1.Forecasts for the Cost of Housing
For the purpose of this data mining research, a housing dataset is utilised. This dataset contains information on all of the various property values. Along with the location, the size of the house, and any other pertinent information that is required for it, the dataset that is used for price prediction has been included in this project. You may be able to follow a predictive model with straightforward methods such as regressions or machine learning libraries, depending on the amount of sophistication of the model. This project will find its application in various aspects of the real estate industry. This project leverages several housing datasets in order to make accurate price projections for residential real estate utilising various algorithms and methodologies. Either use a data analytics application like Tableau or Excel to carry out linear regression, or select a machine learning library to use in conjunction with the programming language “R” or Python. Both of these options are available to you.
2. Predicting Diseases using Artificial Intelligence Using Naive Bayes
In today’s world, immediate access to medical treatment is something that everyone could require, but it is often inaccessible for a variety of reasons. An end user support system, the smart health illness prediction makes it possible for users to obtain timely counselling with the assistance of an online intelligent health system. The database contains comprehensive information on symptoms as well as the diseases that are related with them. The system does an analysis of the diseases that may be related with the patient’s symptoms and then provides the patient with recommendations for further testing, such as an X-ray, blood test, or CT scan, as the system requires. Users are also able to directly get in touch with the professional doctors for any illness and share their reports through the platform. It is not a one-time thing; rather, a correct login detail is provided with the other person for use in the future.
3. A System for the Detection of Fake Logos Online
Thousands of brands lose a significant amount of their annual revenue every year as a direct result of unlawful knock off brands and counterfeits of those trademarks. These knockoff goods are of a lower quality overall, which hurts the reputation of the brand as a whole as a result. In addition, customers feel as though they have been duped when they part with their hard-earned money to get something that is merely a fake. A consumer-facing online system that detects phoney logos will be able to tell the difference between authentic products and counterfeits. In addition to assisting users in their struggle against counterfeit goods, it also assists brands in their fight against piracy.
4.The Recognition of Handwritten Digits
The Handwritten Digit recognition project is widely considered to be among the most successful data mining initiatives by both data scientists and all of the aficionados of machine learning. In the context of this study, machine learning methods are utilised to differentiate and categorise graphical representations of handwritten numerals. This project can be created with the assistance of a computer vision AI model, machine learning techniques, and Convolutional Neural Networks. The project will have a nice graphical user interface that allows users to write or draw on a canvas, and for the output, a model that is good at predicting the digit will be produced. Python and R are both useful languages that can be utilised for this project. The Scikit-learn model in Python, which makes use of algorithms such as K-Nearest Neighbors and a Support Vector Classifier, will be an excellent choice for the project.
5.An investigation into the classification of mushrooms
Details of the samples that are related to the 23 species of gilled mushrooms from the Lepiota and Agaricus Family of Mushrooms that are described in the Audubon Society Field Guide to North American Mushrooms are being mined for information as part of this data mining project (1981). Each type of mushroom is ranked according to whether it is safe to eat, harmful, of unknown edibility, or not recommended. Therefore, by the end of this project, you will have the ability to differentiate mushrooms according to their particular group, even though there is no rule like “leaflets three, let it be” that specifies whether or not something is edible.
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