Project Title: Rental Bike Share Prediction Technologies: Machine Learning Domain: Transport Project Difficulties level: Intermediate Problem Statement Bike-sharing systems are a new generation of traditional bike rentals where the whole process from membership, rental, and return has become automatic. Through these systems, users are able to easily rent a bike from a particular position and return back at another position. Currently, there are over 500 bike-sharing programs around the world which are composed of over 500 thousand bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. Apart from interesting real-world applications of bike-sharing systems, the characteristics of data generated by these systems make them attractive for research. The goal here is to build an end-to-end regression task. Here the user will provide the data and the result will be given by the best performing hyper-tuned Machine Learning model. The user will also get privileges to choose the deployment options. Dataset The Dataset you can get through this link: Dataset (https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset)Link (https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset) Project Evaluation metrics: Code: • You are supposed to write the code in a modular fashion. • Testable: It can be tested at the code level. • Maintainable: It can be maintained, even as your codebase grows. • Portable: It works the same in every environment (operating system) • You have to maintain your code on GitHub. • You have to keep your GitHub repo public so that anyone can check your code. • Proper readme file you have to maintain for any project development. • You should include the basic workflow and execution of the entire project in the readme file on GitHub. • Follow the coding standards: https://www.python.org/dev/peps/pep-0008/ (https://www.python.org/dev/peps/pep-0008/) Cloud: • You can use any cloud platform for this entire solution hosting like AWS, Azure, GCP, or any other platform. API Details or User Interface: • You have to expose your complete solution as an API or try to create a user interface for your model testing. Anything will be fine for us. Logging: • Logging is a must for every action performed by your code use the python logging library for this. Deployment: • You can host your model in the cloud platform, edge devices, or maybe locally, but with a proper justification of your system design. Solution Design: System Architecture: • You've to submit a system architecture design in your wireframe document and architecture document. Project Code: • You have to submit your code GitHub repo in your dashboard when the final submission of your project. Project Demo Video: • You have to record a project demo video for at least 5 Minutes upload it on your drive and share the link.

Project Title: Travel Package Purchase Prediction Technologies: Machine Learning Domain: Tourism Difficulties level: Intermediate Problem Statement Tourism is one of the most rapidly growing global industries and tourism forecasting is becoming an increasingly important activity in planning and managing the industry. Because of high fluctuations in tourism demand, accurate predictions of the purchase of travel packages are of high importance for tourism organizations. The goal is to predict whether the customer will purchase the travel or not. Approach: The classical machine learning tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building, and Model Testing. Try out different machine learning algorithms that are best fit for the above case. Dataset The Dataset you can get through this link: Dataset (https://drive.google.com/file/d/1DEM63ENqHREzylG6HCA_t9z7QL90ym-y/view?usp=sharing) Project Evaluation metrics: Code: • You are supposed to write the code in a modular fashion. • Testable: It can be tested at the code level. • Maintainable: It can be maintained, even as your codebase grows. • Portable: It works the same in every environment (operating system) • You have to maintain your code on GitHub. • You have to keep your GitHub repo public so that anyone can check your code. • Proper readme file you have to maintain for any project development. • You should include the basic workflow and execution of the entire project in the readme file on GitHub. • Follow the coding standards: https://www.python.org/dev/peps/pep-0008/ (https://www.python.org/dev/peps/pep-0008/) Cloud: • You can use any cloud platform for this entire solution hosting like AWS, Azure, GCP, or any other platform. API Details or User Interface: • You have to expose your complete solution as an API or try to create a user interface for your model testing. Anything will be fine for us. Logging: • Logging is a must for every action performed by your code use the python logging library for this. Deployment: • You can host your model in the cloud platform, edge devices, or maybe locally, but with a proper justification of your system design. Solution Design: System Architecture: • You've to submit a system architecture design in your wireframe document and architecture document. Project Code: • You have to submit your code GitHub repo in your dashboard when the final submission of your project. Project Demo Video: • You have to record a project demo video for at least 5 Minutes upload it on your drive and share the link.

