GDP Prediction of India

Introduction

March of 2020 is the month with COVID 19 drastic spread all over the countries and WHO declared it as a global Pandemic. As the spread increased many countries implemented lockdown and the exports, imports, manufacturing were halted for a certain time and so the global economic crisis took place.

So we trained a machine learning model that could predict the GDP growth of India so that the government could invest in the right sectors and take smart and optimized decisions to overcome the economic crisis. This project aims on building a predictive model of the GDP growth rate of India after this pandemic COVID-19 and to suggest the optimal scenario for stimulating the growth rate.

We will be working with datasets on Agriculture, Corporate performance, Public expenditure, GST revenue, and Index of Industrial Production (IIP). We will be Implementing MLPRegressor and ARIMA time series model for GDP Prediction based on the previous year’s GDP growth. We also used different regression algorithm like Linear regression based on the accuracy.

Analysis

1.1 Regression on Neural Network (manually configured)

We implemented regression on a neural network which is different on the output layer compared to normal MLP’s because the output is not limited to 1 or 7 integers, we need a series of output so we to solve this issue we must remove the activation function of the last layer or set it to linear. We tried with different loss functions, optimizers for better results. In the end, the loss is measured with mean-squared logarithmic error, and optimizer is set to adam and metrics as a mean absolute percentage error for the best outcomes.

Fig 1 : Plotting was done with black dots as test data and blue dots as predicted data

1.2 Regression on Neural Network (using inbuilt function MLPREGRESSOR)

We implemented the MLP regression using the predefined function of sklearn with solver set to lbfgs, alpha set to 0.001, and epochs to 1000.

Fig 2 : Plotting was done with black dots as test data and blue dots as predicted data.

1.3 Linear Regression

Then we implemented linear regression with sklearn package with fit_intercept set to false and other parameters are left as default for best results.

Fig 3 : Plotting was done with black dots as test data and blue dots as predicted data

Learning Outcome

Fig 4 : represents the results of manually configured multi-layer perceptron regressor having mean absolute percentage error as 19.3342

Fig 5: MLPregressor outputs an accuracy of 85.44%

Fig 6: Linear Regression outputs an accuracy of 89.67%

ARIMA Prediction

Upcoming Years prediction using Time series model

Conclusion and Future Work

• Our research solution is to gather the news articles of particular year and analyze the data effect of positive and negative rates on each sector and predict the GDP sectors using the time series model and alter the values based on the total positive and negative rates on each sector and then predict the value of GDP using these predicted sectors. ​
• When we analyzed the effect of data on each GDP sector using sentimental analysis, we noticed a less accuracy in specifying the positive and negative rates of the news articles. So, we can increase the accuracy of it by implementing a customized sentimental analyzer for verifying these articles. This was our thought on future works.​

I would like to thank my teammate Madhu Kiran for forming an excellent team and making this project successful.

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