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Top 50 Machine Learning Projects | Machine Learning Projects

DAY 3 DESIGN

What is Machine Learning

Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves.

Top 50 Machine Learning Projects

  1. Real time object recognition using Raspberry Pi and OpenCV
  2. Smart School Bus Tracking System
  3. Leap Motion Controlled Drone
  4. Leap Motion Drone
  5. Intelligent Coal Mine Monitoring System based on the Lora-cloud
  6. Dry and Wet Age-Related Macular Degeneration Classification using OCT Images and Deep Learning
  7. Bitcoin Price Prediction using Machine Learning | Python
  8. Employee Attrition using Machine Learning
  9. Alzheimer’s Disease Detection using Machine Learning | OpenCV
  10. Arrhythmia classification using Python
  11. Ground water level Prediction
  12. Intrusion Detection using Classification
  13. Hotel review rating classification using NLP
  14. Election Results prediction based on Twitter data
  15. Arabic Natural Language Processing
  16. Road accident Analysis and classification
  17. Human activity Recognition
  18. Crime Analysis using K means
  19. Detecting Malware Websites
  20. Liver Disease prediction
  21. Loan approval prediction
  22. Hate speech Detection Using Machine learning
  23. Stock market prediction using Classification
  24. Student Performance analysis
  25. Student feedback classification using Random Forest
  26. Credit card fraud detection using Deep learning
  27. Fake News detection using machine learning
  28. Fake profile identification Machine learning
  29. Rainfall prediction using machine learning
  30. Cyber Threat Analysis on Android Apps
  31. Student Performance Prediction – Machine Learning
  32. Hashtag Clustering using NLP | Machine Learning
  33. KDD & Data Mining Approach for Finding Network Attacks
  34. Churn Modelling Analysis using Deep Learning | Python
  35. Diabetes Prediction using Machine Learning | AI | Python
  36. Student Placement Prediction using AI | Machine Learning
  37. Text Summarization using NLP I Machine Learning
  38. Rating Prediction using Machine Learning

 

  1. Real time object recognition using Raspberry Pi and OpenCV

It drives deeper and various algorithms that can be used for real time object detection using raspberry pi and OpenCV. This network uses the tangent sigmoid function as kernel function. Finally, the simulated result shows that a used network classifier provides minimum error i.e.; during training and better accuracy in classification.

  1. Smart School Bus Tracking System

The smart school bus tracking system is an easy-to-use software include both a web-based program and a mobile application that gives parents, students and school service firms the ability to track accurately the location of the school service vehicles.

 

  1. Leap Motion Controlled Drone

Leap Motion Controller is used for recognition of gestures, which are motion of the hand and as a result. It can control the motion of the drone by simple gestures from the human hand

 

  1. Leap Motion Drone

It controls the drone based on the hand movement and gestures. Data from a Leap Motion controller i.e.; which detects gestures and hand movement, are captured by MATLAB and the control signals are send.

 

  1. Intelligent Coal Mine Monitoring System based on the Lora-cloud

It enhances the productivity at the side of product high-quality the automation within the field of coal mine as a way to lessen the employee’s efforts. The numerous of wellbeing plays a key position in producing technique.

 

  1. Dry and Wet Age-Related Macular Degeneration Classification using OCT Images and Deep Learning

The age-related macular degeneration are diseases that can have adverse effects on the eyes of the elderly. The both dry and wet types are accurately detected for timely treatment. It is shown through performance results of the deep networks dry vision impairment can be detected more accurately than wet.

 

  1. Bitcoin Price Prediction using Machine Learning | Python

This approach to test the hypothesis inefficiency of the crypto currency market can be exploited to generate abnormal profits. It analyzed stock markets prediction; these methods could be effective also in predicting crypto currencies prices.

  1. Employee Attrition using Machine Learning

A machine learning model is the output generated when to train 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.

 

  1. Alzheimer’s Disease Detection using Machine Learning | OpenCV

This is a progressive disease and early detection of classification of AD can majorly help in controlling the disease. Image feature extraction techniques i.e.; along with machine learning algorithms for this purpose.

  1. Arrhythmia classification using Python

Arrhythmia signal that provides faster and more accurate result is increasingly becoming need of the moment. Various machine learning skills have been applied to advance the accuracy of results, increase the speed and robustness of the models.

  1. Ground water level Prediction

It captures trends on water levels in observation wells, it explores the correlation between the rainfall levels and water levels. The periodic and polynomial models are developed i.e.; using the groundwater level data of observation wells while the rainfall model also uses the rainfall data.

 

  1. Intrusion Detection using Classification

Intrusion detection system is a system that monitors and analyzes data i.e.; to detect any intrusion in the system or network. 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.

 

  1. Hotel review rating classification using NLP

The technique that allows machines to read and understand through human emotions and extract useful insights for many businesses to grow and develop in the field. Hotel reviews collected from the guests can be classified into three subclasses i.e., positive, negative, or neutral and therefore it can analyze the sentiment of the customer.

