Top 10 Projects for Data Science and Machine Learning

Top 10 Projects for Data Science and Machine Learning

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For those just starting out in the field of data science and machine learning, this section contains several fun projects they can try. These are some simple machine learning tasks that you may learn in a short amount of time.

1)Detection of Facial Masks in Real Time

Computer vision and image processing have a significant positive and negative impact on the identification of the face mask. Face detection has a variety of practical applications, including face recognition and facial movements, the latter of which requires the face to be displayed with an exceptionally high level of precision. Despite the rapid development of machine learning algorithms, the challenges presented by face mask identification technology appear to be adequately managed. This technological advancement is gaining more and more significance as it is used to identify people’s faces in photographs and in live video streams. Face detection, on the other hand, is a highly difficult task by itself, according to the recently proposed models of face mask detection. The examination of events and video surveillance is always a difficult task. This is because current facial detectors have produced spectacular results, which have inspired the development of even more advanced facial detectors.

2)Checker of the Performance of the Production Line

Because Bosch is one of the most successful manufacturing firms in the world, the company must make sure that the recipes it uses to produce its cutting-edge mechanical components adhere to the strictest possible quality and health regulations. To accomplish this, it is necessary to carefully monitor the parts of the product as they move through the various manufacturing processes. Because data is recorded at each stage of the assembly process on Bosch’s assembly lines, the company can utilize advanced analytics to further improve these manufacturing processes. However, the intricate nature of the data and the complexity of the production line provide challenges for the currently used approaches. Within the context of this competition, Bosch is posing a challenge to Kagglers to forecast internal failures by using the hundreds of measurements and tests performed on each component along the assembly line. This would make it possible for Bosch to provide end-users items of higher quality at more affordable prices.

3)Estimating the Levels of Interest Shown in Rental Listings

It should not be enough to just go through a never-ending list of postings when trying to find the ideal location to call your new home. RentHop simplifies finding an apartment by using statistics to rank the suitability of available rentals. However, while looking for the ideal apartment can be a challenge in and of itself, constructing and making sense of all the available real estate data through programming is even greater. This recruiting competition hosted by Two Sigma and incorporating rental listing data courtesy of RentHop is an opportunity for you to showcase your skills. Based on the listing’s creation date and the other features, we will make an educated guess as to the number of queries a new listing will receive. If this is done, RentHop will be better able to handle fraud control, identify potential listing quality issues, and allow owners and agents to better understand the requirements and preferences of renters.

4)OpenCV is a Project Designed for Novices to Learn the Fundamentals of Computer Vision

For those just starting out with computer vision, a good OpenCV project to master the fundamentals is Single and Multi-Object Tracking. When using Single Object Tracking, also known as SOT, the tracker is provided with the bounding box of the target in the very first frame. The tracker’s mission at this point is to establish a location for the same target in each of the other frames. If you require resources, then you must. You will learn how to do computer vision on images using OpenCV and Python by utilizing Jupyter Notebook in this project-based class that lasts for one hour and is based on a project. Rhyme, Coursera’s hands-on project platform, is what this class uses to get projects done. The fact that you are not required to set up your development environment is the feature of this project-based course that stands out as the most beneficial. For this undertaking, you will obtain prompt access to a cloud PC that already has Python, Jupyter, and OpenCV loaded on it.

5)The Recognition of Human Activity Through the Use of Smartphone Datasets

The term “human activity recognition,” or HAR, can be used in various contexts, including medical research and human survey systems. Within this project’s scope, we develop a dependable activity recognition system centered on a mobile device, specifically a smartphone. The only sensor used to capture time-series signals by the system is a three-dimensional smartphone accelerometer. This research focuses on the recognition of human activity through the use of smartphone sensors by employing a variety of machine learning classification strategies. The information obtained from smartphone accelerometer and gyroscope sensors is sorted so that it can distinguish different types of human movement. The outcomes of the various methods used are compared concerning their levels of accuracy and precision.

