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Prashant Abbi  • Peerlist

Designed and developed a Data Analysis project that involved building a model that predicts whether the finished goods, i.e medicines from pharmaceutical companies would be delivered to various international locations on time or not. The model is trained and tested using the Decision Tree Algorithm while taking the demand forecast, productions, transportation costs, and delay factors into account.

Supply Chain Management in the Pharmaceutical Industry

This project predicts the estimated crop yield (Lint Yield of Cotton) based on input parameters (US State, Area Planted, Area Harvested, N-P-K Acreage % and N-P-K Quantity in Pounds/Acre). The data set analyzes historical data of 14 states in the US since 1964 and also recommends the fertilizer quantity to be used. The prediction model is based on Gradient Boosting Regression (90.1% R2 value) with K Folds Cross Validation (5). Fertilizer Recommendation uses the Multi-Variate Regression algorithm.

Machine Learning: Crop Yield Prediction and Fertilizer Recom

Designed and developed a Data Analysis project on the spread of the Novel Coronavirus in India. This project is an attempt at analyzing the coronavirus spread in India using data science concepts and analytics with the help of Python in the Jupyter Notebook interface, using popular packages of numpy, pandas, matplotlib, seaborn, folium, and fbprophet for implementing a time series forecast.