Analysis of prices of commodities in various APMCs to uncover trends in prices of each commodity and APMC.

The purpose of this project is to explore the impact of a set of variables like horsepower, transmission configuration, engine cylinder configuration, etc. on the mileage `mpg` (Miles per Gallon). And come up with a model which can accurately predict the mileage of a car. Ordinary Least Squares model was fit and best model was selected based on RMSE, R-squared and AIC value. Most of the predictors in the dataset are correlated which makes the ordinary least squares solution unstable and increases the variability. Therefore, we used the Partial least Sqaures which finds components that maximally summarise the variation of the predictors while simultaneously requiring the predictors to have maximum correlation with the response. 3 components were chosen as it gave the minimum RMSE value. On comparing both the models in terms of RMSE and R-squared, we conclude that PLS is a bit superior than the linear regression as the RMSE is significantly lower.

This project is aimed at analysing crime against women in Delhi. The data was obtained from Safecity, it also has a mobile applicaion. Safecity is a platform that crowdsources personal stories of sexual harassment and abuse in public spaces. The idea is to make this data useful for individuals, local communities and local administration to identify factors that causes behavior that leads to violence and work on strategies for solutions. One of the long term goals of this project is to help police departments understand, predict and if possible prevent or to mitigate potential damage from crime.

The goal was to come up with a logistic regression model for predicting the results of presidential elections and comparing it with the smarter baseline model.