6)The Forecasting of Driver Demand

One of the most rapidly expanding trends in the world of online retailing is the provision of food delivery services facilitated by technologically advanced application platforms. While we all enjoy placing orders online, one thing that none of us particularly enjoy is having to deal with varying prices for delivery fees. The delivery cost heavily depends on the number of riders available in your region, the number of orders placed in your area, and the distance covered. Because there is a shortage of drivers, there has been an increase in the cost of delivery, which has caused a significant number of consumers to cancel their orders, resulting in a loss for the company. If we keep track of the number of hours that a certain delivery executive is working, we can more effectively assign certain drivers to a given area based on the demand in that area. This will allow us to address the difficulties that have been raised.

7)A Prediction for the Price of Dogecoin

Machine learning presents a challenge in the form of a regression problem when attempting to forecast the price of a cryptocurrency. Bitcoin is one of the cryptocurrencies that has been the most successful to date, however, the price of bitcoin has recently experienced a significant decline because of dogecoin. Dogecoin is now trading at a very low price compared to bitcoin; nevertheless, financial analysts believe that dogecoin values may experience a significant surge in the near future. To predict the price of dogecoin, we have access to a wide variety of different machine learning strategies. You can either train a machine learning model from scratch or use a highly capable model that is already on hand, such as the Facebook Prophet Model. However, in the next section, you will be applying machine learning to the task of predicting the price of Dogecoin using the auto package that is available in Python.

8)Analysis of the Prediction of Lost Customers

Throughout the past couple of quarters, a well-known financial institution has noticed a significant number of customers either closing their accounts or migrating to financial institutions that are their competitors. This has caused a large hole in their quarterly revenues and could significantly impact their yearly revenues for the current fiscal year. As a result, the company’s stocks have plummeted, and its market cap has decreased significantly. The bank needs to be able to determine which customers are likely to leave so that it may take the appropriate preventative measures and other measures to keep these customers as clients. For this machine learning churn prediction research, we have been given customer data concerning the individual’s previous dealings with the bank, in addition to basic demographic details.

We use this to develop relations and linkages between data variables and customers’ tendency to churn, and we build a classification model to determine whether or not a customer would quit the bank as a result of using this information. In addition, we go through the process of explaining model predictions using several different visualizations and provide insight into which causes or factors are responsible for the churn of the consumers. This project guides you through a comprehensive end-to-end cycle of a data science project in the banking industry, beginning with the discussions that take place during the creation of the issue statement and ending with the preparation of the model so that it is ready for deployment.

9)Recruiting Based on Coupon Purchase Prediction

Ponpare is the most popular joint coupon site in Japan. They provide enormous savings on anything from hot yoga to gourmet food and a concert extravaganza throughout the summer. Ponpare’s coupons allow clients to walk through doors they previously could have only fantasized about entering. They have the opportunity to learn challenging talents, have previously unimaginable adventures, and dine like (and with) celebrities. This competition encourages you to predict which coupons a client will buy within a specified amount of time based on their previous purchases and browsing habits. Ponpare’s recommendation system will be improved with the help of the models generated as a result of this research. Consequently, the company will be able to ensure that its clients are not deprived of the opportunity to discover their upcoming favorites.

10)The Use of Neural Networks in Classification

The most straightforward of the various designs for deep learning is the autoencoder. They belong to a subcategory of feedforward neural networks, in which the input is initially condensed into a lower-dimensional code. After that, the output is pieced back together using the code’s summary or compact representation. Because of this, autoencoders are constructed with an encoder, a code, and a decoder as part of their internal design. You will require an encoding method, a decoding method, and a loss function before you can even begin the development process. Binary cross-entropy and mean squared error are excellent options when selecting a loss function. Backpropagation is another method that can be used to train autoencoders. This method is similar to the one used to train artificial neural networks. Now that we have everything out of the way let’s talk about the applications of these networks.

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