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Pantech provides the best machine learning projects for final year students which offer concepts for what’s essential for final year engineering students. Developing real-world projects is the best way to hone skills and materialize theoretical knowledge into practical experience. The benefit of Machine Learning is that it helps to expand the horizon of thinking and make some amazing projects.
Pantech is a platform that offers a wide range of machine learning training programs for final year students and helps to build skills designed for emerging technologies. It helps to understand the fundamentals of principles and technical conditions of machine learning projects. It helps to understand and erect self-confidence to achieve the best dream career in machine learning and provides guidance to learn further skills in a quicker way.
Why machine learning is important to learn, because it has experienced exponential growth and there’s a demand for engineers that can help companies throughout various industries identify openings for implementation of the technology and the most effective, profitable ways to use it. It’s getting so important that numerous companies are seeking to fill the range of IT positions with individuals who bring a background or experience with machine learning.
The main purpose is to develop machine learning algorithms for predicting the percentage of Student Placement Prediction using machine learning based on the data related to the university’s academic reputation, opportunities of the city where the university is located, facilities, and cultural opportunities of the university. It analyzes the previous year’s historical data and predicts placement of current students and aids to increase the placement percentage of the institutions.
The method of extracting these summaries from the original huge text without losing vital information. It is to identify the important sections, interpret the context and reproduce in a new way. This ensures that the core information is convey through the shortest text possible. There are important applications for text summarization using NLP in various NLP-related tasks such as text classification, question answering, legal texts summarization, and news summarization. Moreover, the generation of summaries can be integrate into these systems as an intermediate stage which helps to reduce the length of the document.
The enormous information in health care is to be process in order to identify, diagnose, detect and prevent the various diseases. Big data analysis contains a large number of records. It develops a centralized patient monitoring system using big data. In the proposed system, a large set of medical records is taken as input. Heart disease is a major health problem and it is the leading cause of death throughout the world. Thus, the system helps to classify a large and complex medical dataset for Heart Disease Detection using Big Data.
Predicting Employee Attrition using Machine Learning model is the output generate when training a machine learning algorithm with data. After training, when it provides a model with an input, will be given an output. It can be used in real-time to learn from data. The improvements in accuracy are a result of the training process and automation that are part of machine learning. This project provides a solution for the given problem as it gives a prediction model that can be used to predict which employee will leave the company and which will not leave. It helps in finding the exact reasons which are motivating the employees for shifting companies like lower salary, fewer promotions or heavy workload, etc.
The advances in machines and technologies used in smart farming using machine learning, useful and accurate information about different matters plays a significant role in it. It focuses on predicting the appropriate crop based on the climatic situations and the yield of the crop based on the historic data by using supervised machine learning algorithms. The only remedy to the crisis is to do all that is possible to make agriculture a profitable enterprise and attract the farmers to continue the crop production activities.
This approach tests the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. It analyzed stock markets prediction; these methods could be effective also in predicting bitcoin price prediction using machine learning. It is predicted as the average price across the preceding days, and that the method based on long short-term memory recurrent neural networks systematically yields the best return on investment.
Deep learning is usually associate with having a high number of input layers, one or more hidden layers that connect input layers and perform computational algorithms to determine a probability to predict. The concept of churn modeling analysis using deep learning is the application area of analytical customer relationship management in the widest perspective. In fact, this application area is a sub-part of customer behavior modeling in customer analytics. From the marketing perspective, the concept of churn is associate with customer loyalty and customer value concepts that are related to each other.
This data set consists of information of users who age, type of symptoms related to diabetes. Data is classified and shown in the form of different graphs. The easy data analysis will show results of medical information of changes of getting diabetes on universal plots. Early Diabetes Prediction Using Machine Learning in a human body or a patient for higher accuracy through applying various Machine Learning techniques. It Provides better results for prediction by constructing models from datasets collected from patients.
With emerge of KDD and data mining approach for finding network attacks is the traditional techniques become more complex to deal with Big Data. Therefore, it intends to use Big Data techniques to produce high-speed and accurate intrusion detection systems. The results of the experiment showed that the model has high performance and efficiency for Big Data. It is a software application that monitors the network or system activities for malicious activities and unauthorized access to devices. The implementation of different data mining algorithms including linear regression and K-Means clustering to automatically generate the rules for classifying network activities.
It is an effective and efficient malicious applications detection tool needed to tackle and handle new complex malicious apps create by hackers. With the idea of using machine learning approaches for detecting the malicious android application. It provides an efficient and convenient way to access, find and share information; however, the availability of this information has caused an increase in cyber threat analysis. The importance of developing a national security policy created for mobile devices in order to protect sensitive personal data.
