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Did you know how Machine Learning projects are changing society? Across the globe they are making a big impact. Machine learning projects are serving from healthcare and finance to retail and entertainment. In order to achieve valuable insights in real-time, you can utilize the advantages of machine learning across various domains. It requires sophisticated algorithms and powerful frameworks. Here we have given some lists of Machine learning projects here. Check out the recent real-time projects of Machine Learning below
It is a kind of software development to identify the customers who are leaving a company’s service and taking necessary action to retain them.
We use decision trees, logistic regression, random forests, and neural networks for this project.
It is the software to detect how many people have requested for a bike booking within a specific time frame from the particular location.
We use data preprocessing, geo spatial clustering, time series analysis and more.
It is the software which uses machine learning algorithms to analyze factors that predict housing prices, land prices, demanding areas, and also do future predictions.
We use machine learning algorithms, regression techniques, and ensemble methods.
It is a kind of chat bot that mainly uses machine learning & natural language processing to answer questions from users.
The prime method employed here is QnA Maker service.
It is the method to detect the demand rate for taxis in the particular area within the specific timeframe. This improves customer service for the taxi companies.
In this we use machine learning, statistical models, and time series analysis.
It is the tool to predict the quality of the wine for the given features. It will be useful for various industries which use wine as the prime materials.
We use data processing, model construction, classification approaches, cluster analysis, and more.
It is the process of estimating the price of the house and land by using various methods. It also involves predicting the factors that affect housing prices.
We use Machine learning, multiple linear regression, decision tree regression.
It is the process of predicting which coupon a customer will purchase within a given timeframe. This helps customers not to miss out on any great deals & offers.
We use customer segmentation, machine learning methods to develop this tool.
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It is a kind of machine learning (ML) technique used for categorizing human activities with the combination of sensors and machine learning algorithms.
We use data preparation, missing value imputation, magnetic induction and more for this software.
It is the machine learning technique which utilizes statistical and ML techniques to predict future events and inform strategic decision-making.
We use exponential smoothing, ARIMA Model, Autoregressive model, series regression, and more.
It is a statistical method which uses machine learning to identify topics and themes in a collection of documents.
We mainly use Latent Dirichlet Allocation, structural topic modeling, latent semantic indexing, and more.
It helps pharmaceutical companies to develop new drugs and tests their effectiveness. It speeds up the drug development process.
We incorporate molecular docking, molecular dynamics, computational biology, ligand-based drug design and more.
It is the tool to identify the handwritten numbers from the labeled datasets with the help of machine learning methods.
We use feature extraction technique, convolution neural networks, multilayer perceptron, pooling layer, and more.
It is otherwise known as earthquake prediction. It detects earthquakes in the past, plate boundaries, electrical resistivity, and more.
We implement laser measuring technology, animal behavior sensors, seismic gap theory, and more.
It is the process of analyzing income levels, and predicting future income. It is mainly used for the accurate prediction of the income levels and other performance issues.
We use machine learning methods, and we also use CSPro.
It is a kind of machine learning method used to predict the value of houses according to various factors. And it is used to build predictive models.
We use random forest and gradient boosting algorithms to build software.
It is a machine learning tool that uses data to predict if a passenger survived a boat/ship sinking. It utilizes the factors like age, gender, and ticket class to have a good prediction.
We implement logistic regression, K-Nearest neighbors, Naive Bayes, decision tree, and random forest.
These tools use AI to assign categories to text documents. It is mainly used for organizing and structuring large amounts of unstructured text.
We implement Naive Bayes, Support Vector Machines, Decision Trees, Random Forests, and more.
It uses a machine learning algorithm to extract information from a resume and organize it in a digital format. It compares candidate qualifications and speeds up the hiring process.
We use statistical parsing, keyword-based parsing, grammar-based parsing, and more.
It helps manufacturers to improve their production. It involves quality checks, production monitoring, production line testing, security shipment timelines, and more.
We implement cross-validation, grid-search, to predict the best performance.
It helps credit card companies and banks to predict the likelihood of a customer defaulting on their payments.
We implement subgroup predicting, logistic regression, Naive Bayes, Random Forest classifier, and more.
It is a method used for human conversation either in a written format and allows people to interact with digital devices.
We use Natural Language Processing, pre-defined conversation flows, content types and semantic blocks.
It is a machine learning tool that uses data to predict if a passenger survived a boat/ship sinking. It utilizes the factors like age, gender, and ticket class to have a good prediction.
We implement time-series forecasting models, regression forecasting models, historical forecasting models, and more.
It is used to detect the false and misleading information given as news by channels or through any other platforms. It helps to prevent the impact of negative news on society.
We use Porter Stemmer and counter vectorization, term frequency-inverse document frequency, and count vector.
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Plant species identification is the process of identifying an unknown plant to a known species. It uses a systematic approach to group, name, and identify plants.
