We study the impact of diversifying recommendations for inclusivity on one of the largest visual content discovery platforms in the world, Pinterest. Pinterest re-designed its recommendation systems to improve the representation of all skin tones in recommended content and foster a more inclusive user experience. We describe the design of the new recommendation system and present results from a field experiment in which users across six countries were randomly assigned to receive a more diverse set of recommendations based on content skin tone. We find that the overall engagement rates remain stable and engagement with previously underrepresented content increases significantly. More broadly, users diversify their consumption by engaging with content from all skin tone ranges. We shed light on the mechanism driving these results using heterogeneous treatment effect analysis. We find that engagement for users with "preference for deeper skin tone content" increases significantly and engagement for users with "preference for lighter skin tone content" remains relatively stable. Finally, we analyze post-launch data to better understand the long-term implications of diversifying recommendations. Our research provides practical insights for platform managers and policymakers to create inclusive digital environments that promote engagement while catering to diverse user preferences.

Solar energy is one of the most popular renewable energy sources in recent years. These solar systems not only reduce the need for electricity but also greatly improve the environment. Due to our excessive dependence on fossil fuels and their limited sources and the pollution caused by them, most people are turning to solar energy to power their homes and industries. It's critical to understand the location of the solar panel and the local weather since solar power efficiency is highly dependent on environmental factors. With the inherent unpredictability of photovoltaic power output, machine learning is a useful tool for predicting how weather conditions affect solar power plants, and there are various other viable prediction methods available. This research evaluates several meteorological characteristics for PV forecasting using numerous machine learning algorithms. Using the collected historical data set, a machine learning model is created. Necessary preprocessing techniques such as univariate and bivariate analysis are used. Machine learning algorithms benefit from data visualization as it makes it easier to understand features and create a robust classification model. Accuracy-related performance metrics such as MAE, MSE, and RMSE are used to compare the methods. We evaluate and contrast the various models' performances. This evaluation approach helps to choose the algorithm that best suits the task at hand.