Student Performance Prediction using Machine Learning analysis of outcomes based on learning is a system that will strive for excellence at different levels and diverse dimensions in the field of students’ interests. It analyzes the student’s demographic data, study-related and psychological characteristics to extract all possible knowledge. It provides the prediction of academic success or failure without illustrating the reasons for this prediction. These attributes were from the same type of data category whether demographic, study-related attributes, that lead to a lack of diversity of predicting rules.
Use of Clustering in Machine Learning is the task of mapping text to its accompany hashtags. In this process, a novel model for hashtag prediction and show this task a useful surrogate for learning good representations of text. This hashtag-based detail query shows the result as to whether it will be positive or negative and random forest algorithm. The hashtag prediction provides a more direct form of supervision: the tags are labeling of the salient aspects of the text. Hence, predicting provides stronger semantic guidance than unsupervised learning. The abundance of hashtags provides a huge labeled dataset for learning potentially sophisticate models.
Rainfall Prediction using Machine Learning gives awareness to people and knowledge in advance about rainfall to take certain precautions to protect their crops from rainfall. It was concluded the enhancements, optimizations, and integrations of data mining methods are vital to explore and solve these problems. It provides a critical analysis and review of the latest data mining techniques used for rainfall prediction and predict rainfall with maximum accuracy by optimizing and integrating data mining techniques.
It mainly focus on Credit Card Fraud Detection using Deep Learning. After the classification process of the random algorithm to analyze the data set and the user provides the current dataset. It will apply the processing of some of the attributes provided can find affected fraud detection in viewing the graphical model visualization. The deep learning methods for credit card fraud detection compare the performance with three different financial datasets. Experimental results show the great performance of the deep learning methods that can be implement effectively for real-world credit card fraud detection systems.
It describes incorrect and misleading articles published mostly for the purpose of making money through page views. The topic of machine learning methods for fake news detection using machine learning, most of it has been focusing on classifying online reviews and publicly available social media posts. This project could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news-related articles.
This method can be extend on any platform that needs Fake Profile Detection using Machine Learning to deploy on public profiles for various purposes. It uses available information which makes it convenient for organizations that avoid any breach of privacy. The organizations use private data to further extend the capabilities of the proposed model. This model uses a classification technique and can process a large dataset of accounts at once, eliminating the need to evaluate each account manually.
Stock Market Prediction using Machine Learning is the act of trying to determine the future value of a stock from social media social media offers a robust outlet for people’s thoughts and feelings Analysis of social media is strongly related to sentiment analysis. It is used for analyzing social network content and improves the average accuracy.
The proposed framework analyzes the students demographic data, study-related and psychological characteristics to extract all possible knowledge from students, teachers, and parents. Seeking the highest possible accuracy in academic performance prediction using a set of powerful data mining techniques. Student Performance analysis using Machine Learning of outcomes based on learning is a system that will strive for excellence at different levels and diverse dimensions in the field of student’s interests.
Malware Detection in Websites promotes the growth of Internet criminal activities and constrains the development of Web services. As a result, it develops a systemic solution to stop the user from visiting such Web sites. Thus, it eliminates the possibility of exposing users to browser-based vulnerabilities.
Liver Disease Detection using Machine Learning was used to evaluate prediction algorithms in an effort to reduce the burden on doctors. It will take results of how much percentage patients get the disease as positive information and negative information. Thus, outputs shown from the proposed classification model indicate Accuracy in predicting the result. Liver disease may cause the appearance of malignant in the effects the rest of the body.
With the enhancement in the banking sector, lots of people are applying for Loan approval prediction using machine learning and the bank has its limited assets which has to grant limited people, so as to find to whom the loan can be grant which will be a safer option for the bank is a typical process. It reduces this risk factor behind selecting the safe person so as to save lots of bank efforts and assets. The analysis will be done to find the most relevant attributes, i.e., the factors that affect prediction result the most.
The exponential growth of social media such as Twitter and community forums has revolutionize communication and content publishing, but is also increasingly exploit for the propagation of Hate Speech Detection using Machine Learning and the organization of hate-based activities. The structures serving as feature extractors that are particularly effective for capturing the semantics of hate speech and methods are evaluate on the largest collection of hate speech datasets based on twitter to outperform of identifying hateful content.
It analyzed the data for observation of wells in each of the districts and developed seasonal models to represent the GroundWater Prediction using Machine Learning behavior and capture trends on water levels in observation wells, the rainfall model explores the correlation between the rainfall levels and water levels. The periodic and polynomial models are develop only using the groundwater level data of observation wells while the rainfall model also uses the rainfall data.
