Data Processing & Feature Engineering
1. Standardized GDP datasets from multiple sources.
2. Created new indicators like GDP growth rate, sectoral GDP contributions, and dropout rate impact scores.
GDP Analysis: Trends, Forecasting, and Economic Insights
About the project
This Python-based data analytics project provides an in-depth analysis of India’s GDP trends, regional economic disparities, sectoral contributions, and socio-economic relationships. The study focuses on uncovering growth trends across Indian states, identifying top-performing and struggling regions, and analyzing the link between GDP and education.
Through Exploratory Data Analysis (EDA), statistical modeling, and correlation analysis, this project delivers valuable data-driven insights and policy recommendations to support economists, policymakers, and businesses in economic planning and investment decisions.
Analysis Relevance
This project applies a mix of descriptive, diagnostic, predictive, and prescriptive analytics, making it a comprehensive economic study with real-world applications.
✔ Descriptive Analytics – Summarized India’s GDP trends, growth patterns, and sector-wise contributions.
✔ Diagnostic Analytics – Explored why some states are growing faster while others are lagging.
✔ Predictive Analytics – Modeled potential economic outcomes using trend analysis and correlation modeling.
✔ Prescriptive Analytics – Provided policy recommendations based on insights from economic indicators.
Steps Taken in Analysis
- Collected and merged GDP data from government economic reports, IMF, and World Bank datasets.
- Cleaned and standardized data across states and economic sectors.
- Handled missing values using linear interpolation and rolling averages.
- Identified historical GDP trends from 2012 to 2016.
- Analyzed regional economic disparities across Indian states.
- Visualized state-wise growth rates, sectoral contributions, and income inequalities.
- Calculated state-wise GDP growth rates and per capita income variations.
- Categorized states into growth quartiles based on GDP per capita.
- Engineered new features such as:
- GDP Growth Rate per State
- Sector-wise GDP Contribution (Primary, Secondary, Tertiary)
- GSDP vs. Dropout Rate Relationship
- Identified a strong negative correlation between dropout rates and GDP per capita.
- Found that tertiary sector employment correlates with low dropout rates, while primary sector employment is linked to high dropout rates.
- Established relationships between population growth, education, and economic development.
- Used time series forecasting models to predict India’s economic trajectory.
- Applied Pearson and Spearman correlation coefficients to examine economic dependencies.
- Suggested strategies to revive GDP growth and close economic gaps between states.
- Proposed sectoral investment priorities for long-term economic sustainability.
Key Insights & Findings
India’s GDP Growth is Declining
📍 Overall GDP growth has slowed in recent years.
📍 Fast-growing states: Andhra Pradesh, Goa, and Jammu & Kashmir.
📍 Slow-moving states: Haryana, Odisha, and Gujarat.
Regional Economic Disparities
📊Top 5 states have an average GSDP of ₹999,556 crore, while bottom 5 states have ₹45,111 crore.
📊 The top states' GSDP is 22 times higher than the bottom states and 2.6 times higher than the national average.
GDP Per Capita & Income Inequality
💵 Goa’s per capita income (₹271,893) is 8x higher than Bihar’s (₹33,954).
💵 28 states are above the national per capita income average of ₹113,941, while 16 states are below it.
Sector-Wise GDP Contributions
🏢 Tertiary sector contributes the most, followed by the secondary sector, while the primary sector has minimal or negative impact on economic growth.
🚜 Agriculture, Forestry & Fishing, Manufacturing, and Real Estate drive the highest economic returns.
GDP and Education Relation
📊 Higher GDP per capita leads to lower dropout rates, proving a direct link between education and economic growth.
📊 Primary sector jobs correlate with high dropout rates, while tertiary sector jobs correlate with lower dropout rates.
State-Wise Economic Divide
📊Top states like Maharashtra and Karnataka dominate industry and services, while Bihar and Jharkhand lag behind with low industrialization.
📊 The gap in infrastructure and investment widens economic disparities, requiring targeted policy action.
Screenshots
Technical Highlights
Exploratory Data Analysis (EDA)
1. GDP trends visualized using line charts and bar plots.
2. State-wise economic growth mapped using choropleth maps.
3. Correlation heatmaps and scatter plots used to identify key relationships.
Statistical Analysis & Modeling
1. Pearson correlation coefficients applied to detect economic dependencies.
2. Trend analysis performed using regression models.
Data Visualization
📊 GDP Growth Trends (Line Charts)
🌍 State-wise Economic Disparities (Choropleth Maps)
📈 GDP Per Capita Comparisons (Bar Charts)
📉 Dropout Rates vs. GDP Per Capita (Scatter Plots)
🔍 Sectoral Contributions to GDP (Stacked Bar Charts)
Skills Demonstrated
✅ Python for Data Analysis (Pandas, NumPy)
✅ Exploratory Data Analysis (EDA) & Feature Engineering
✅ Data Visualization (Matplotlib, Seaborn, Plotly)
✅ Statistical Analysis (Pearson Correlation Analysis)
✅ Data Cleaning & Transformation
Wrapping Up
This project showcases my expertise in economic data analytics, statistical modeling, and data visualization. The insights provide valuable recommendations for policymakers, economists, and business leaders, helping to shape sustainable economic policies and investment strategies for India's future.