Mohamed Mubassir Hussain

Highly motivated and detailed-oriented candidate passionate about using data to improve business performance and customer experience. Skilled at leveraging data to develop actionable solutions to business challenges and utilizing data mining and data visualization to create meaningful insights. Excellent technical aptitude and knowledge of programming languages, data analytics and data visualization.

Campus Ambassador at SLICE Chennai,India (April 2018 - March 2019)

Technical Intern at 3G IRPS IITM Research Park, India (May 2019 - June 2019)

Project Title: Machine Learning and Artificial Intelligence to Address Seasonality in Irish Tourism

Roles and Responsibility: I led this project, Sole contributor responsible for all phases of the project, including Requirement Gathering, Development, and Testing

Description: This project addressed the challenge of seasonality in Irish tourism. The tourism sector in Ireland experiences significant fluctuations throughout the year, with the highest number of tourists visiting during the summer months. These seasonal variations lead to issues such as crowded tourist areas, transportation challenges, regional imbalances, and economic instability. To combat this, I leveraged machine learning and artificial intelligence. I used machine learning algorithms and techniques to predict visitor demand, analyze historical visitor data, and identify seasonal patterns and trends. Machine learning also helped us segment visitors for targeted marketing campaigns and personalized recommendations, especially during off-peak seasons. My goal was to provide data-driven solutions to optimize resource allocation, diversify tourism offerings, and enhance the overall visitor experience, ultimately leading to a more stable and sustainable Irish tourism sector.

Technologies Used: Machine Learning, Python, Data Analysis, Machine Learning Algorithms, AI-driven recommendation systems, Firebase, Jupiter notebook.

Project Title: Rent Analysis and Classification of Housing Market

Roles and Responsibility: Sole contributor responsible for all phases of the project, including Requirement Gathering, Development, and Testing.

Description: Using various datasets, including the Birmingham House Rent Prediction dataset, Flats for Rent in Mumbai dataset, and Paris Housing Classification dataset, this project involved in-depth analysis of rental data in cities like Mumbai, Paris, and Birmingham. The primary focus was on the House Rent Prediction dataset, which contains information about rental properties globally. The analysis examined relationships between factors such as location, property type, number of rooms, amenities, and rental costs. Machine learning methods were employed to perform analysis and classification, with the goal of creating high-accuracy predictions using five supervised learning algorithms. The Knowledge Discovery in Databases (KDD) method was used to implement a cyclical process of data selection, cleaning, transformation, modeling, evaluation, and deployment.

Technologies Used: Machine Learning, Python, Knowledge Discovery in Databases (KDD).