It can be detected by developing an accurate prediction model which will be capable of automatic separation of various accidental scenarios. This cluster will be useful to prevent accidents and develop safety measures. It acquires maximum possibilities of accident reduction by using some scientific measures. It determines significantly affects the severity of the driver’s injuries which are caused due to road accidents. Accurate and comprehensive accident records are the basis of Road Accident analysis using Machine Learning. The effective use of accident records depends on some factors, like the accuracy of the data, record retention, and data analysis.
It utilizes smart data as a means of learning and discovering Human Activity Recognition using Machine Learning patterns for health care applications. This uses frequent pattern mining, cluster analysis, and prediction to measure and analyze energy usage changes sparked by occupants’ behavior. It is used for each of the methods wherein the data are collected by different means such as sensors, images, accelerometers, gyroscopes, etc., and the placement of these devices at various locations.
This system will prevent crime from occurring in society. It is analyzed which is stored in the database. The data mining algorithm will extract information and patterns from the database. Crime Analysis using K-means Clustering will be done based on places where crime occurs, a gang who is involved in the crime took place. This will help to predict crime that will occur in the future.
Intrusion Detection System using Machine Learning (IDS) is a system that monitors and analyzes data to detect any intrusion in the system or network. The high volume, variety, and high speed of data generated in the network have made the data analysis process to detect attacks by traditional techniques very difficult. It used the k-Means method in the machine learning libraries on spark to determine whether the network traffic is an attack or a normal one.
Message classification of learning group using machine learning in social media systems which include Facebook, Instagram, Twitter, etc. have brought an exponential boom with the mistreatment of human beings of hateful messages, bullying, sexism, racism, competitive content, harassment, poisonous remark, etc. Thus, there’s an in-depth to identify, manage and decrease the bullying contents unfold over social media sites, which has stimulated behavior to automate the detection method of offensive language or cyberbullying.
It presents a systematic review of methodologies that propose ways of reducing the dropout rate in Virtual Learning Environments (VLE). This generates large amounts of data about courses and students whose analysis requires the use of computational analytical tools. It aims to identify solutions that use Machine Learning (ML) techniques to reduce these high dropout rates. The amount of data collected through the educational databases and it increasing rapidly in volume which allows statistical analysis, data mining, and predictive actions.
Applying machine learning algorithms for learning, analyzing, and classifying the product information based on the customer experience. The product data with customer reviews are collected from a unified computing system (UCS) which is a server for data-based computers for evaluating hardware, support to visualization, software management. Thus, it determines the significance of understanding customers’ opinions in terms of the shopping experience in a particular e-commerce website.
A software-defined network (SDN) is a network that is used to build, design hardware components virtual and dynamic change the settings of network connections. It consists of three planes such as data plane, control plane, and application plane. It improves the network performance by decoupling control and forward function. The control programs running in a logically centralized controller will control multiple routers across the network.
A massive dataset training when no single node can work out the accurate results within an acceptable time. However, this will inevitably expose more potential targets to attackers compared with the non-distributed environment.
It improved the data poison detection scheme to provide better learning protection with the aid of the central resource. To efficiently utilize the system resources, an optimal resource allocation approach is developed.
Sentiment analysis methods have been used to improve the Elections Results Prediction of counting methods. It is significant in relation to the observation period, the data collection and cleansing methods, and the performance evaluation strategy.
For predicting election results diverse places around the world have utilized machine learning models to advance deep learning algorithms. It is to extract strong sentiments from Twitter data linked to elections and used a time series method to forecast final outcome.
Ransomware detection and classification using machine learning is a type of malware that prevents users from using computers or mobile phones for accessing certain files unless the user pays a ransom which is often by credit card.
The major type of malware, ransomware, encrypts a user’s sensitive information and returns the original files to the user after a ransom is paid. It extracts high-level flow features from the traffic and uses this data for ransomware classification.
The prediction of share prices is the function of deciding the future price of company stock or other commercial tools. It is performed for the stock market prediction using machine learning value and daily direction of change in the stock index.
Such huge numbers of models have been created for foreseeing future stock costs. This develops and assesses different techniques to see future stock trades and experimental results state different classification techniques can be successfully deployed for share price prediction.
Identifying Crime Detection using Machine Learning allows us to tackle problems with unique approaches in the crime category and improve more security measures in society.
It involves predicting crimes classifying, pattern detection, and visualization with effective tools and technologies. The use of past crime data helps to correlate factors that might help to understand the future scope of crimes.
Crop Price Prediction using Machine Learning creates an economic future for developing countries, the demand for modern technologies in this sector is higher. The key technologies used for this problem are Deep Learning, Machine Learning, and Visualization.
Like the product, an android mobile application is developed and the users input their location to start the prediction process. It is to detect the nature and quality of soil in a particular area considering the level at the time and predicting future value using ML model.