Education plays a pivotal role in alleviating poverty, driving economic growth, and empowering individuals, thereby significantly influencing societal and personal development. However, the persistent issue of school dropout poses a significant challenge, with its effects extending beyond the individual. While previous research has employed machine learning for dropout classification, these studies often suffer from a short-term focus, relying on data collected only a few years into the study period. This study expanded the modeling horizon by utilizing a 13-year longitudinal dataset, encompassing data from kindergarten to Grade 9. Our methodology incorporated a comprehensive range of parameters, including students' academic and cognitive skills, motivation, behavior, well-being, and officially recorded dropout data. The machine learning models developed in this study demonstrated notable classification ability, achieving a mean area under the curve (AUC) of 0.61 with data up to Grade 6 and an improved AUC of 0.65 with data up to Grade 9. Further data collection and independent correlational and causal analyses are crucial. In future iterations, such models may have the potential to proactively support educators' processes and existing protocols for identifying at-risk students, thereby potentially aiding in the reinvention of student retention and success strategies and ultimately contributing to improved educational outcomes.

September 8 has been celebrated as the'International Literacy Day' across the world since 1967. The significance of this day arises from the fact that despite the steady rise in literacy rates over the past 50 years, there are still 773 million illiterate adults around the world. In India, though the literacy rate has seen phenomenal growth--from 18.3% to 74.4% between 1951 and 2018--there are 313 million illiterate people, according to the study, "Literacy in India: The gender and age dimension." Illiteracy and dropout rates are acutely linked. Dropping out of school is a rampant trend in India.

HUANGCHUAN VILLAGE, CHINA – Every day after lunch, Qu Yexiu used to potter around her house in northwest China doing housework and looking after her 2-year-old grandson. Now, every day after lunch, Qu and her grandson visit the newly opened early-childhood development center in their village of Huangchuan in the mountains of Shaanxi province, where he can play with other toddlers. "Things are better now that we have this village center," said Qu, 56. She looks after her two grandchildren while their parents work and live in nearby Anhui province. The other grandchild attends a preschool.

During the last two years, Microsoft has upped the ante on Machine Learning and Analytics. From hiring top notch data scientists to acquiring niche startups, Redmond has made the all the right moves to transform Azure into one of the best analytics platforms. These investments have started to pay off for the company. It has been successful in articulating and demonstrating the value of data-driven insights to governments, medical institutions, and public sector organizations. Emerging markets that are turning technology savvy are becoming the hotbed for evaluating the upcoming trends such as Machine Learning and Artificial Intelligence.