GST Data Analysis

This project analyzes Goods and Services Tax (GST) data to understand registration patterns, state-wise trends, and payer behavior. The analysis uses Python with libraries like Pandas, Matplotlib, Seaborn, and Scipy.

Project Structure

• finaldata.xlsx: The dataset used for the analysis.
• gst_data_analysis.ipynb: (Optional) Jupyter Notebook containing the analysis code. You provided a python script, but a notebook is common.
• README.md: This file, providing an overview of the project.
• eligible_payers_distribution.png: (Optional) If the code generates this.
• compliance_by_return_type.png: (Optional) If the code generates this.
• top_states_by_registered.png: (Optional) If the code generates this.
• state_compliance.png: (Optional) If the code generates this.
• jk_trends.png: (Optional) If the code generates this.
• time_trends.png: (Optional) If the code generates this.
• compliance_trend.png: (Optional) If the code generates this.
• high_value_states.png: (Optional) If the code generates this.
• correlation_matrix.png: (Optional)
• eligibility_vs_registrations.png: (Optional)
• state_registration_comparison.png: (Optional)
• payer_distribution_pie.png: (Optional)
• compliance_by_return_boxplot.png: (Optional)
• actual_vs_predicted.png: (Optional)

Data Description

The dataset (finaldata.xlsx) contains GST-related information, including:

• srcStateName: Name of the state.
• srcYear: Year of the data.
• srcMonth: Month of the data.
• GST (Goods and Service Tax) Return Type: Type of GST return.
• Payer eligible for GST (Goods and Service Tax) registration: Number of payers eligible for registration.
• GST (Goods and Service Tax) Payers registered before due date: Number of payers registered before the due date.
• GST (Goods and Service Tax) Payers registered after due date: Number of payers registered after the due date.
• YearCode: Numerical code for the year.
• Year: Year.
• MonthCode: Numerical code for the month.
• Month: Month name.

Code Description

The provided Python script performs the following analysis:

1. Data Loading and Exploration:

   • Loads the data from finaldata.xlsx using Pandas.
   • Displays the first few rows, dataset dimensions, column information, missing values, and a statistical summary.
   • Fills missing values with 0.
   • Calculates Total_Registered payers.

2. State-wise Analysis:

   • Visualizes the top 10 states/UTs by the number of eligible GST payers.
   • Provides a summary of regional variations in payer eligibility.
   • Interactive Question: Which state do you think has the highest number of GST payers, and why might that be the case?

3. Time-based Trends:

   • Visualizes GST payer eligibility and registrations over time.
   • Summarizes fluctuations and trends in payer activity.
   • Interactive Question: Can you identify any seasonal patterns or significant changes in GST activity over the observed period? What factors might explain these trends?

4. Return Type Analysis:

   • Visualizes the total number of registered GST payers by return type.
   • Provides a table comparing eligible and registered payers for each return type.
   • Summarizes the contribution of different return types to overall registrations.
   • Interactive Question: What does the difference in GSTR-1 and GSTR-3 tell us about the filers?

5. Correlation Analysis:

   • Calculates and visualizes the correlation between key GST metrics (eligible payers, registrations before/after due date, total registrations).
   • Summarizes the relationship between payer eligibility and registration numbers.
   • Interactive Question: How does the strong correlation between eligible payers and total registered payers influence decision-making?

6. Registration Timing:

   • Compares the number of GST payers registered before and after the due date.
   • Summarizes the timely compliance of GST payers.
   • Interactive Question: What are the possible reasons for the number of registrations after the due date?

Key Findings

The analysis reveals the following key insights:

• State-wise Variation: States like Maharashtra and Uttar Pradesh have the highest number of eligible GST payers, indicating significant regional differences.
• Time-based Trends: GST payer eligibility and registrations show fluctuations over time, possibly influenced by seasonal or policy changes.
• Return Type Contribution: GSTR-3 filers contribute a larger share to total GST registrations compared to GSTR-1 filers.
• Correlation: A strong positive correlation exists between the number of eligible payers and the total number of registered payers.
• Registration Timing: Most GST payers register before the due date, indicating good compliance.
• The data is right skewed.

Visualizations

The analysis includes the following visualizations:

• Bar plots showing the top 10 states/UTs by eligible GST payers.
• Line plots showing GST payer trends over time.
• Bar plots showing total registered payers by return type.
• Heatmaps visualizing the correlation between GST metrics.
• Bar plots comparing before and after due date registrations.

Dependencies

The code requires the following Python libraries:

• Pandas
• Matplotlib
• Seaborn
• Scipy
• Statsmodels

Further Exploration

• Explore the reasons behind the state-wise variations in GST payer registration.
• Investigate the factors influencing the time-based trends in GST activity.
• Analyze the characteristics of businesses that file GSTR-1 versus GSTR-3 returns.
• Build a predictive model for registration compliance.
• Examine the impact of policy changes on GST registration and compliance.