Category: AI Artificial Intelligence Projects

Download all Artificial Intelligence AI Projects using Python. Here all students can able to view Artificial Intelligence Project Ideas, Synopsis, abstracts, Source Code, Project Reports, PPT, etc. Advanced Artificial Intelligence Projects for 20222 academic years also can be useful for Students.

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AI4Jobs: A Machine Learning Framework for Fake Recruitment Detection in Online Portals

AI4Jobs: A Machine Learning Framework for Fake Recruitment Detection in Online Portals

Abstract

With the growing influence of online job portals and recruitment platforms, fake job postings have become a major cyber threat, targeting job seekers and exploiting their personal information. This project proposes a machine learning-based system to automatically detect fake job postings using NLP and classification algorithms. By analyzing job descriptions and related metadata, the system leverages models like Logistic Regression, Random Forest, SVM, and others to classify whether a job post is genuine or fraudulent. The proposed model achieves high accuracy and enables real-time detection through a web interface built using Flask.

Introduction

In the digital age, job seekers often rely on online platforms for career opportunities. However, cybercriminals exploit this reliance by posting fraudulent job advertisements to collect sensitive information or extort money. Manual detection is inefficient due to the massive volume of listings. This system employs machine learning models to automate the classification of job postings as real or fake, ensuring a secure recruitment experience. It uses data pre-processing, TF-IDF feature extraction, and various supervised learning techniques to achieve accurate predictions.

Problem Statement

How can we accurately detect and classify fake job postings using automated systems that analyze textual content and metadata, in order to protect users from employment fraud?

Existing System and Disadvantages

Existing Systems:

  • Manual moderation of job posts
  • User reporting systems
  • Simple keyword filters or rule-based checks

Disadvantages:

  • Time-consuming and error-prone
  • Inability to scale to thousands of posts per day
  • High false negatives and low detection rates

Proposed System and Advantages

Proposed System:

  • A machine learning pipeline using Natural Language Processing (NLP) for text-based analysis
  • Multiple ML classifiers trained on historical job posting data
  • Flask-based web interface for user interaction and prediction

Advantages:

  • Automated, real-time detection of fake job posts
  • High classification accuracy using ensemble ML techniques
  • Scalable and adaptive to new data
  • Protects users from scams and identity theft

Modules

  1. Data Pre-processing Module
    • Cleans and prepares data by handling missing values and irrelevant features
  2. Feature Extraction Module
    • Applies TF-IDF to convert job descriptions into numerical vectors
  3. Model Training Module
    • Trains multiple models (Naive Bayes, SVM, Random Forest, etc.) on labeled data
  4. Prediction Module
    • Provides predictions for new job posts through the trained models
  5. Visualization & Analysis Module
    • Displays confusion matrix, accuracy graphs, and classification reports
  6. Web Interface Module (Flask)
    • Allows users to input job descriptions and get results

Algorithms / Models Used

  • Logistic Regression
  • Random Forest
  • Support Vector Machine (SVM)
  • Decision Tree
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • TF-IDF Vectorization for text processing

Software Requirements

Component

Technology

Frontend

HTML, CSS, JavaScript

Backend

Python, Flask

Database (Optional for logging)

MySQL

Libraries

scikit-learn, pandas, matplotlib, joblib, seaborn

Hardware Requirements

Component

Specification

Processor

Intel i3 or above

RAM

4GB minimum

Storage

100MB (for dataset and models)

OS

Windows/Linux/Mac

Conclusion

The project successfully demonstrates the use of machine learning to detect fake job postings with high accuracy. By analyzing textual data from job advertisements, the system can proactively classify fraudulent content and prevent potential exploitation of job seekers. The implementation of multiple ML models provides comparative performance insights, with ensemble models like Random Forest and SVM showing the best results.

Future Enhancement

  • Integrate deep learning models (LSTM or BERT) for better contextual understanding
  • Build an alert system to flag suspicious postings automatically on job portals
  • Include user feedback loops to improve model retraining
  • Extend support for multi-language job postings
  • Add database integration for storing logs and user sessions

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AGRICULTURE LAND CLASSIFICATION USING DEEP LEARNING

AGRICULTURE LAND CLASSIFICATION USING DEEP LEARNING

Abstract

Land cover classification is a fundamental task in remote sensing used for mapping different types of land surfaces such as forests, urban areas, water bodies, and agricultural land. This project leverages deep learning, specifically the U-Net++ architecture with EfficientNet-B3 encoder, to accurately classify satellite images. A Flask-based web application enables users to upload satellite images and view segmented output along with land distribution analysis. The model also computes and visualizes the percentage coverage of each land category in a given image, facilitating better understanding and decision-making for environmental monitoring and land use planning.

Introduction

Monitoring and managing land resources is vital in the context of urbanization, agriculture, deforestation, and environmental sustainability. Satellite imagery, combined with advanced deep learning techniques, provides a scalable solution for automatic land cover classification. Traditional classification methods often require extensive manual efforts or lack precision. This project addresses these limitations by integrating a deep learning segmentation model (U-Net++) into a user-friendly web interface for real-time land classification and analysis.

Problem Statement

Conventional land cover classification techniques struggle with:

  • Inconsistent accuracy across varying landscapes
  • Lack of scalability for large-scale monitoring
  • Minimal interactivity and user engagement in existing tools

There is a need for an automated, accurate, and interactive system that classifies land cover from satellite imagery and provides analytical insights to support planning and monitoring.

Existing System and Its Disadvantages

Existing Systems:

  • Manual classification or semi-automated tools in GIS software
  • Traditional machine learning with handcrafted features
  • Open-source tools lacking interactivity or model flexibility

Disadvantages:

  • Require domain expertise and manual labeling
  • Lack adaptability to new or unseen landscapes
  • Minimal support for live image upload and result visualization
  • Poor performance in noisy or complex regions

Proposed System and Its Advantages

Proposed System: This project proposes a deep learning-based web platform using U-Net++ with EfficientNet-B3 encoder, capable of segmenting satellite images into land cover classes and visually displaying the results.

