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-american-university-data-ipeds-dataset

2021

The “Export” of Taiwanese Law to Japan — the Citations of Taiwanese Precedents and Regulations in Japanese Litigations

2021_rcep_carbon_convergence

2022_covid

covid v2.0: 2022

3yp_leo_powergroup

2021-2022 LEO Project Power Group Common Repository

a.i_global_surveillance

Python Web Scraping Project, Data Prep, Data Visualization, Highlighting the under-reported grand scope of China's One Belt, One Road Initiative and how technology can play a detrimental role in empowering authoritarian regimes around the world.

ab-testing-of-fast-food-marketing-campaign

accounting-fraud-prediction

accountingfrauddetection

african-economies-debt-data-analysis

In the past 20 years, China has poured in upwards of $150 billion into African nations for Infrastructure, Resources and Agricultural projects. These funds have given the economies a great boost but they come with their costs. Our analysis looks at the boost to development caused by these investments and the impact of such high loans on the countries' economies

agricultural-management-system

Online platform for farmers to get best price of their product via directly connecting with consumers using HTML,CSS, Javascript, PHP, MySQL.

agriculturalsustainability

The initial question posed for the Analytic report and Research Proposal Capstone is to determine if smallholder agricultural farmers, businesses, and entrepreneurships are lucrative and sustainable in Sierra Leone, Senegal, Guinea-Bisaau and/or Guinea. The question stems from US Government Agencies, International Development sector, and Non-Government Organizations redirecting and focusing funds into the agricultural sector of developing countries. There is the logical assumption that developing countries need a sustainable and growing agricultural market; however, in the United States, 2 percent of the population are farmers but feed upwards to 330 million people annually (source: American Farm Bureau Federation). Based on this assumption, a smaller percentage of farmers would be able to sustain either of the four countries. By analyzing the growth of the agricultural sector in four Western African countries and the effect on the country's Gross Domestic Product this capstone will determine which West African Country would be best for an agricultural smallholder farmer, business or entrepreneurs.

agriculture-website-with-codeigniter

it contains the whole agriculture system regarding sale , purchase with customer, products of companies.

aiif

Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O'Reilly) by Yves Hilpisch.

air-quality-prediction-using-machine-learning-

Air pollution has been a severe problem in the major smart cities. Air quality of a certain region can be used as one of the major factors determining pollution index also how well the city’s industries and population is managed. Air pollution has remained a major challenge for the public and the government all over the world. Air pollution causes noticeable damage to the environment as well as to human health resulting into acid rain, global warming, heart diseases and skin cancer to the people. To overcome this, we are using a machine learning approach for predicting the air quality and thus we can take appropriate measures to reduce it. In This Project we will be making an Air Quality Prediction system which will predict the quality of air based on a particular dataset. Technology we will use is Machine Learning in which we will train the dataset based on Machine Learning Algorithms and predict the quality of air.

amurobohack-smart-city

Smart Traffic Lights , using image processing and object detection

analysis-of-associate-degree-offered-by-postsecondary-education

The Integrated Postsecondary Education Data System (IPEDS), established as the core postsecondary education data collection program for NCES, is a system of surveys designed to collect data from all primary providers of postsecondary education.

analysis-of-belt-road-trade-effect

The“Belt and Road”is aimed to connect regions or countries,build a trading community and promote the free and inclusive development of the global economy.Based on the flow network model,this paper compares the global trade network with the“Belt and Road”trade network from two perspectives:community structure and core-edge structure,and describes the relationship between the economic structures of China and the United States and two networks’differences by correlation analysis.The study found that:1)the structures of the two trade networks are relatively stable.Compared with the global trade network,the“Belt and Road”trade network has regional discontinuities in the distribution of communities and reflects the strategic deployment of the“Belt and Road”;2)the ranking of node’s coreness has the characteristics of power law distribution,but the core-edge structures of the two networks develop differently;3)the trade strengths,corenesses,and economic strengths of China and the United States are all positively related to the trade network’s integrity,but the correlation between the trade intensity of the countries with different development levels and the core-edge structure is different.

