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fatigue-driving-detection

Software quality management practice project

fault-detection-in-power-microgrid

This project presents the concept of fault detection and location in a Power Microgrid making use of the machine learning concepts like Artificial Neural Network. The electronic equipment used in microgrids is in essential need of more secure protection against short circuit faults. Due to the high current at the time of fault occurrence, the whole system might be de-energized which would have a severely negative impact on the entire system. A fault occurs when two or more conductors come in contact with each other or ground. Ground faults are considered as one of the main problems in power systems and account for more than 80% of all faults. An effective method to detect, isolate, and protect the power microgrid system against the effects of short circuit faults is extremely important. In this project we worked on a highly effective new method to protect the microgrid system using an Artificial Neural Network (ANN) that will detect and find the location of the fault before it affects other parts of the system. It would, therefore, be more dependable for microgrid protection. This protection network is distributed all along the power microgrid system protecting the entire microgrid network and is connected to the other protective devices in the system. This project focuses on detecting faults and identifying the location of the faults on electric power transmission lines in the power microgrid network.

finance-management-case-study

This was my case study for the subject of Finance Management. It included basic python programming, financial modelling and some microeconomics theory

flower_certification_demand_survey-2018

花き認証の受容性調査　flower and agricultural products certification. trust. WTP. consumer demand.

food-security

Our Project will shed some light on the nature of the challenges that agriculture and food systems are facing now and throughout the 21st century, and provides some insights as to what is at stake and what needs to be done. What emerges is that “business as usual” is no longer an option but calls for major transformations in agricultural systems, in rural economies and in how we manage our natural resources.

forestry-calculator

A script for parsing individuated forest inventories in .csv format and estimating relevant forestry, architectural and environmental factors.

fraud-detection-on-accounting-financial-systems

frauddetection

Accounting Fraud Detection Using Machine Learning

full-cycle_ml_solution

Create an automatic solution for evaluating the results of financial and economic activities of public sector enterprises

fyp

Reference code for methodology reproduction of my bachelor thesis: Analysis of international trade data - correlating Chinese economic influence with Chinese United Front influence in Oceania

gdp-analysis-niti-ayog-

NITI Aayog: Background NITI Aayog (National Institution for Transforming India) is a policy think tank of the Government of India; it provides strategic inputs to the central and the state governments to achieve various development goals. In the past, NITI Aayog has played an important role in initiatives such as Digital India, Atal Innovation Mission and various agricultural reforms and have designed various policies in education, skill development, water management, healthcare, etc. NITI Aayog was established to replace the Planning Commission of India, which used to follow a top-down model for policy making, i.e., it typically designed policies at the central level (such as the 5-year plans). On the other hand, NITI Aayog designs policies specific to the different states or segments of the economy. Finance Minister, Arun Jaitley, made the following observation on the necessity of creating NITI Aayog, "The 65-year-old Planning Commission had become a redundant organisation. It was relevant in a command economy structure, but not any longer. India is a diversified country and its states are in various phases of economic development along with their own strengths and weaknesses. In this context, a ‘one size fits all’ approach to economic planning is obsolete...". Project Brief You are working as the chief data scientist at NITI Aayog, reporting to the CEO. The CEO has initiated a project wherein the NITI Aayog will provide top-level recommendations to the Chief Ministers (CMs) of various states, which will help them prioritise areas of development for their respective states. Since different states are in different phases of development, the recommendations should be specific to the states. The overall goal of this project is to help the CMs focus on areas that will foster economic development for their respective states. Since the most common measure of economic development is the GDP, you will analyse the GDP of the various states of India and suggest ways to improve it. Understanding GDP Gross domestic product (GDP) at current prices is the GDP at the market value of goods and services produced in a country during a year. In other words, GDP measures the 'monetary value of final goods and services produced by a country/state in a given period of time'. GDP can be broadly divided into goods and services produced by three sectors: the primary sector (agriculture), the secondary sector (industry), and the tertiary sector (services). It is also known as nominal GDP. More technically, (real) GDP takes into account the price change that may have occurred due to inflation. This means that the real GDP is nominal GDP adjusted for inflation. We will use the nominal GDP for this exercise. Also, we will consider the financial year 2015-16 as the base year, as most of the data required for this exercise is available for the aforementioned period. Per Capita GDP and Income Total GDP divided by the population gives the per capita GDP, which roughly measures the average value of goods and services produced per person. The per capita income is closely related to the per capita GDP (though they are not the same). In general, the per capita income increases when the per capita GDP increases, and vice-versa. For instance, in the financial year 2015-16, the per capita income of India was ₹93,293, whereas the per capita GDP of India was $1717, which roughly amounts to ₹1,11,605. India ranks 11th in the world in terms of total GDP; however, it lies at the 139th position in terms of per capita GDP.

gender_inequality_drivers

An analysis of the relationship between test achievement score disparity by gender and other indicators of education, economics, and gender inequalities such as GDP, general gender inequality index, share of gender in the workforce, share of secondary-school educated by gender and the like.

gender_inequality_drivers-1

An analysis of the relationship between test achievement score disparity by gender and other indicators of education, economics, and gender inequalities such as GDP, general gender inequality index, share of gender in the workforce, share of secondary-school educated by gender and the like.

github-slideshow

A robot powered training repository :robot:

global-financial-crises-and-economic-growth-evidence-from-asean-economies

global-power-plant-database

A comprehensive, global, open source database of power plants

hnc-ma-thesis-tfp-fdi-china-asean

These are the files I used to perform the data analysis in my master's thesis. This repository contains the original CSV files I collected from various sources and the R code I wrote to clean and analyze them. Each step is documented carefully with R markdown files so as to ensure maximum reproducibility.

hr-analytics-prediction--salary-prediction-ml-project

Predictive HR Analytics:Hr analytics equips HR leaders with the necessary data to improve HR functions and the employee experience. With the continuous influx of tech innovations challenging the workplace, managing employees intelligently and supporting them through the demanding employee lifecycle is essential. Enhancing HR strategies through HR analytics can promote job satisfaction and lead to a healthy company culture comprised of engaged individuals.

hsc_moderation

Analysis of NSW Education Standads Authority moderation of school assessment marks in the case of tied first place

ias-106

Intermediate Microeconomic Theory

increase-lead-conversion-rate-for-online-education-company

Increase the lead to consumer conversion rate for X Education, an online education company. Analyse dataset using python and present insights to management.

indian-asean-trade-data-analysis

To analyze which area of production needs more attention for exports and reduce imports with relevancy ASEAN countries.

industrialeconomy

Industrial economy homework

information-distortion

Supplementary codes for "Multi-Period Choice on Distorted Information", term paper for Intermediate Microeconomics

iot-based-fire-alarm

Get notified when fire is detected in the room so that we can save many lives.

juanjishengjingwangluo

kubenrobotics

Material for Education in Robotics at Kuben Upper Secondary School

landforestryinformationparse

토지 임야 정보 api 가져오기(XML)

leadership

Leadership and management ideas

leaf-disease-detection-using-image-processing

Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper introduces an efficient approach to identify healthy and diseased or an infected leaf using image processing and machine learning techniques. Various diseases damage the chlorophyll of leaves and affect with brown or black marks on the leaf area. These can be detected using image prepossessing, image segmentation. Support Vector Machine (SVM) is one of the machine learning algorithms is used for classification. The Convolutional Neural Network (CNN) resulted in a improved accuracy of recognition compared to the SVM approach.