Advantages:

  • High accuracy through advanced CNN architecture
  • Fully automated segmentation with minimal human input
  • Interactive and easy-to-use web interface
  • Displays land class distribution and charts for visual analytics
  • Supports expansion with more datasets or models

Modules

  1. User Upload Module
    • Upload satellite images and ground truth masks
  2. Pre-processing Module
    • Normalize and resize images using Albumentations
  3. Model Inference Module
    • Predict segmented image using trained U-Net++ model
  4. Postprocessing Module
    • Apply color mapping and calculate land area percentage
  5. Visualization Module
    • Generate bar chart and display results
  6. Web Interface (Flask)
    • Render upload form and prediction results dynamically

Algorithm / Model Used

Model: U-Net++ with EfficientNet-B3 Encoder

  • Encoder: EfficientNet-B3 (pre-trained on ImageNet)
  • Decoder: Nested skip connections (U-Net++)
  • Output: 7-class pixel-wise segmentation
  • Framework: segmentation_models_pytorch

Workflow:

  1. Preprocess input using Albumentations
  2. Pass through U-Net++ for segmentation
  3. Map classes to RGB for visualization
  4. Count pixels for land distribution stats

Software Requirements

  • Python 3.x
  • Flask
  • PyTorch
  • segmentation-models-pytorch
  • OpenCV
  • Albumentations
  • Matplotlib
  • HTML/CSS (Jinja2 templating)

Hardware Requirements

  • GPU (optional but recommended for training)
  • Minimum 8 GB RAM
  • CPU with multi-threading
  • Storage for model and static files

Conclusion

This project successfully demonstrates the power of deep learning for satellite image analysis through land cover classification. The system combines an advanced segmentation model with an intuitive web interface, enabling users to gain insights into land distribution from satellite data. The classification results and area-based analysis empower planners, environmentalists, and researchers to make informed decisions.

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Innovative AI and Data Science Projects using Python

Empowering Innovation: AI, ML, DL, and Cyber security Projects for the Future

Project ID Project Name Domain Language
AR-001 Hybrid Image Protection System Using Invisible Watermarking Cyber security/ Image Processing and Security Python
AR-002 Agriculture Land Classification using Deep Learning Deep Learning Python
AR-003 TravelBot-HYD NLP and RNN Based Urban Trip Planner Deep Learning/NLP Python
AR-004 Hyderabad Navigator Chatbot: Intelligent Trip Planning Using NLP and Random Forest Machine Learning/NLP Python
AR-005 Cloth(Fabric) Defect Detection using deep learning and Market Integration Deep Learning Python
AR-006 Stroke Prediction Using Machine Learning Models With Flask And Mysql Integration Machine Learning Python
AR-007 EduAssist: AI-Powered College Enquiry Chatbot Machine Learning/NLP Python
AR-008 AI-Based Healthcare System for Disease Prediction Using CNN and XGBoost with Chatbot Assistance Artificial Intelligence Python
AR-009 Diabetic Retinopathy Detection Using CNN with Inception v2 and Inception v3 Deep Learning Python
AR-010 Automatic Video Dubbing for Indian Regional Languages Artificial Intelligence and Multimedia Processing Python
AR-011 SMS Spam Detection using Machine Learning Machine Learning Python
AR-012 Evaluation of Academic Performance of Students Machine Learning Python
AR-013 Real Time Face Emotions Recognition Artificial Intelligence Python
AR-014 Smart Student Attendance System Integrating QR Codes and Facial Recognition Artificial Intelligence Python
AR-015 Enhancing Image Clarity with GANs A Deep Learning Approach to Super Resolution Deep Learning Python
AR-016 Detect Potato Disease A Deep Learning Approach for Early Detection of Potato Leaf Diseases Deep Learning Python
AR-017 Malaria Detection Using CNN Integrated with an Interactive Chatbot System Deep Learning/NLP Python
AR-018 Measuring Semantic Textual Similarity Using TF-IDF and Cosine Similarity Natural Language Processing  Python
AR-019 Dual-Mode Text Similarity Checker using TF-IDF and GloVe Embeddings in Flask Natural Language Processing  Python
AR-020 Videozen – Protecting Videos with Encryption and Decryption Using a Combination of Blowfish and AES for Security Cybersecurity / Information Security Python
AR-021 PhishNet Detecting Phishing URLs Using Convolutional Neural Networks Deep Learning/CNN Python
AR-022 DeepSpam Neural Network Approach to Detect Spam in YouTube Comments Artificial Neural Network Python
AR-023 SmartLand Real-Time Satellite Image Segmentation and Classification Using YOLOv8 for Sustainable Land Monitoring Deep Learning/YOLO Python
AR-024 DeepPhish Machine Learning Solutions for URL-Based Phishing Detection Machine Learning Python
AR-025 GetStego An Intelligent Web-Based Multimedia Steganography System for Secure Communication Cybersecurity and Information Security Python
AR-026 AI4Jobs A Machine Learning Framework for Fake Recruitment Detection in Online Portals Machine Learning Python
AR-027 Air Canvas Virtual Brushes in the Wind Using Python and OpenCV Artificial Intelligence Python
AR-028 LandMarkAI YOLOv8-Based Satellite Image Analysis for Sustainable Growth Deep Learning Python
AR-029 FitMind AI-Based Fitness and Mental Wellness Recommendation System with Chatbot Support Artificial Intelligence Python
AR-030 Rainfall Prediction Using Ensemble Learning Ensemble Learning Python
AR-031 AI-Based Skin Disease Detection and Patient Engagement System Deep Learning Python
AR-032 Early detection and classification of Alzheimer’s using Machine Learning Machine Learning Python
AR-033 Classification of Surya Namaskar Yoga Asana using Convolutional Neural Networks (CNN) Deep Learning Python
AR-034 Email Spam Detection Using Deep Learning Deep Learning Python
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Advanced AI/ML Data Science Projects 2025

ADVANCED PYTHON Based DATA SCIENCE PROJECTS on MACHINE LEARNING, ARTIFICIAL INTELLIGENCE, DEEP LEARNING, CYBER SECURITY & GENERATIVE AI