 

  1. Election Results prediction based on Twitter data

Sentiment analysis methods have been used to improve the predictive results of counting methods. It significantly in relation to the observation period, i.e.; the data collection and cleansing methods, and the performance evaluation strategy.

 

  1. Arabic Natural Language Processing

This oversight by developing tools and techniques that deliver state-of-the-art performance in a variety of language processing tasks. Machine translation is most active area of research, but also worked on statistical parsing and part-of-speech tagging.

 

  1. Road accident Analysis and classification

It can be detected by developing an accurate prediction model which will be capable of automatic separation of various accidental scenarios. These cluster will be useful i.e.; to prevent accidents and develop safety measures. It acquires maximum possibilities of accident reduction by using some scientific measures.

  1. Human activity Recognition

It utilizes smart data as a means of learning and discovering human activity patterns for health care applications. This uses i.e.; frequent pattern mining, cluster analysis, and prediction to measure and analyze energy usage changes sparked by occupant’s behavior.

  1. Crime Analysis using K means

This system will prevent crime occurring in society. It is analyzed which is stored in the database. Data mining algorithm will extract information and patterns from database. Clustering will be done i.e.; based on places where crime occurred, gang who involved in crime took place. This will help to predict crime which will occur in future.

 

  1. Detecting Malware Websites

Malicious Web sites promote the growth of Internet criminal activities and constrain the development of Web services. As a result, it develops solution to stopping the user from visiting such Web sites. Thus, it eliminates the possibility of exposing users to the browser-based vulnerabilities.

 

  1. Liver Disease prediction

This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors. It will take results of how much percentage patients get disease i.e.; as a positive information and negative information. Thus, outputs show from proposed classification model indicate that Accuracy in predicting the result.

 

  1. Loan approval prediction

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.

 

  1. Hate speech Detection Using Machine learning

The exponential growth of social media such as Twitter and community forums has revolutionized communication and content publishing, but is also increasingly exploited for the propagation of hate speech and the organization of hate-based activities.

 

  1. Stock market prediction using Classification

Stock market prediction is the act of trying to determine the future value of a stock from social media social media offers a robust outlet for people 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.

 

  1. Student Performance analysis

The proposed framework analyzes the students i.e.; 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.

 

  1. Student feedback classification using Random Forest

The models for detecting i.e.; student states and for associating adaptive system strategies with such states were learned from tutoring dialogue corpora using new data-driven methods.

 

  1. Credit card fraud detection using Deep learning

It mainly focused on credit card fraud detection using Deep Learning. After classification process of random algorithm i.e.; to analyze data set and user provide current dataset. It will apply the processing of some of the attributes provided can find affected fraud detection in viewing the graphical model visualization.

  1. Fake News detection using machine learning

It describes incorrect and misleading articles published mostly for the purpose of making money through page views. The topic of machine learning methods for detection has been focusing on classifying online reviews and publicly available social media posts.

  1. Fake profile identification Machine learning

This method can be extended on any platform that needs binary classification to be deployed 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.

 

  1. Rainfall prediction using machine learning

Prediction of rainfall gives awareness to people and know in advance about rainfall to take certain precautions to protect their crop from rainfall. It was concluded the enhancements, optimizations and integrations of data mining methods are vital i.e.; to explore and solve these problems.

 

  1. Cyber Threat Analysis on Android Apps

It is an effectively and efficiently malicious applications detection tools i.e.; needed to tackle and handle new complex malicious apps created by hackers. With idea of using machine learning approaches for detecting the malicious android application.

 

  1. Student Performance Prediction – Machine Learning

Performance analysis outcome i.e.; based on learning is a system which will strive for excellence at different levels and diverse dimensions in the field of student’s interests. It analyzes the student’s demographic data, study related and psychological characteristics to extract all possible knowledge

 

  1. Hashtag Clustering using NLP | Machine Learning

Hashtag prediction is the task of mapping text to its accompanying hashtags. In this process a model for hashtag prediction show this task a useful surrogate for learning good representations of text. This hashtag i.e.; based detailed query show the result as whether it will be positive or negative and random forest algorithm.

 

  1. KDD & Data Mining Approach for Finding Network Attacks

With emerge of Big Data, i.e.; 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 system. The results of the experiment shows that model has high performance and efficient for Big Data.

 

  1. Churn Modelling Analysis using Deep Learning | Python

Deep learning is usually associated with having a high number of i.e.; input layers, one or more hidden layers that connect input layers and perform computational algorithms to determine a probability to predict.

  1. Diabetes Prediction using Machine Learning | AI | Python

This data set consists of information of user i.e.; 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.

 

  1. Student Placement Prediction using AI | Machine Learning

The main purpose of this research is to develop machine learning algorithms for predicting percentage of student placement i.e.; 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.

  1. Text Summarization using NLP I Machine Learning

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 the core information is conveyed i.e.; through shortest text possible.

 

  1. Rating Prediction using Machine Learning

The review text content analysis and uses the principles of natural language process. This method insights can be drawn from the relationship between i.e.; costumers and items. This is based on recommender systems, specifically on collaborative filtering, and focuses on the reviewer’s point of view.

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