We make use of morphological features, leaf characteristics, genome analysis, and more.
It is a data mining technique that utilizes customer purchasing patterns for increasing sales. It also helps to show the purchase history, and offer list items.
We make use of Apriori algorithms, FP growth algorithms, association rule mining, and more.
It is otherwise known as sales forecasting. It is the process of estimating the future sale of the store based on historical data, market trends, and more.
We use historical data analysis, moving averages, machine learning, artificial intelligence, and more.
It is the machine learning technique that is used to predict the future price of avocados. It can also provide the details of farmers, distributors, retailers, and consumers.
We use regression techniques, label encoder for region column and rolling, and forecast methodology.
It is the process of speech processing and computational paralinguistics. It aims to recognise the emotion that comes out of the spoken language.
We incorporate deep learning, machine learning, digital signal processing, feature selection, and more.
It mainly uses machine learning intelligence to extract and label relevant medical information from a variety of unstructured texts.
We use entity recognition, relation extraction, entity linking, and assertion detection.
It helps you to find images that are similar to the given images. It helps people to find out or recognise the doubtful images of the particular picture.
Here, we use two prime methods. They are hashing and embedding-based similarity searches.
It is the software to identify the language of the text document. This software can detect a wide range of languages and some regional culture languages.
We implement N-gram based approach, frequent words method, short word-base approach, and deep learning techniques.
It is the method to measure and report the quality of the air, and how it affects the health of the people. It gives real-time information about the pollutant in the air and the quality level.
We use machine learning, random forest, satellite remote sensing, factor analysis, and more.
It is a machine learning technique that utilizes the nerve present in the ultrasound images. It helps doctors to locate nerves more accurately while performing medical procedures.
We use random undersampling, neural segmentation, and ultrasound images.
It is the process of making a machine learning technique available for use. It mainly involves pre-tuning a pre-trained model which is available for use.
We employ neural networks, and machine learning methods to build software.
It is also known as music notation software. It allows users to create, edit, and print sheet music for composition. It is a program used to create sheet music easily.
We use notation software, audio recording systems, visual art formation elements, and more.
Text detection performs optical character recognition to detect visible text from frames in a video, or a video segment. It is the software technology that changes printed documents into digital image files.
We employ text classification models and several machine learning techniques.
It is a strategy that uses technology to anticipate and prevent equipment breakdown in renewable energy systems. It improves reliability, efficiency, and longevity.
We use machine learning techniques, optimization techniques, and condition-based maintenance.
It is a chatbot based software application and gives mental support to users. It helps users to take control of their mental wellbeing.
We use machine learning & artificial intelligence techniques to build mental health assistant bots.
It mainly uses data analytics, machine learning, user-friendly interfaces, to help business people to predict current and future sales.
We use time series analysis, multivariable analysis, qualitative forecasting, linear regression, and more.
We provide Machine learning real-time projects by experts with in-depth implementation knowledge. We use the latest technologies and tools to design your customized project.
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It is used for the classification of software bugs based on their severity and impact. These software tools will help users to identify and prevent software bugs.
We use machine learning, graph neural networks, pareto principle, and more.
It helps retailers and brands to determine optimal prices for their product and services. It helps retailers to understand the needs of the customers and their behavior.
We implement data analysis, scenario modeling, AI recommendations, dynamic pricing, competitor analysis, and more.
It is a machine learning software that utilizes various techniques and identifies the iris flower from its species. It is a popular software tool because of the availability of larger datasets.
We use logistic regression algorithm, and K-Nearest Neighbors classification Algorithm.
Inventory demand forecasting also known as inventory forecasting. It helps businesses to predict and manage inventory levels to reduce overstocking and out of stock materials.
We use qualitative and quantitative methods, and demand forecasting techniques.
It is a technique that is used to check loan eligibility by analyzing historical and current data about a customer. It is aimed to automate the process of loan prediction.
We use logistic regression, random forest, decision trees, support vector machines, and more.
Stock prices prediction helps you discover the future value of company stock and other financial assets.It highly helps business people to predict stock market status.
We use linear regression, support vector machines, recurrent neural networks, and more.
It analyzes text and determines the emotional tone of a message. It analyzes how users talk about a product or service. It helps businesses to improve customer service.
We incorporate sentiment classification, emotion recognition, deep learning, feature extraction, and more.
It helps to suggest music to users based on their preferences. It helps to explore new music, explore new artists to have a seamless experience.
We use machine learning and deep learning methodologies to create a music recommendation system.
It predicts interest rates that will end up at the end of a given period of time. It outlines a factor composed of market risk, time, and equilibrium value.
We implement gap analysis, yield curve risk, and more to develop an interest rate prediction model.
It helps users to predict movies that people like to watch. It also analyzes users viewing history, ratings, reviews to generate personalized recommendations.
We incorporate collaborative filtering, content-based filtering, hybrid systems, and more.
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