Our project attempts to investigate economic indicators responsible for the economic growth of India, we wish to learn how the economic indicators are correlated with each other. We have accumulated data from 1960-2020 to do a timeseries analysis. We will take a deep dive into the trends witnessed over the years and how they affected other economic indicators. We will also study what economic indicators are highly correlated with GDP as GDP is commonly accepted as the primary source of judging economic growth.
Preprocessing_the_initial_csv.ipynb : Remove null values and redundant features.
EDA.ipynb : Explanatory Data Analysis of the data.
Forecasting.ipynb : Use various time series forecasting models to predict GDP.
FBProphet.ipynb : Time series forecasting using the FBProphet library of Facebook.
We choose data collected by the World Bank in the form of the the World Development Indicators.
They allowed us to tailor a custom dataset through the vast number of indicators they have collected data for. Our dataset only took indicator data for India, as our study pertains to that. The dataset is available in the form of Data.csv.
Our team started off by understanding all the indicators and their relavence by performing Exploratory Data Analysis in the form of EDA.ipynb. Before we could perform this however, our dataset was raw and had to be preprocessed before we could do anything which we did via Preprocessing the initial csv file.ipynb which produced final.csv. We gathered insights by plotting graphs in plots_complete.ipynb. Since our data was timeseries data, we making models and tested them in Forecasting.ipynb.
Standard python library
numpy
pandas
matplotlib
seaborn
scikit-learn
statsmodels
scipy
fbprophet
AlphaCube_024_042_056_165