NikitaMiraje
commented
2 weeks ago Hi,
I would like to work on this issue. @Akshat111111 Could you please assign it to me?
In-Scope Tasks:
- Load and preprocess the dataset.
- Perform univariate analysis (e.g., distribution of expenses and revenue).
- Conduct bivariate analysis to explore relationships between variables (e.g., expenses vs. revenue).
- Execute multivariate analysis (e.g., correlation heatmap).
- Analyze categorical data.
- Handle missing values appropriately.
- Detect and analyze outliers.
-
Visualize findings using various plots.
Out-of-Scope Tasks:
- Building predictive models or machine learning algorithms.
- Conducting hypothesis testing or advanced statistical analysis.
- External data collection or enrichment.
Dataset - https://www.kaggle.com/datasets/prasad22/government-financial-statistics-dataset
Thank you!!
Akshat111111
Is your feature request related to a problem? Please describe.
Dataset I'll use :- https://www.kaggle.com/datasets/prasad22/government-financial-statistics-dataset
Government Finance Statistics (GFS) database contains Fiscal data for all reporting countries in the framework of the [Government Finance Statistics Manual (GFSM) 2014]. It includes detailed data on expenditures and revenue and includes data for the general government sector and its subsectors (ex. The central government, state government, local government, and social security funds). The [Historical Public Debt Database] contains Debt-to-GDP ratios for 187 countries. Exploratory Data Analysis (EDA) on the Government Finance Statistics and Historical Public Debt Database can reveal trends in revenues, expenditures, and debt-to-GDP ratios across different countries and time periods. It helps identify fiscal patterns, anomalies, and correlations, providing insights into the economic health and policy effectiveness of nations. EDA can also highlight how different levels of government contribute to overall fiscal performance and assist in forecasting future fiscal trends.
Describe the solution you'd like I will usge libraries like numpy , matplotlib , pandas to ensure through data analysis on the mentioned dataset .
Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.
Additional context Add any other context or screenshots about the feature request here.
@Akshat111111 kindly assign this issue to me.