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Objective of this project is to perform predictive assesment on the Gross Domestic Product of India through an inferential analysis of various socio-economic factors to find out which predictors contribute most to the GDP. Various models are compared and Stepwise Regression model is implemented which resulted in 5.7% Test MSE.

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Predicting-GDP-of-India

Objective: Prediction of GDP based on Different Socio-economic factors

Data Source: World Bank Open Data

Models Score
1 Random Forest 15.64 % Test MSE
2 Ridge Regression 10.51 % Test MSE
3 Principal Component Regression 9.27 % Test MSE
4 Lasso Regression 6.84 % Test MSE
5 MLR with Forward Subset Selection 5.22 % Test MSE

Implemented Model: MLR with Forward Subset Selection

Model Coefficients:

GDP Growth = – 2.949 + 1.988 *(Population age 40-44, male) 
		                 – 5.305e-01 * (Population age 35-39, female) 
		                 + 2.538 * (Population age 25-29, female) 
		                 – 8.169e-01 * (Population age 15-64, Total)
		                 – 6.625e-02 * (Consumer Price Index) 
		                 – 5.478e-13 * (Discrepancy in Expenditure Estimate of GDP) 
		                 – 9.095e-02 * (Gross National Expenditure) 
		                 + 3.706e-02 * (Military Expenditure) 
		                 – 4.941e-10 * (Arms Import)

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Objective of this project is to perform predictive assesment on the Gross Domestic Product of India through an inferential analysis of various socio-economic factors to find out which predictors contribute most to the GDP. Various models are compared and Stepwise Regression model is implemented which resulted in 5.7% Test MSE.

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