  1. Automated Emerging Cyber Threat Identification and Profiling Based on Natural Language Processing
  2. A Deep Attentive Multimodal Learning Approach for Disaster Identification From Social Media Posts
  3. Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
  4. LiDA: Language-Independent Data Augmentation for Text Classification
  5. Phishing Detection System Through Hybrid Machine Learning Based on URL
  6. DeepSide A Deep Learning Framework for Drug Side Effect Prediction
  7. ROAD The Road Event Awareness Dataset for Autonomous Driving
  8. PregBot :A System Based on ML and NLP for Supporting Women and Families during Pregnancy
  9. Parkinson’s Disease Detection Using Machine Learning
  10. Automated Detection of Cardiac Arrhythmia using Recurrent Neural Network
  11. An Efficient Spam Detection Technique for IoT Devices using Machine Learning
  12. Multi Class Stress Detection Through Heart Rate Variability A Deep Neural Network Based Study
  13. Web-Based Music Genre Classification for Timeline Song Visualization and Analysis
  14. Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations
  15. A Novel Approach for Disaster Victim Detection Under Debris Environments Using Decision Tree Algorithms With Deep Learning Features
  16. Price Negotiating Chabot with Text & Voice on E-commerce website
  17. Malicious URL Detection based on Machine Learning
  18. Deep fake Detection: A Systematic Literature Review
  19. DEARNN A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform
  20. Security of Cyber Physical Systems: Design of a Security Supervisor to Thwart Attacks
  21. A Deep Learning Based Efficient Firearms Monitoring Technique for Building Secure Smart Cities
  22. Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cyber security
  23. Child Mortality prediction using Machine Learning Techniques
  24. Improving Shopping Mall Revenue by Real Time Customized Digital Coupon Issuance
  25. Predicting Behavior Change in Students With Special Education Needs Using Multimodal Learning Analytics
  26. Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation
  27. Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart Cards A Deep Learning Approach
  28. Prediction of Air Pollution by using Machine Learning Algorithm
  29. Privilege Escalation Attack Detection and Mitigation in Cloud Using Machine Learning
  30. Propounding First Artificial Intelligence Approach for Predicting Robbery Behavior
  31. The Influence of Artificial Intelligence on E-Governance and Cyber security in Smart Cities
  32. WaterNet A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes
  33. Human Action Recognition from depth maps and Postures using Deep Learning
  34. Abnormal Traffic Detection Based on Attention and Big Step Convolution
  35. Behavior Analysis For Mentally Affected People
  36. Explainable Artificial Intelligence for Patient Safety A Review of Application
  37. Personalized Federated Learning for In-Hospital Mortality Prediction of Multi-Center ICU
  38. RFCNN Traffic Accident Severity Prediction Based on Decision Level Fusion of Machine and Deep Learning Model
  39. Water Quality Monitoring & Forecasting System
  40. Fake Profile Identification in Social Network using Machine Learning and NLP
  41. Fighting Money Laundering With Statistics and Machine Learning
  42. Malware Detection A Framework for Reverse Engineered Android Applications through Machine Learning Algorithms
  43. Optimal Ambulance Positioning for Road Accidents With Deep Embedded Clustering
  44. Phish Catcher Client-Side Defense Against Web Spoofing Attacks Using Machine Learning
  45. A Novel Approach to Improve Software Defect Prediction Accuracy Using Machine Learning
  46. BlockChain Based Federated Learning With SMPC Model Verification Against Poisoning Attack
  47. Effective Software Effort Estimation Leveraging Machine Learning for Digital Transformation
  48. A Multi-perspective Fraud Detection Method for Multi-Participant E-commerce Transactions
  49. Creating Alert Messages Based On Wild Animal Activity Detection Using Hybrid Deep Neural Networks
  50. Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual Transformers
  51. Two Stage Job Title Identification System For Online Job Advertisements
  52. An Efficient Privacy Enhancing Cross Silo Federated Learning And Applications For False Data Injection Attack Detection In Smart Grids
  53. Thyroid Disease Prediction Using Machine Learning
  54. Suicidal Tendency Detection
  55. Identifying Student Profiles within Online Judge systems using Explainable Artificial Intelligence
  56. Trustworthy and Reliable Deep-Learning-Based Cyber-attack Detection in Industrial IoT
  57. Deep Learning Anti-Fraud Model for Internet Loan Where We Are Going
  58. Social Media and Misleading Information in a Democracy: A Mechanism Design Approach
  59. Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
  60. Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning
  61. Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers
  62. EPilots A system to predict hard landing during the approach phase of commercial flights
  63. Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms
  64. Campus Placements Prediction & Analysis using Machine Learning
  65. Prediction Of Used Car Prices Using Artificial Neural Networks And Machine Learning
  66. Trustworthiness Assessment of Users in Social Reviewing Systems
  67. Personality aware Product Recommendation System based on User Interests Mining and Meta path Discovery
  68. Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security
  69. Mining Fraudsters and Fraudulent Strategies in Large-Scale Mobile Social Networks
  70. Mitigating COVID-19 Transmission in Schools With Digital Contact Tracing
  71. A Machine Learning Approach For Opinion Mining Online Customer Reviews
  72. Prediction of Air Pollution by using Machine Learning Algorithm
  73. Detection of Fake and Clone accounts in Twitter using Classification and Distance Measure Algorithms
  74. A Spam Transformer Model for SMS Spam Detection
  75. A Student Attendance Management Method Based on Crowd sensing in Classroom Environment
  76. BullyNet Unmasking Cyberbullies on Social Networks
  77. Predicting Urban Water Quality With Ubiquitous Data – A Data-Driven Approach
  78. Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms
  79. Machine Learning Based Heart Disease Prediction System
  80. Detection of Cyberbullying on Social Media Using Machine learning
  81. DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions
  82. TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks
  83. RFCNN: Traffic Accident Severity Prediction Based on Decision Level Fusion of Machine and Deep Learning Model
  84. Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps
  85. Machine Learning for Real-Time Heart Disease Prediction
  86. DeepGrip: Cricket Bowling Delivery Detection with Superior CNN Architectures
  87. CSKG4APT: A Cyber security Knowledge Graph for Advanced Persistent Threat Organization Attribution
  88. Machine Learning-Based Analysis of Crypto Currency Market Financial Risk Management
  89. Machine Learning and End-to-end Deep Learning for the Detection of Chronic Heart Failure from Heart Sounds
  90. Joint Resource Allocation and Trajectory Optimization for Multi-UAV-Assisted Multi-Access Mobile Edge Computing
  91. Movie Success Prediction Using Naïve Bayes, Logistic Regression and Support Vector Machine
