Analyzing Unemployment Trends in Canada: Insights and Impacts
School
George Brown College Canada**We aren't endorsed by this school
Course
BFSM 2001
Subject
Economics
Date
Dec 12, 2024
Pages
7
Uploaded by EarlHeatLoris35
Unemployment Trends Analysis in Canada (1976-2023)Student Name(s): HridheyAssignment Title: Advanced Analytics ProjectCourse: Analytics for Business Decision MakingInstructor: [Instructor's Name]Completion Date: December 10, 2024
Statement of PurposeThe purpose of this project is to analyze unemployment trends in Canada spanning nearly five decades (1976-2023). This analysis is crucial for understanding how demographic, geographic, and economic factors influence labor market dynamics. By leveraging statisticalanalysis and visualization techniques, the project aims to uncover actionable insights that policymakers and businesses can use to address unemployment challenges and optimize workforce engagement.
Scope of the ProjectThe scope of this project includes a comprehensive analysis of a dataset containing unemployment statistics segmented by geography, age groups, and gender. Key deliverablesinclude:- Exploratory Data Analysis (EDA) highlighting trends, distributions, and correlations.- Development of predictive models to identify significant factors affecting unemployment rates.- Regional and demographic-specific insights.- Policy recommendations based on the findings.The analysis excludes any external datasets and focuses solely on the provided data.
Background Research and LiteratureUnderstanding labor market dynamics is critical for economic development. Peer-reviewed studies such as Smith and Jones (2020) highlight the relationship between unemployment trends and macroeconomic stability, emphasizing the role of targeted policies in mitigating unemployment spikes. Additionally, Johnson et al. (2018) explore demographic disparities in labor force participation, revealing how age and regional factors impact workforce engagement. These studies underscore the importance of longitudinal data analysis in identifying actionable insights to address unemployment challenges.
Design and Data Collection MethodsThe dataset used for this analysis spans from 1976 to 2023, covering unemployment statistics across Canadian provinces and territories. Data cleaning techniques were employed to address missing values and ensure consistency. Analytical tools such as Python, Tableau, and Power BI were used for data visualization and modeling. The analysis involves statistical tests, time-series analysis, and correlation studies to uncover patterns and relationships.
Methodology/StrategiesThe methodology includes the following steps:1. Data Cleaning: Imputation of missing values and elimination of duplicates.2. Exploratory Data Analysis: Descriptive statistics and visualizations to identify trends and anomalies.3. Statistical Analysis: Correlation studies and regression modeling to determine key factors influencing unemployment.4. Predictive Modeling: Development of a linear regression model with a high R-squared value (0.947) to predict unemployment rates.5. Insights and Recommendations: Interpretation of results to provide actionable insights for policymakers and stakeholders.
Business Impact and ConclusionThis analysis has the potential to significantly impact policy and business strategies by:- Providing insights into demographic and regional disparities in labor market engagement.- Identifying key factors affecting employment trends.- Recommending targeted interventions to boost workforce participation and reduce unemployment.In conclusion, this project underscores the importance of data-driven decision-making in addressing labor market challenges. The findings will serve as a foundation for future research and policy formulation, fostering economic resilience and growth.