Project Title: Customer Experience Prediction Technologies: Machine Learning Domain: Telcom Difficulties level: Intermediate Problem Statement Globally, the number of mobile cellular subscriptions is approaching the number of people on the planet, with emerging countries accounting for more than three-quarters of the total. GPS navigation, voice and text over data, and social media exchanges are just a few instances of how we are becoming increasingly reliant on our mobile phones. We expect to be online at all times because our business and personal lives would be disrupted if we weren't. Telecommunications operators (Telcos) are struggling to match these high expectations in a market where traditional phone and text plans are being phased out in favor of data offerings that support a wide range of mobile apps. For telcos, having a clear, up-to-date understanding of customer experience and satisfaction is a critical competitive advantage. Telcos, on the other hand, face the issue of coping with massive amounts of data created by mobile consumers every second. The main objective here is - 1. Telecommunications operators (telcos) traditional sources of income, voice, and SMS are shrinking due to customers using over-the-top (OTT) applications such as WhatsApp or Viber. 2. In this challenging environment it is critical for telcos to maintain or grow their market share, by providing users with as good an experience as possible on their network. But the task of extracting customer insights from the vast amounts of data collected by telco is growing in complexity and scale every day. 3. To measure and predict the quality of a user’s experience on a telco network in real-time is the major aim. Dataset The Dataset you can get through this link: Dataset (https://drive.google.com/file/d/17mB8IsypzFd7oBdYv4xFvyV_RYLdQiw_/view?usp=sharing) Project Evaluation metrics: Code: • You are supposed to write the code in a modular fashion. • Testable: It can be tested at the code level. • Maintainable: It can be maintained, even as your codebase grows. • Portable: It works the same in every environment (operating system) • You have to maintain your code on GitHub. • You have to keep your GitHub repo public so that anyone can check your code. • Proper readme file you have to maintain for any project development. • You should include the basic workflow and execution of the entire project in the readme file on GitHub. • Follow the coding standards: https://www.python.org/dev/peps/pep-0008/ (https://www.python.org/dev/peps/pep-0008/ ) Cloud: • You can use any cloud platform for this entire solution hosting like AWS, Azure, GCP, or any other platform. API Details or User Interface: • You have to expose your complete solution as an API or try to create a user interface for your model testing. Anything will be fine for us. Logging: • Logging is a must for every action performed by your code use the python logging library for this. Deployment: • You can host your model in the cloud platform, edge devices, or maybe locally, but with a proper justification of your system design. Solution Design: System Architecture: • You've to submit a system architecture design in your wireframe document and architecture document. Project Code: • You have to submit your code GitHub repo in your dashboard when the final submission of your project. Project Demo Video: • You have to record a project demo video for at least 5 Minutes upload it on your drive and share the link.

Project Title: Shipment Pricing Prediction Technologies: Machine Learning Domain: Supply Chain Difficulties level: Intermediate Problem Statement The market for supply chain analytics is expected to develop at a CAGR of 17.3 percent from 2019 to 2024, more than doubling in size. This data demonstrates how supply chain organizations understand the advantages of being able to predict what will happen in the future with a decent degree of certainty. Supply chain leaders may use this data to address supply chain difficulties, cut costs, and enhance service levels all at the same time. The main goal is to predict the supply chain shipment pricing based on the available factors in the dataset. Approach: The classical machine learning tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building, and Model Testing. Try out different machine learning algorithms that are best fit for the above case. Dataset The Dataset you can get through this link: Dataset (https://www.kaggle.com/datasets/divyeshardeshana/supply-chain-shipment-pricing-data) Project Evaluation metrics: Code: • You are supposed to write the code in a modular fashion. • Testable: It can be tested at the code level. • Maintainable: It can be maintained, even as your codebase grows. • Portable: It works the same in every environment (operating system) • You have to maintain your code on GitHub. • You have to keep your GitHub repo public so that anyone can check your code. • Proper readme file you have to maintain for any project development. • You should include the basic workflow and execution of the entire project in the readme file on GitHub. • Follow the coding standards: https://www.python.org/dev/peps/pep-0008/ (https://www.python.org/dev/peps/pep-0008/) Cloud: • You can use any cloud platform for this entire solution hosting like AWS, Azure, GCP, or any other platform. API Details or User Interface: • You have to expose your complete solution as an API or try to create a user interface for your model testing. Anything will be fine for us. Logging: • Logging is a must for every action performed by your code use the python logging library for this. Deployment: • You can host your model in the cloud platform, edge devices, or maybe locally, but with a proper justification of your system design. Solution Design: System Architecture: • You've to submit a system architecture design in your wireframe document and architecture document. Project Code: • You have to submit your code GitHub repo in your dashboard when the final submission of your project. Project Demo Video: • You have to record a project demo video for at least 5 Minutes upload it on your drive and share the link.