  92. Earthquake Early Warning (EEW) system; Machine learning; Earthquake Location
  93. Block Hunter Federated Learning for Cyber Threat Hunting in Block chain-based IIoT Networks
  94. Detection Of Thyroid Disorders Using Machine Learning Approach
  95. Social Media and Misleading Information in a Democracy: A Mechanism Design Approach
  96. Optimize The Storage Volume Using Data Mining Techniques
  97. Analysis of Women’s Safety in Indian Cities using Machine Learning on Tweets
  98. Feature-Level Rating System Using Customer Reviews and Review Votes
  99. Food Demand Forecasting Using Machine Learning and Statistical Analysis
  100. Strategic Improvements in Malls for Customers by Using Machine Learning
  101. Car Price Prediction Using Random Forest Regression Machine Learning
  102. Car Price Prediction Using Linear Regression Model in Machine Learning
  103. Face Recognition Attendance System Using Machine Learning
  104. Face Detection and Recognition for Criminal Identification System
  105. Cyberbullying Detection Using Machine Learning
  106. Rainfall Prediction Using Machine Learning
  107. Prediction Of Loan Eligibility of The Customer
  108. Driver Attentiveness Detection System with OpenCV Using Machine Learning
  109. Driver Sleep Detection and Alarming System Using Machine Learning
  110. A Data Mining Approach Combining K-Means Clustering with Bagging Neural Network Driver
  111. A Machine Learning-Based Lightweight Intrusion Detection System for The Internet of Things
  112. Chatbot Application Using Machine Learning
  113. Forensic Scanner Identification Using Machine Learning
  114. Prediction Of Student’s Performance Using Educational Data Mining
  115. Soil Moisture Retrieval Using Groundwater Dataset Using Machine Learning Algorithm
  116. Stock Market Analysis Using Machine Learning
  117. Stress Detection in It Professional by Image Processing and Machine Learning
  118. Driver Drowsiness Alert Detection Using Machine Learning
  119. Face Recognition Using Machine Learning
  120. Machine Learning to Detect Emotions in Real-Time Based on Facial Expressions
  121. Study Of Blockchain Technology in The Farmers Portal
  122. Travelsense: Personalized Travel Recommendation and Itinerary Planner
  123. Fake News Detection Using Machine Learning
  124. Car Parking Number Plate Detection Using Machine Learning
  125. Running Car Number Plate Detection System
  126. Crime Rate Prediction & Analysis Using K-Means Clustering Algorithm
  127. Detection Of Fake Job Recruitment Using Machine Learning Techniques
  128. After Capturing and Processing the Image for Authentication Using Machine Learning
  129. Crime Activity Detection Through Videos Using Machine Learning
  130. Real-Time Object Detection
  131. Real-Time Chatbot Assistant
  132. Advanced Image Captioning with Deep Learning
  133. Object Tracking Using Python from Video
  134. Detection Of Brain Stroke Disease Using Machine Learning
  135. Modeling And Predicting Cyber Hacking Breaches
  136. Detection Of Lung Cancer from Ct Image Using SVM Classification and 3D CNN On Lung Nodules Data Set
  137. Email Spam Filter Using Natural Language Process
  138. House Price Prediction Using Machine Learning
  139. SMS Spam Detector Using Machine Learning
  140. Detection Of Cyber Attacks in Network Using Machine Learning Techniques
  141. Currency Detection Using OpenCV in Machine Learning
  142. Emotion-Based Music Recommendation System
  143. Diagnosing Chronic Kidney Disease Using Hybrid Machine Learning Algorithms
  144. Image-Based Product Recommendation System
  145. Breast Cancer Detection Using Machine Learning
  146. Question Paper Generation System
  147. Comparison Of Machine Learning Algorithms for Predicting Crime Hotspots
  148. Image Encryption and Decryption Using 3DES Encryption
  149. File Encryption and Decryption or Using Cryptography and Tkinter
  150. Online Grocery Shopping System
  151. Hotel Management System
  152. Hospital Information Management System
  153. Text Recognition OCR with Tesseract and OpenCV
  154. Object Counting and Tracking with OpenCV and Python
  155. Eye Blinking Detection
  156. Count Vehicles on Images with OpenCV and Deep Learning
  157. Face Landmarks Detection – OpenCV with Python
  158. Add Watermark to Multiple Images with OpenCV and Python
  159. Face Swapping Detection Using OpenCV
  160. Road Lines Detection Hough Transform Using OpenCV
  161. Rental Bike Sharing Using Machine Learning
  162. Crop Recommendation (Smart Farming) Using Machine Learning
  163. Dall E Creating Images from Text Using OpenCV
  164. Multiple Disease Prediction System Using Machine Learning in Python
  165. Collaborative Filtering-Based Book Recommender System Using Machine Learning
  166. Flight Price Prediction Using Machine Learning
  167. Employee Salary Prediction Using Machine Learning
  168. Real-Time Stock Price Prediction and Market Analysis Using Machine Learning
  169. Enhancing Visual Intelligence: Streaming Object Detection and Recognition with Machine Learning in Image, Video, And OpenCV
  170. Online Course Recommendation System Using Machine Learning
  171. Automated Pest Detection Using Image Classification
  172. Traffic Rules Violations Detection System
  173. Handwriting Recognition System
  174. Characterizing And Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites
  175. Sentiment Analysis of Good reads Reviews Using Machine Learning
  176. Analyzing And estimating the IPL Winner Using Machine Learning
  177. Book Genre Classification Using Machine Learning
  178. Error Detection Credit Card Using Machine Learning
  179. Analyzing And estimating the IPL Winner Using Machine Learning
  180. Performance Analysis and Evaluation of Machine Learning Algorithms in Rainfall Prediction
  181. Artificial Intelligence-Based Fake Currency Detection System
  182. Evaluation Of Machine Learning Algorithms for The Detection of Fake Bank Currency
  183. Detecting The Movement of Objects with Webcam and Alert Using Machine Learning
  184. Currency Detection and Verification Using OpenCV
  185. Real Time Car Number Plate Detection Using OpenCV
  186. A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs   
  187. 5g-Smart Diabetes Toward Personalized Diabetes Diagnosis with Healthcare Big Data Clouds
  188. Detection Of Lung Cancer from Ct Image Using SVM Classification And 3d CNN on Lung Nodules Data Set
  189. Automatic Age and Gender Classification
  190. Emotion-Based Music Player Using OpenCV
  191. Real Time Face Emotion Detection Using OpenCV in CNN Model
  192. Emotion Based Music Player Using Artificial Intelligence
  193. Block chain Based Certificate Validation
  194. Detection And Classification of Fruit Diseases Using Image Processing and Cloud Computing
  195. Personal Voice Assistant
  196. Smart Drug Recommendation and Disease Prediction System
  197. Drug Recommendation System Based on Sentiment Analysis of Drug Reviews Using Machine Learning
  198. Medicine Drugs Recommendation System Using Content-Based Filtering
  199. Face Mask Detection Using Tensor Flow and Kera’s
  200. Face Mask Detection System Using OpenCV- Streamlit App
  201. Fish Disease Detection Using Image Based Machine Learning Technique In Aquaculture
  202. Sign Language Detection Using Machine Learning
  203. Vehicles Speed Detection System Using OpenCV
  204. Speed Estimation and Number Plate Detection Using OpenCV
  205. Hand And Face Landmarks Detection Media pipe Using Artificial Intelligence
