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Some of Mogital's Inspiring Success Chronicles

Welcome to Mogital Success Chronicles, where we celebrate our triumphs in solving complex data challenges. This dedicated space showcases the unique stories of how we've partnered with diverse clients to transform their businesses using the power of data. Each project reveals our tailored approach, the innovative solutions we've devised, and the tangible results achieved. These chronicles demonstrate not just our technical prowess, but also our commitment to facilitating meaningful change. Explore our stories and discover how Mogital Analytics can turn your data into your most powerful asset.



Optimizing Yields in Solvent-Based Processes with Mogital Analytics

The 'Mogital Analytics' project brings a meticulous exploration into process efficiency, focusing on identifying the best yield among two methods and various solvents. Through careful data management, statistical analysis, and experimental results, we identified the optimal conditions and interactive effects of various factors on yield. Further, the project scrutinized the role of catalysts in the yield, providing a comprehensive study for process optimization. All these insights form the foundation for targeted improvements in related industrial applications, paving the way for heightened productivity and cost-effectiveness.


Predicting Customer Loan Default Payments Using Logistic Regression: A Case Study of a Financial Institution in Ghana


This project utilizes Logistic Regression to predict loan default among customers of a financial institution in Ghana. By leveraging a dataset of 30,000 observations, we've identified eight significant features that influence default prediction. These insights can help financial institutions enhance their decision-making, improve transparency, and potentially reduce interest rates, ultimately fostering more confidence in their clientele. Through this model, we aim to fast-track the decision-making process, making loan accessibility easier and more efficient for individuals.



Universal Health Coverage through Mobile Transactions: A Sociodemographic Study in Ghana

The study in Adidome, Ghana, investigates the feasibility of Universal Health Coverage (UHC) via earmarked mobile transactions. With a sample size of 351 participants, the results reveal an overwhelming willingness of the populace to contribute towards UHC using this approach. The analysis indicates that the age group 21-30 years significantly influences the willingness to pay. The study also highlights the importance of perceived benefits in driving willingness to contribute to UHC. This research provides useful insights for policymakers to strategize the successful implementation of UHC through mobile transactions.


Leveraging Health Metrics for Predicting Fitness

This project focused on analyzing a dataset containing 386 patient records with 28 measurements. Through comprehensive data cleaning and exploratory analysis, patterns were identified and visualized to understand correlations between various health metrics and patient fitness levels. Key findings indicated that normal ear-nose-throat (ENT) conditions, blood sugar, Hepatitis B status, urine results, and drug screen results significantly predicted fitness levels. Notably, patients with normal urine results were less likely to be predicted as fully fit. The project underscores the critical role of detailed health data in predictive modeling of patient fitness.



Forecasting Texas' Energy Transition: A Simple ARIMA Modeling Approach

In this study, we utilized the Autoregressive Integrated Moving Average (ARIMA) model to project the future of Texas' electricity generation landscape, focusing on the role of natural gas, coal, and wind energy. The research offers a unique perspective on how wind energy might fill the potential deficits if Texas gradually reduces its natural gas reliance by 75% between 2021-2050. It emphasizes the importance of enabling renewable energy sources while also considering technological solutions like battery storage to address their intermittent nature. The modeling results show promising prospects for wind energy, particularly offshore, as a critical player in Texas' energy transition.


Optimizing Underground Dewatering Systems: An Empirical Approach

This project presents a data-driven approach to optimize underground dewatering systems. We developed mathematical models using Dynamic Linear Model (DLM), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM) to predict water levels in shaft bottoms. Using correlation analysis and the Theis equation, we examined the relations between pump run hours, discharged water volume, and water-level drop. Our findings indicate a possibility to speed up dewatering by adjusting pumping rates, allowing substantial time savings in dewatering operations.



Evaluating the Effectiveness of Greenhouse Sanitizers in Pathogen Reduction and Crop Quality Improvement

This research aimed to evaluate the effectiveness of commercial sanitizers in eliminating Salmonella Typhimurium from hydroponic systems and assessing their impact on crop quality. The study also explored the potential of low-pH nutrient solutions and SaniDate treatment in reducing pathogen levels in lettuce. Statistical methods were applied to ensure significant results were not due to chance. Findings revealed all sanitizers effectively reduced Salmonella levels, and different treatments significantly influenced the quality and nutritional attributes of lettuce and basil. Finally, the SaniDate treatment proved successful in lowering Salmonella and L. monocytogenes levels over time.


Harnessing Ecotourism Potential Through Statistical Analysis

Through a comprehensive survey-based research study, this project explores the character of a targeted landscape and its potential for ecotourism. Employing descriptive statistics and hypothesis testing, the study uncovers the socio-demographic characteristics of respondents, awareness levels, and perceptions of ecotourism development. Notable insights include the significant involvement of local stakeholders and traditional authorities in land ownership and ecotourism development, and the critical role of secondary stakeholders. Techniques like word frequency analysis and Latent Dirichlet Allocation (LDA) were applied to open-ended responses, unveiling topics around ecotourism's aesthetic, cultural, and economic potential, while also highlighting the awareness gap among community members.

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