Project Title: Stores Sales Prediction Technologies: Machine Learning Domain: Sales & Marketing Difficulties level: Intermediate Problem Statement Nowadays, shopping malls and Big Marts keep track of individual item sales data in order to forecast future client demand and adjust inventory management. In a data warehouse, these data stores hold a significant amount of consumer information and particular item details. By mining the data stored in the data warehouse, more anomalies and common patterns can be discovered. Approach: The classical machine learning tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building, and Model Testing. Try out different machine learning algorithms that are best fit for the above case. Dataset The Dataset you can get through this link: Dataset (https://www.kaggle.com/datasets/brijbhushannanda1979/bigmart-sales-data) Project Evaluation metrics: Code: • You are supposed to write the code in a modular fashion. • Testable: It can be tested at the code level. • Maintainable: It can be maintained, even as your codebase grows. • Portable: It works the same in every environment (operating system) • You have to maintain your code on GitHub. • You have to keep your GitHub repo public so that anyone can check your code. • Proper readme file you have to maintain for any project development. • You should include the basic workflow and execution of the entire project in the readme file on GitHub. • Follow the coding standards: https://www.python.org/dev/peps/pep-0008/ (https://www.python.org/dev/peps/pep-0008/) Cloud: • You can use any cloud platform for this entire solution hosting like AWS, Azure, GCP, or any other platform. API Details or User Interface: • You have to expose your complete solution as an API or try to create a user interface for your model testing. Anything will be fine for us. Logging: • Logging is a must for every action performed by your code use the python logging library for this. Deployment: • You can host your model in the cloud platform, edge devices, or maybe locally, but with a proper justification of your system design. Solution Design: System Architecture: • You've to submit a system architecture design in your wireframe document and architecture document. Project Code: • You have to submit your code GitHub repo in your dashboard when the final submission of your project. Project Demo Video: • You have to record a project demo video for at least 5 Minutes upload it on your drive and share the link.

Project Title: Consignment Pricing Prediction Technologies: Machine Learning Domain: Logistics Difficulties level: Intermediate Problem Statement The market for logistics analytics is expected to develop at a CAGR of 17.3 percent from 2019 to 2024, more than doubling in size. This data demonstrates how logistics organizations understand the advantages of being able to predict what will happen in the future with a decent degree of certainty. Logistics leaders may use this data to address supply chain difficulties, cut costs, and enhance service levels all at the same time. The main goal is to predict the consignment pricing based on the available factors in the dataset. Approach: The classical machine learning tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building, and Model Testing. Try out different machine learning algorithms that are best fit for the above case. Dataset The Dataset you can get through this link: Dataset (https://www.kaggle.com/datasets/divyeshardeshana/supply-chain-shipment-pricing-data) Project Evaluation metrics: Code: • You are supposed to write the code in a modular fashion. • Testable: It can be tested at the code level. • Maintainable: It can be maintained, even as your codebase grows. • Portable: It works the same in every environment (operating system) • You have to maintain your code on GitHub. • You have to keep your GitHub repo public so that anyone can check your code. • Proper readme file you have to maintain for any project development. • You should include the basic workflow and execution of the entire project in the readme file on GitHub. • Follow the coding standards: https://www.python.org/dev/peps/pep-0008/ (https://www.python.org/dev/peps/pep-0008/) Cloud: • You can use any cloud platform for this entire solution hosting like AWS, Azure, GCP, or any other platform. API Details or User Interface: • You have to expose your complete solution as an API or try to create a user interface for your model testing. Anything will be fine for us. Logging: • Logging is a must for every action performed by your code use the python logging library for this. Deployment: • You can host your model in the cloud platform, edge devices, or maybe locally, but with a proper justification of your system design. Solution Design: System Architecture: • You've to submit a system architecture design in your wireframe document and architecture document. Project Code: • You have to submit your code GitHub repo in your dashboard when the final submission of your project. Project Demo Video: • You have to record a project demo video for at least 5 Minutes upload it on your drive and share the link.