  206. Iris Mouse Or Eye Mouse Controlled Using OpenCV
  207. Beautify Using OpenCV
  208. Analysis And Prediction of Covid-19 Using Time Series Forecasting
  209. Analysis And Prediction of Occupational Accidents
  210. Artist Recommendation System Using Collaborative Filtering
  211. Biometric Steganography Using Mid Position Value Technique
  212. Classification Of Covid-19 Using Chest X-Ray
  213. Covid-19 Future Forecasting
  214. Covid19 Social Distance Monitoring System Using Yolo
  215. Credit Card Fraud Detection
  216. Detection Of Malicious Code Variants Based on Deep Learning
  217. Face To Emoji Using OpenCV and HaarCascade Classifier / Facial Expression Recognition Emoji
  218. Fusion Approach to Infrared and Visible Images
  219. Money Laundering Detection Using Machine Learning Methods
  220. Prediction Analysis Using Support Vector Machine in Cardiovascular Ailments
  221. Vehicle Pattern Recognition Using Machine Learning and Deep Learning to Predict Car Model
  222. A Hybrid Fuzzy Logic-based Deep Learning Approach for Fake Review Detection
  223. A Joint Multi-Task CNN for Cross-Age Face Recognition
  224. Advertisement Classification using machine learning
  225. Android malware detection using Machine learning techniques
  226. Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems
  227. Black Friday Sales Prediction Using ML
  228. Detection of Child Predators Cyber Harassers on Social Media
  229. Retinal Disease Prediction using Machine Learning
  230. Detection of fake online reviews using Supervised Learning and Non-Supervised Learning–JAVA
  231. Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and LASSO Feature Selection Techniques
  232. Early detection and prediction of Alzheimer disease
  233. Desktop Voice Assistant
  234. Detection of Phishing Websites using Supervised Machine Learning Techniques
  235. Detection Phishing Websites using Machine Learning
  236. Plant disease detection and Classification Using ML
  237. Plant Species Health Detection Using AI
  238. Plagiarism Checker Using Flask
  239. Weapons Detection using OpenCV
  240. Depression Detection on Tweets Using Machine Learning
  241. Pan Card Tempering Detector
  242. EDA – Bank Loan Default Risk Analysis
  243. Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems
  244. Block chain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems
  245. Lip Reading using Neural Network and Deep learning
  246. Machine Learning Algorithm For Stroke Disease Classification
  247. Human Activity Recognition
  248. Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments
  249. Estimating the price of houses using machine learning
  250. Semi Supervised Machine Learning Approach for DDoS detection
  251. Traffic Sign Detection And Recognition
  252. twitter sentiment analysis using machine learning
  253. Suicide tweet detection machine learning
  254. Passive Aggressive Classifier for Detection of Encrypted VPN
  255. Building Search Engine Using Machine Learning Technique
  256. Converging Block chain and Machine Learning for Healthcare
  257. Data Analysis by Web Scraping using Python
  258. A Corona Recognition Method Based on Visible Light Color and Machine Learning
  259. Machine Learning Approach to predict autism spectrum disorder
  260. Detection of Cyber Attack in Network Using Machine Learning Techniques
  261. Filtering Instagram Hashtags Through Crowd tagging and the HITS Algorithm
  262. Generating Cloud Monitors from Models to Secure Clouds
  263. Employee salary hike prediction using machine learning
  264. Crypto currency Price Analysis with Artificial Intelligence
  265. Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles
  266. Lifestyle Disease Prediction
  267. Software Defect Estimation Using Machine Learning Algorithms
  268. Characteristics & predicating early reviews for effective product marketing one commerce websites
  269. Analysis and Prediction of Industrial Accidents Using Machine Learning
  270. Facial Emotion Reorganization using CNN
  271. Detecting Mental Disorders in Social Media Through Emotional Patterns – The case of Anorexia and Depression
  272. Fake Account Detection using Machine Learning
  273. Use of Artificial Neural Networks to Identify Fake Profiles
  274. A Driving Decision Strategy (DDS) Based on Machine learning for an autonomous vehicle
  275. Fake Media Detection Based on Natural Language Processing and Block chain Approaches
  276. A Block chain based Autonomous Decentralized Online Social Network
  277. Quality Risk Analysis for Sustainable Smart Water Supply Using Data Perception
  278. Block chain-based Decentralized Authentication Modelling Scheme in Edge and IoT Environment
  279. A Machine Learning Model for Average Fuel Consumption in Heavy Vehicles.
  280. Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data a Comparative Analysis
  281. Privacy Preserving Location Data Publishing: A Machine Learning Approach
  282. Spammer Detection And Fake User Identification On Social Network
  283. Face Recognition and Crime detection
  284. Hepatitis disease Prediction Using Machine Learning classification techniques
  285. Intrusion Detection System using KDD dataset and Deep learning methods
  286. Automating E-Government using AI
  287. Building Search Engine using Machine Learning Algorithm
  288. Breast Cancer Detection
  289. Customer Segmentation Using Clustering And Data Mining Techniques
  290. Comprehensive and Scalable Stock Market Analysis System using Python
  291. Detecting Spam Email with Machine Learning Optimized with Bio- Inspired Metaheuristic Algorithms
  292. Securing Data With Block Chain And Ai
  293. Automatic Identification Of Malaria-Infected Cells Using Deep Convolutional Neural Network
  294. Employee Block chain In Agriculture
  295. Decentralized Web Hosting Using Block chain
  296. Activity Minimization Of Misinformation Influence In Online Social Networks
  297. Biometric Information Recognition Using Artificial Intelligence Algorithms
  298. Employees Face Recognition System
  299. Eye Ball Cursor Movement Using OpenCV
  300. QR Code Based Attendance System Using Deep Learning
  301. Detection Of Fake News Through Implementation Of Data Science Application
  302. A Survey of Driving Safety with Sensing, Vehicular Communications, and Artificial Intelligence-Based Collision Avoidance
  303. Phishing Website Detection Using Machine Learning Algorithms
  304. Smart Health Consulting Online System Using Block chain
  305. Multi Disease Prediction Using Machine Learning
  306. Online Voting System Using Eye Matching Using Hamming Algorithm
  307. Suspicious Activity Detection Using Deep Learning
  308. Detection of Supra Ventricular Arrhythmia using LSTM, BI-LSTM & GRU
  309. A Deep Learning-Based Approach for Inappropriate Content Detection and Classification of YouTube Videos
  310. Block chain E-Voting Done Right: Privacy and Transparency with Public Block chain
  311. Digital Diary with Chatbot
  312. Body Fat Estimator
  313. Watermarking Images using Deep Learning
  314. Predict Zomato Restaurant Ratings
  315. Cricket Match Outcome Prediction Using Machine Learning
  316. Flight Delay Prediction with Error Calculation using Machine Learning
  317. Online Disease Detection and Prediction Analysis
  318. Electronic Voting Machine using Artificial Intelligence-AI_EVM
  319. Fake Marriage Profile Detection
