
time periods of 1990s and 2000s. The opponents have blamed it for increased poverty,
inequality and economic degradation.
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2. GOALS AND OBJECTIVES
2.1 Project Goals and Objectives
Analyze data sets of key Indicators of the change in Economy.
Infer past shifts through a time-series plot
Identify the type of classification problem and apply necessary machine learning
algorithms.
Predict upcoming values of GDP using the machine learning techniques
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3. SCOPE
3.1 Scope Definition
The project will examine GDP data from a government website, after performing data
analysis on the data, we will be able to create python modules which will perform
induvial functions of applying required machine learning algorithm. The end result
would be a prediction outcome that will be obtained from one of the methods of the
algorithm.
3.2 Scope Budget
The only resource considered for the scope budget should be Time. Everything else is
being performed by Open Source tools.
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4. CONSTRAINTS
4.1 Project Constraints
Data obtained is not of sufficient measure, for more accurate predictions we
need increased amounts of data.
Key Indicators are variable in nature; they cannot be solely relied. There are
many innate factors that affect GDP in different ways.
Data visualizing is very mundane as we cannot expect outliers within the data.
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5. PROJECT MANAGEMENT APPROACH
5.1 Project Timeline
PERIOD RECOMMENDED
COMPLETION
Mid of September Topic Decided
Started Project Planning
Goals & Objectives figured out
Analyzing the problem/topic
Beginning of October Started the literature review. Reading different research papers related to the topic.
Started obtaining data, cleaning it and applying Exploratory Data Analysis
October end Detailed data analysis. Extensive data visualization.
November Attempt to classify the problem to find an appropriate machine algorithm
Mid November Apply machine learning algorithm and find desired output.
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6. RISK ASSESSMENT
Risk Assessment is an intrinsic part of software development and prepares the organization for future costs and failures which can occur. Risk analysis or assessment is not about making large amounts of paperwork, but instead it is about distinguishing sensible measures to control the risks in our project. One of the best modes to battle risks is to form a broad plan which can prevent the occurrence of failure. Expected problems have the advantage of having preset solutions. Unexpected problems tend to be very problematic since there is no planned method of resolving them.
We have not used any fixed rules for planning risk assessment or analysis on our project; instead we have followed some general principles.
For ensuring that risk assessment or analysis is carried out correctly, we have used the following few points to identify the risks:
1. Identification of risks.2. Decide who/what will be harmed and in what way. 3. Evaluation of the risks.4. Results findings should be recorded and then it should be implemented.5. Reviewing of the assessment and update accordingly.
So during the coding phasing and testing phase of the project following risks should have kept in mind and taken care of.
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6.1 Project Risk Assessment
Risk Risk Level
(L/M/H)
Likelihood of Event Mitigation Strategy
Program size M Not Likely. Careful designing of modules and similar coding.
Error H Very Likely. Proper testing is done by unit testing at the time of coding.
Slow L Likely. Ensure a decent system to run tests on and a good connection at the same time.
Power consumption L Likely It will be managed in the maintenance phase.
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7. LITERATURE REVIEW
Gross Domestic Product (GDP)
Gross Domestic Product can be defined as a quantitative measure of the values of every
final goods and features services that are produced in the specified time period usually
reffered to as quarterly or yearl. The estimates of GDP are most commonly used to
figure out the performance in terms of economy and living in terms of standard of a
given country so that we can make comparisons with countries .
It cannot be relatable as a measure which is complete in terms of estimating economic
activity. It merely accounts for the determinant output and the value which is added at
each stage of necessar production, it doesn’t involve the whole some of output aside the
whole production phase. It forcefully leaves out transactions such as business to
business in the beginning and median stages of production.
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Data Analysis
If we come in terms of defining data analysis, we can express it as a process which
involves doing an inspection, followed by cleaning, transforming to the desired
formations and finally modelling a set of data obtained from a trusted source which can
be a website or a github repository. The main objective of finding out meaningful
information, to suggest inferences and to give advices for decision making. Data
analysis is known to have varied facets and ability of approaches, encapsulating very
varied techniques which come under a diversity of names in different domains.
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Machine Learning
The fact that Machine Learning is a field which is part of Computer Science is known to
every novice out there. It is also known that the field of machine learning has evolved
from the studiosity of recognition of pattern and learning theory through computation in
the field of Artifical Intelligence. The field of machine learning goes thourgh a journey
that studies and constructs algorithms that can learn from data sources of varied origins
and make predictions. The algorithms considered for machine learning are known to
build models from the input dataset, enabling them to make data driven assumptions.
Approaches to machine learning are of various types, some of them are listed below:
Decision tree learning
Association rule learning
Artificial neural networks
Clustering
Bayesian networks
Reinforcement learning
Representation learning
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8. DISCUSSION AND CONCLUSION
In this project we have examined economic data to analyze the GDP and viusalize the
growth of the Indian Economy, from the year 1951 to the year 2012. The GDP has
grown at a linear manner. Even though GDP is not a very dependable key indicator to
analyze economy, yet we can easily visualize how the GDP has grown into the recent
times. Also we closely can examine which industries are contributing majorly to the
GDP.
After analysis, we have come to the conclusion that the problem in hand is a regression
problem, and we can apply necessary regression algortihms to find the outcome of the
project. Since the problem is a regression problem, we can easily determine the
regression line, this regression line proves the fact that the GDP has grown in a rather
linear fashion over the years.
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