Project Title: Reliable Author Identification in Avila Bible Technologies: Machine Learning Domain: Education Difficulties level: Intermediate Problem Statement The study of highly uniform handwriting and book typologies is a particularly fascinating case in the realm of manuscript studies (paleography and codicology). In such circumstances, examining some basic layout aspects, primarily those connected to the structure of the page and the use of available space, can be highly useful in distinguishing between comparable scribal hands. You need to establish a set of layout elements in this framework to create a pattern recognition system for identifying the scribes who worked together to transcribe a single medieval Latin text. You also need to test the discriminative strength of each considered characteristic, to see if selecting a subset of traits for each scribe, specifically designed to identify him from the others, could help us get better results. This method allowed us to add a simple reject option for unreliably classified samples, such as those that were not assigned to any scribe or were assigned to many scribes. The experiments, which used a big database of digital images from the so-called "Avila Bible" – a massive Latin copy of the entire Bible compiled during the sixteenth century between Italy and Spain – proved that the proposed method works. Various photographs of the pages inside the Avila Bible were taken, and based on those photographs, they have derived various features. In the Bible, there are a total 12 authors who have written different scripts. So, our goal is solving the classification problem and predict which author wrote the particular script. Dataset The Dataset you can get through this link: Dataset (https://archive.ics.uci.edu/dataset/459/avila) Project Evaluation metrics: Code: • You are supposed to write the code in a modular fashion. • Testable: It can be tested at the code level. • Maintainable: It can be maintained, even as your codebase grows. • Portable: It works the same in every environment (operating system) • You have to maintain your code on GitHub. • You have to keep your GitHub repo public so that anyone can check your code. • Proper readme file you have to maintain for any project development. • You should include the basic workflow and execution of the entire project in the readme file on GitHub. • Follow the coding standards: https://www.python.org/dev/peps/pep-0008/ (https://www.python.org/dev/peps/pep-0008/) Cloud: • You can use any cloud platform for this entire solution hosting like AWS, Azure, GCP, or any other platform. API Details or User Interface: • You have to expose your complete solution as an API or try to create a user interface for your model testing. Anything will be fine for us. Logging: • Logging is a must for every action performed by your code use the python logging library for this. Deployment: • You can host your model in the cloud platform, edge devices, or maybe locally, but with a proper justification of your system design. Solution Design: System Architecture: • You've to submit a system architecture design in your wireframe document and architecture document. Project Code: • You have to submit your code GitHub repo in your dashboard when the final submission of your project. Project Demo Video: • You have to record a project demo video for at least 5 Minutes upload it on your drive and share the link.