  320. Biometric Information Recognition Using Artificial Intelligence
  321. Fake OR Real Face Detection using OpenCv
  322. Skin Cancer Detection Squamous Cell Carcinoma
  323. Intermediate Fasting Application
  324. Sentiment Analysis using Machine Learning and Flask App
  325. Vehicle Parking Lot Monitoring System using OpenCV
  326. Sentiment Analysis Django WebApp
  327. Garbage Classification using Convolutional Neural Network
  328. Real Time Vehicle Identification and Detection System Along with Vehicle Counting
  329. Enhancing Visual Intelligence Streaming Object Detection and Recognition with Machine Learning in Image, Video, and OpenCV
  330. Brain Stroke Prediction and Analysis of Machine Learning
  331. Heart Disease Prediction Using Machine Learning Algorithms
  332. Text Classification on Twitter Data
  333. An Efficient Approach for Interpretation of Indian Sign Language using Machine Learning
  334. Suspicious Activity Detection using Machine Learning
  335. Multi classifier interactive learning for ambiguous speech emotion recognition
  336. Image Understanding Through Deep Learning with Automatic Caption Generation
  337. Disease prediction in particular area or crop
  338. Helmet Detection using Machine Learning
  339. Healthcare-Multi Diseases Prediction Using ML
  340. Movie Recommendation System Using Machine Learning
  341. Farming Made Easy using Machine Learning
  342. Campus Placement Prediction using Machine Learning
  343. Estimate Distance from the camera for detected faces
  344. Human Stress Detection in and through Sleep Using ML- (Smart Sleeping)
  345. Ingredients identification from the food image
  346. Recipe Generation from Food Image
  347. Diabetic Retinopathy Detection using CNN
  348. SMARTEDPRO: Integrated Smart Education Management System / Student Management System/College management System
  349. Blood Bank Management System
  350. Hospital Management System
  351. Library Management System
  352. Insurance Management System
  353. E-Learning Assessment
  354. Vehicle Service Management System
  355. Next-Generation Secure E-Commerce Platform / Online Shopping
  356. Integrated Educational Administration: A Comprehensive School Management System
  357. Robust Malware Detection For IOT Devices Using Deep Eigenspace Learning
  358. AI Driven Drug Interaction Prediction System
  359. Ai Powered Biomedical Image Analysis
  360. Ai-Enhanced Elderly Care Companion
  361. Hand Written Text Conversion Into Editable Digitable Format
  362. Evolution Of Deep Learning-Based Sequential Recommender Systems: From Current Trends To New Perspectives
  363. Fruit Disease Detection And Prevention Using Generative Adversarial Networks
  364. Brain Tumour Detection Using Deep Learning
  365. Epilots A System To Predict Hard Landing During The Approach Phase Of Commercial Flights
  366. Real Time Earthquake Early Warning System using Machine Learning
  367. Startup Success Prediction Using Machine Learning
  368. Development of Multilingual Video Dubbing Software for Indian Regional Languages
  369. Web Based Graphical Password Authentication System
  370. Web based turf playground booking hub
  371. Timetable Generation System
  372. QR Based Food Ordering System
  373. Stock Price Prediction using Twitter Dataset
  374. Depression Detection Using ECG
  375. Secure crypto-biometric system for cloud computing
  376. Audio to Sign Language Translator Using Python
  377. Predicting Accuracy of Players in the Cricket using Machine Learning
  378. Recovery Of Image Using One Dimensional Signal
  379. Early Detection of Cancer using AI
  380. Kidney Stone Detection using Ultrasound Images
  381. Parking Management System
  382. Toll System using Automatic Number Plate Detection
  383. Machine Learning Based Prediction Model for College Admission / Prediction of Admission in Engineering College
  384. Future of Loan Approvals with Explainable AI
  385. Al Powered Server Log Management Software
  386. Microwave Medical Image Segmentation for Brain Stroke Diagnosis: Imaging-Process-Informed Image Processing
  387. Two Factor Worm Detection Based on Signature & Anomaly
  388. Children ADHD Disease Detection using Pose Estimation Technique
  389. Exam Invigilation Duty
  390. IOT Based Smart Irrigation System using Reinforcement Learning
  391. Detecting Web Attacks with End-to-End Deep Learning
  392. Blockchain Traceability for Sustainable Agriculture
  393. Smart Farming: Crop Selection, Fertilizer Advice, Disease detection and prevention using Deep Learning
  394. Movie Recommendation System Using Sentiment Analysis From Microblogging
  395. Automated Resume Analysis & Skill Suggesting Website using NLP
  396. A Machine Learning Approach Using Statistical Models for Early Detection of Cardiac Arrest in Newborn Babies in the Cardiac Intensive Care Unit
  397. Artificial Intelligence Crime: An Overview of Malicious Use and Abuse of AI
  398. Discover Customers’ Gender From Online Shopping Behavior
  399. Flight Delay Prediction Based on Aviation Big Data and Machine Learning
  400. Predicting the Rice leaf diseases using CNN
  401. Prediction of Engineering branch selection for Inter Students
  402. A Measurement Approach for inline Intrusion Detection of Heartbleed-like Attacks in IoT Frameworks
  403. Emotion detection-based movie and music recommendation
  404. Fake Logo Detection
  405. Signature Verification System
  406. Auto capture Selfie by Detecting Smile
  407. Online Nursery Plant Shopping Website Python
  408. An experiment study for software quality prediction with machine learning
  409. HealthCare Chatbot
  410. Online Organic Health Food Store Project
  411. Online price comparison website python
  412. Text and image plagiarism detection
  413. Deep Learning Based Road Accident Detection System
  414. Station-Level Demand Prediction for Bike-Sharing System
  415. Personalized Travel Planning System
  416. Stock Price Prediction Using LSTM
  417. Smart Quiz: A Comprehensive Online Quiz Management System
  418. Indian Railway Booking System
  419. Online Fire Reporting System
  420. Online College Magazine
  421. Online Medicine Donation
  422. E-Diary Management System/ Smart Note Manager
  423. Gym Management System
  424. Loan Management System
  425. Online Banking System
  426. Vehicle Parking Management System
  427. Online Notes and Material Sharing
  428. Online Voting System
  429. Steganography For Text, Image, Audio And Video Using Python
  430. Network Intrusion Detection Using Supervised Machine Learning Technique With Feature Selection
  431. Auditorium booking web application using Python
  432. HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System
  433. Real Time Personalized Physiologically Based Stress Detection for Hazardous Operations
  434. Traffic Route Prediction
  435. Food Calorie Count Using Django
  436. Estimation of Nutrition Components
  437. Ground water Level Prediction Using Hybrid Random Forest And DCNN
  438. Crime Data Analysis and Prediction Using Decision Tree
  439. AI Based FAQ Chatbot with Voice Assistance
  440. An Online Learning Platform with Intelligent Tutoring using AI Components
  441. Liver Disease Prediction using GA features selection, Social Spider Optimization and CNN Classification
  442. Online Book Reselling System
  443. Secure Communication using DiffieHellman Key Exchange, AES & SHA for Data Integrity
  444. Vehicle 2 Vehicle Communication
  445. Image Gallery using Google Cloud
