Data Analyzing Immigration to Canada using Predictive Analysis (Multiple Linear and Non-Linear Regression)
P. Deeraj1, K. Hari Kiran2, M. Hemanth Varma3, J. Siva Priya4
1P. Deeraj, Department of Computer Science Engineering SRM Institute of Science and Technology, Chennai, India.
2K. Hari Kiran, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai, India.
3M. Hemanth Varma, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai, India.
4J. Siva Priya, B. E, M.E, Assistant Professor, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2686-2690 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2281129219/2019©BEIESP | DOI: 10.35940/ijeat.B2281.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The immigration to Canada impacts the government in different manner like increase in population, waste, fossil fuel and it also benefits like increase economic growth, trade which will increase the GDP value of Canada, increase in workforce of country, open market, globalization, technologies and adapt to different cultures, food, and people[1] . These would result in a decrease in discrimination and aware about their rights and duties. The immigrants are more interested in entrepreneurship than others [2]. Which would impact increase in development in the country. The work explores the impact of immigration to Canada from all around the world. The top 5 countries that immigrate to Canada is analyzed by using Jupyter notebook. The prediction is done only for the top 5 countries that immigrate to Canada by analyzing the previous immigrants from 1980 – 2013. The multiple linear regression is used to analyze the data.
Keywords: Entrepreneurship, Multiple Linear Regression, Globalization, Workforce.