Project Title: Market Basket Project on E-Commerce Technologies: Machine Learning Domain: E-Commerce Difficulties level: Intermediate Problem Statement Most customers do not post a review rating or any comment after purchasing a product which is a challenge for any E-commerce platform to perform If a company predicts whether a customer liked/disliked a product they can recommend more similar and related products as well as they can decide whether or not a product should be sold at their end. This is crucial for E-commerce-based companies because they need to keep track of each product of each seller so that none of the products discourage their customers to come shopping with them again. Moreover, if a specific product has very few ratings that are too negative, a company must not drop the product straight away, maybe many customers who found the product to be useful haven't actually rated it. Some reasons could possibly be comparing your product review with those of your competitors beforehand, gaining lots of insight about the product and saving a lot of manual data pre-processing, maintaining good customer relationships with the company, lending gifts, offers, and deals if the company feels the customer is going to break the relation. The objective of this case study is centered around predicting customer satisfaction with a product which can be deduced after predicting the product rating a user would rate after he makes a purchase. Dataset The Dataset you can get through this link: Dataset (https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce) Project Evaluation metrics: Code: • You are supposed to write the code in a modular fashion. • Testable: It can be tested at the code level. • Maintainable: It can be maintained, even as your codebase grows. • Portable: It works the same in every environment (operating system) • You have to maintain your code on GitHub. • You have to keep your GitHub repo public so that anyone can check your code. • Proper readme file you have to maintain for any project development. • You should include the basic workflow and execution of the entire project in the readme file on GitHub. • Follow the coding standards: https://www.python.org/dev/peps/pep-0008/ (https://www.python.org/dev/peps/pep-0008/) Cloud: You can use any cloud platform for this entire solution hosting like AWS, Azure, GCP, or any other platform. API Details or User Interface: You have to expose your complete solution as an API or try to create a user interface for your model testing. Anything will be fine for us. Logging: Logging is a must for every action performed by your code use the python logging library for this. Deployment: You can host your model in the cloud platform, edge devices, or maybe locally, but with a proper justification of your system design. Solution Design: System Architecture: You've to submit a system architecture design in your wireframe document and architecture document. Project Code: You have to submit your code GitHub repo in your dashboard when the final submission of your project. Project Demo Video: You have to record a project demo video for at least 5 Minutes upload it on your drive and share the link.

Project Title: Backorder Prediction Technologies: Machine Learning Domain: E-commerce Difficulties level: Intermediate Problem Statement Backorders are unavoidable, but by anticipating which things will be back-ordered, planning can be streamlined at several levels, preventing unexpected strain on production, logistics, and transportation. ERP systems generate a lot of data (mainly structured) and also contain a lot of historical data; if this data can be properly utilized, a predictive model to forecast backorders and plan accordingly can be constructed. Based on past data from inventories, supply chain, and sales, classify the products as going into backorder (Yes or No). Approach: The classical machine learning tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building, and Model Testing. Try out different machine learning algorithms that are best fit for the above case. Results: You have to build a solution that should be able to predict the backorder sales for a particular product according to the provided dataset. Dataset The Dataset you can get through this link: Dataset (https://github.com/rodrigosantis1/backorder_prediction/blob/master/dataset.rar) Project Evaluation metrics: Code: • You are supposed to write the code in a modular fashion. • Testable: It can be tested at the code level. • Maintainable: It can be maintained, even as your codebase grows. • Portable: It works the same in every environment (operating system) • You have to maintain your code on GitHub. • You have to keep your GitHub repo public so that anyone can check your code. • Proper readme file you have to maintain for any project development. • You should include the basic workflow and execution of the entire project in the readme file on GitHub. • Follow the coding standards: https://www.python.org/dev/peps/pep-0008/ (https://www.python.org/dev/peps/pep-0008/) Cloud: • You can use any cloud platform for this entire solution hosting like AWS, Azure, GCP, or any other platform. API Details or User Interface: • You have to expose your complete solution as an API or try to create a user interface for your model testing. Anything will be fine for us. Logging: • Logging is a must for every action performed by your code use the python logging library for this. Deployment: • You can host your model in the cloud platform, edge devices, or maybe locally, but with a proper justification of your system design. Solution Design: System Architecture: • You've to submit a system architecture design in your wireframe document and architecture document. Project Code: • You have to submit your code GitHub repo in your dashboard when the final submission of your project. Project Demo Video: • You have to record a project demo video for at least 5 Minutes upload it on your drive and share the link.