  446. Road Accident Severity & Hospital Recommendation using Deep Learning Techniques
  447. A Novel Web Attack Detection System for Internet of Things via Ensemble Classification
  448. Supermarket Basket Project
  449. Suicidal Ideation Detection A Review of Machine Learning Methods and Applications
  450. Food Tech BlockChain Using Python
  451. Crop Yield Prediction using Machine Learning Algorithm
  452. Attendance Capture System Using Face Recognition
  453. Tomato leaf disease detection
  454. Big Mart Sales Prediction Using Machine Learning
  455. Garbage Management System
  456. A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection
  457. SecureWeb: A Novel Machine Learning Methodology for Identifying Cross-Site Request Forgery Vulnerabilities
  458. Intelligent Bird Sound Recognition
  459. NSE Stock Monitoring & Prediction using Robotic Process Automation
  460. Chronic Kidney Disease Prediction using CNN, LSTM and Ensemble Model
  461. Deep Cross-modal Face Naming for People News Retrieval
  462. Grape Leaf Disease Identification using Machine Learning Techniques
  463. Campus Abnormal Behavior Recognition With Temporal Segment Transformers
  464. Explainable Artificial Intelligence for Patient Safety A Review of Application
  465. Federated Learning Based Face and Eye Blink Recognition
  466. Medilink One Health One Card
  467. Navigating the Path to Academic Success
  468. PupilHeart: Heart Rate Variability Monitoring via Pupillary Fluctuations on Mobile Devices
  469. Real-time Traffic Surveillance and Detection using Deep Learning and Computer Vision Techniques
  470. Using Existing CCTV Network for Crowd Management, Crime Prevention & Work Monitoring using AI & ML
  471. A Systematic Review of Predicting Elections Based on Social Media Data
  472. Online College Magazine
  473. Groundwater Level Prediction Using Hybrid Artificial Neural Network with Genetic Algorithm
  474. Electricity Theft Detection In Power Grids With Deep Learning And Random Forests
  475. Doctor Appointment Booking System
  476. Epileptic Seizures Prediction Using Deep Learning Techniques
  477. Classification of Software Defined Network Traffic to Provide Quality of Service
  478. Lung Cancer Stages Prediction
  479. A Machine Learning Modelling for Bitcoin Market Price Prediction Based on the Long Short Term Memory Recurrent Neural Network
  480. Automatic Assessment of Communication Skill in Non-conventional Interview Settings: A Comparative Study
  481. Data Driven Energy Economy Prediction for Electric City Buses Using Machine Learning
  482. Detecting and Mitigating Botnet Attacks in Software-Defined Networks Using Deep Learning Techniques
  483. Privilege Escalation Attack Detection and Mitigation in Cloud Using Machine Learning
  484. An AI-Based Medical Chatbot Model for Infectious Disease Prediction
  485. Graph Convolutional Network-Based Model for Megacity Real Estate Valuation
  486. Efficient Email Phishing Detection Using Machine Learning
  487. Data Poisoning Attacks on Federated Machine Learning
  488. Representing Fine-Grained Co-Occurrences for Behavior-Based Fraud Detection in Online Payment Services
  489. GEFA Early Fusion Approach in Drug-Target Affinity Prediction
  490. Agricultural Land Image Classification Using Regression Neural Network and Compare with KNN for Feature Extraction
  491. MADNet: A Fast and Lightweight Network for Single-Image Super Resolution
  492. Classification of Diabetic Retinopathy Disease Levels by Extracting Topological Features Using Graph Neural Networks
  493. Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews
  494. Mammogram Detection Using Machine Learning
  495. Securing Data in the Image Using SHA & ECC
  496. Hashtag-Based Tweet Expansion for Improved Topic Modelling
  497. Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learning Model
  498. Collaborative Filter Based Group Recommendation for Crowd funding
  499. Predictive Analysis of Medical Insurance Charges Using Linear Regression for Personalized Cost Estimation
  500. Image Forgery Detection using Machine Learning and CNN
  501. Smart Scene: Automated Captioning with Generative AI
  502. Automated Road Condition Classification for Pothole Detection Using Deep Learning
  503. Fingerprint Based Blood Group Identification / Automated Blood Group Detection
  504. Medical Insurance Premium Prediction With Machine Learning
  505. Integrated Quality Prediction Dashboard for Water, Air, and Wine
  506. Early Detection of Down Syndrome in Children Using Convolutional Neural Networks
  507. Email Spam Detection Using Machine Learning Algorithms
  508. Location prediction on Twitter using machine learning Techniques
  509. Virtual Mouse Operation Using Webcam
  510. Emotion Detection using Twitter Datasets and Spacy Algorithm
  511. Designing Secure and Efficient Biometric
  512. Water Quality Monitoring & Forecasting System
  513. Road Traffic Vehicle Detection and Tracking using Deep Learning with Custom-Collected and Public Datasets
  514. Vitamin Deficiency And Food Recommendation System Using Machine Learning
  515. DL-IDF: Deep Learning Based Intrusion Detection Framework in Industrial Internet of Things
  516. Classifying Soil Texture Using RBG Images In Uncontrolled Field Settings
  517. Smart Farming Fruit Disease Detection through Color, Texture Analysis, and Neural Networks
  518. Video captioning using Multi-model Attention-based Transformer Approach
  519. A Novel Approach to Predict Blood Group using Fingerprint Map Reading
  520. Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review
  521. A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model
  522. Analyzing Employee Retention Factors using Machine Learning
  523. Faculty Management System
  524. A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction
  525. Propounding First Artificial Intelligence Approach for Predicting Robbery Behavior Potential in an Indoor Security Camera
  526. Noise Reduction in Web Data A Learning ApproachBased on Dynamic User Interests
  527. A Forest Fire Identification Method for Unmanned Aerial Vehicle Monitoring Video Images
  528. Human Activity Recognition Using SmartPhone Data
  529. Automatic Detection of Genetic Diseases in Paediatric Age Using Pupillometry
  530. Detection of Stroke Disease using Machine Learning Algorithms
  531. Energy efficient teaching-learning-based optimization for the discrete routing problem in wireless sensor networks
  532. Classification of Oral Cancer Into Pre-Cancerous Stages From White Light Images Using LightGBM Algorithm
  533. Applying Machine Learning Algorithms for the Classification of Sleep Disorders
  534. The Short Video Popularity Prediction Using Internet of Things and Deep Learning
  535. Short-Term Arrival Delay Time Prediction in Freight Rail Operations Using Data-Driven Models
  536. Human Stress Detection Based on Sleeping Habits
  537. Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network
  538. AUTOMATING E-GOVERNMENT SERVICES WITH MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
  539. Lightweight Privacy Preserving Medical Diagnosis in Edge Computing
  540. Semantics of Data Mining Services in Cloud Computing
  541. Image segmentation on satellite images
  542. Enhancing Image Clarity with GANs A Deep Learning Approach to Super-Resolution
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Automatic Video Surveillance System AI & IOT Project