analyze-international-debt-statistics

It's not that we humans only take debts to manage our necessities. A country may also take debt to manage its economy. For example, infrastructure spending is one costly ingredient required for a country's citizens to lead comfortable lives. The World Bank is the organization that provides debt to countries. In this project, you are going to analyze international debt data collected by The World Bank. The dataset contains information about the amount of debt (in USD) owed by developing countries across several categories. You are going to find the answers to questions like: What is the total amount of debt that is owed by the countries listed in the dataset? Which country owns the maximum amount of debt and what does that amount look like? What is the average amount of debt owed by countries across different debt indicators? The data used in this project is provided by The World Bank. It contains both national and regional debt statistics for several countries across the globe as recorded from 1970 to 2015.

analyzing-students-performance

Analyzing Students' Performance in Secondary Education of Portugal Schools

ann-structural-mechanics

Projects for application of Artificial Neural Networks in Structural Mechanics

applied_micro

Applied microeconomics project

appliedmicroeconomics

aqualord

Data for Water Resource Management

as-aigfaq

Using OpenAI API to generate FAQ from descriptions

assignment-06-logistic-regression

Assignment-06-Logistic-Regression. Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no") Attribute information For bank dataset Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no") 8. Missing Attribute Values: None

baidu_bigdata_competition

The 6th Baidu & Xi’an Jiaotong University Big Data Competition and the 2nd IKCEST “The Belt and Road” International Big Data Competition

bank_marketing-_prediction

Data Description Feature 1.age |int64| age in years 2.job | object | type of job (categorical: ['management' 'technician' 'entrepreneur' 'blue-collar' 'unknown' 'retired' 'admin.' 'services' 'self-employed' 'unemployed' 'housemaid' 'student']) 3.Salary |int 64| 4.marital | object | marital status (categorical: ['married' 'single' 'divorced']) 5.education | Object | education background (categorical: ['secondary' 'tertiary' 'primary' 'unknown']) 6.targeted |object| ["yes","No] 7.default | Object | has credit in default? (categorical: ['no' 'yes']) 8.balance | int64 | Balance of the individual 9.housing | object | has housing loan? (categorical: ['yes' 'no']) 10.loan | object | has personal loan? (categorical: ['no' 'yes']) 11.contact | object | contact communication type (categorical: ['unknown' 'cellular' 'telephone']) 12.day | int64 | last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 13.month | object | last contact month of year (categorical: ['may' 'jun' 'jul' 'aug' 'oct' 'nov' 'dec' 'jan' 'feb' 'mar' 'apr' 'sep']) 14.duration | int64 | last contact duration, in seconds (numeric) 15.campaign | int64 | number of contacts performed during this campaign and for this client 16.pdays | int64 | number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 17.previous | int64 | number of contacts performed before this campaign and for this client 18.poutcome | object | outcome of the previous marketing campaign (categorical: ['unknown' 'other' 'failure' 'success'])` 19.['response'] |object| ['no' 'yes'] ########## Target Variable Label 1.response|object| heas the client subscibed a term deposit(binary'yes','no') Categorical columns job marital education targeted default housing loan contact month poutcome response Numerical Columns age salary balance day duration campaign pdays previous Exploratory Data Analysis find unwanted Columns find missing values Find features with one values Explore the Categorical Features Find Categorical Feature Distribution Relationship between Categorical Features and Label Explore the Numerical Features Find Discrete Numerical Features Relation between Discrete numerical Features and Labels Find Continous Numerical Features Distribution of Continous Numerical Features Relation between Continous numerical Features and Labels Find Outliers in numerical features Explore the Correlation between numerical features Find Pair Plot Check the Data set is balanced or not based on target values in classification

bert-tickets

[NeurIPS 2020] "The Lottery Ticket Hypothesis for Pre-trained BERT Networks", Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin

biopesticide_startup_ms

Market Segmentation analysis was done on Indian agricultural datasets to identify the best region and state for a Biopesticide startup to enter into the market in regards to their first release. Different factors were analysed through data visualisation and clustering for the company to make a choice based on their products.