###### Abstract
Limited studies on educational inputs and output in Pakistan mostly focused on specific regions [3,7]. One study conducted in Khyber Pakhtunkhwa revealed that essentials of educational infrastructure such as teaching quality, drinking water, gas, electricity, and school building conditions positively impact educational outcomes [3]. Researchers have recognised the impact of the surrounding environment on the performance of academic institutions [1,4,8]. Mostly, their focus remained on student academic performance [8,9], but some researchers targeted a particular region on a small scale [3,7]. Some research focused on early predictors of student success rates in higher education institutions (HEIs) [10]. Some studies considered basic facility parameters (i.e., electricity, gas, libraries, and teaching quality) and showed their impact on schools in some districts of Khyber Pakhtunkhwa province, in Pakistan [3]. Educational opportunities reflect the local school environment and socio-economic factors [1] because the performance of educational institutions in urban areas is different from that in rural areas [1,2,4]. The disparities in school education are due to regional differences and gender inequalities across Turkey [11]. The association of different parameters in different country regions was analysed. In some regions, the number of females in the local population is higher because males tend to migrate earlier for employment. Moreover, institutional facilities and learning environments directly affect the performance of school institutions. Punjab's school education department conducts quarterly district rankings to track school performance and timely highlight those schools that are lagging. This ranking is based on various indicators such as student attendance, teacher presence, and the availability of boundary walls, toilets, drinking water, and furniture. The ranking statistics still show the need to uplift educational levels in different districts of Punjab [12]. Discovering new information from a massive amount of data is challenging and sometimes too expensive [8]. The most commonly adopted process used to extract hidden information from a large amount of data is data mining (DM). The approach used to extract meaningful knowledge from educational data is known as education data mining (EDM). Different machine learning-based models are used for performance measurement, including random forest, decision tree, K-nearest neighbour, and naive Bayes [13]. This study proposes a framework to measure the institutional performance based on key performance indicators through data mining techniques. **Contributions:** This study offers several contributions in the education domain to measure the performance of educational institutions. 1. A state-of-the-art dataset has been collected regarding the different indicators to measure the performance of educational institutions. The collected dataset was prone to noise, biases, and missing and outlier values. 2. Much work has been done to evaluate individual schools or measure student performance rather than institutional performance. To the best of our knowledge, no work has been done to measure institutional performance. However, a novel method for performance measurement of public institutions through machine learning models has been proposed in this study. 3. Regarding institutional performance, a regional perspective has been applied. This indicator has not been explored in the literature to investigate the performance of institutions. 4. Significant feature selection techniques were combined with machine learning models to develop the proposed framework for the performance measurement of public schools. It has also been observed that differences in demographics and provided facilities emerged due to regional differences. 5. This study will help support governance for performance monitoring, policy formulation, target-setting, evaluation, and reforms. The achieved results will help to address the issues and challenges in education worldwide.