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Optimizing the London Restaurant Industry with NLP and Big Data Insights from Google Maps Reviews

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In my dissertation project, I utilize the Google Maps API, Natural Language Processing and Big Data Techiques to analyze restaurant reviews in London. By examining both quantitative and qualitative data, I uncover valuable insights for economic observers, restaurant owners, and customers alike. This analysis offers a procedural framework for deriving meaningful conclusions from Google Maps reviews, benefiting various stakeholders in the restaurant industry.  Disclaimer: The purpose of this project is to focus on academic pratice, not for economic or trading purposes. The information provided is approximate and not 100% accurate. Because the data used in this project was collected in June 2023, To view coding of this project, please follow this link .  Note: Please, click on the below images to view them with better clarity. I. OBJECTIVES:  - To analyze restaurant distribution patterns in London, which contribute to a deeper understanding of the city's culinary geography -...
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Maximize SAP Performance Through Smart Entity Relationship Analysis

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1. Introduction In today's business landscape, organizations rely heavily on complex enterprise resource planning (ERP) systems like SAP to manage their operations effectively. However, as these systems grow in scale and complexity, it becomes increasingly challenging to identify and optimize the intricate relationships between various business entities. One of the primary challenges faced by organizations using SAP is the lack of visibility into the relationships and dependencies between different business entities. These entities, which can include customers, products, orders, and suppliers, are often spread across multiple systems and modules within the SAP ecosystem. Without a clear understanding of how these entities are interconnected, it becomes difficult to identify bottlenecks, streamline processes, and optimize overall system performance. This project aimed to address these challenges by developing a user-friendly web application that provides a comprehensive view of the ...
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Enhancing Heart Failure Predictions: A Data-Driven Approach with Machine Learning

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Heart Failure Prediction using Machine Learning In this project, I investigated various programming and analytical methods for analyzing and evaluating the effectiveness of machine learning models in accurately predicting patient cases of heart failure. The healthcare sector relies on precise predictions of heart failure cases to make well-informed decisions about patient survival. I used scientific computing, data analysis, data visualization, and machine learning techniques to complete this task.  The project demonstrated my proficiency in using various machine learning techniques, data analysis, and visualization tools to tackle a real-world problem in the healthcare domain. It showcases my ability to identify and address challenges, such as class imbalance, and use feature selection methods to improve model performance. To view coding, please follow this link. Note: Please, click on the below images to view them with better clarity. I. IMPLEMENTATION 1.1. Exploratory Data ...
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