Surveillance is an integral part of security. The main objective of the Video Surveillance system IoT project is to build an effective system that can be used across different domains and technologies. The system is used to Detect Human intervention and breach in personal or commercial property of the user in real-time using AI and IoT.

It helps users to secure their property with the help of advanced artificial intelligence. The resulting system is fast and accurate, thus helping users with more secure surveillance systems.

For the most part, the job entails looking out for something undesirable to happen. The application is to have a system that provides real-time monitoring and alert security when a human is detected in a user’s property in their absence.

OBJECTIVES:

The main objective is to build an effective Video Surveillance System that can be used across different domains and technologies. The system is used to detect people trying to breach security in the personal or commercial property of the user in real time and send a message along with a short video clip to the user. 

PURPOSE OF EXISTING SYSTEM:

Currently, the existing Rocker Bogie Suspension Systems Project or surveillance robot for defense Surveillance systems can keep video recordings of homes, offices, banks, and so on. But that is useful only after an incident or robbery happens. No Real-time Updates are provided when there is a breach in real-time. 

Just imagine, You’re at your home and someone breaks security and stole money or goods from your office or property. Or consider you’re out of town for some days and there is a robbery at your home. So after you come back home or someone identifies it after some time and they will update you about the breach at your place.

You can take any action after a breach has been done, not at the time it is being done. That issue will be resolved in our system with real-time monitoring and updates.

SCOPE OF SYSTEM:

The Video Surveillance System can be implemented in any residential, Industrial, or commercial property. The system recommends detecting any human intervention on the user’s property and sends a notification along with a threshold of a 10-second video clip immediately as soon as it detects humans.

PROBLEM DEFINITION:

This Video Surveillance System project aims to develop an advanced Surveillance system that can keep on monitoring homes, offices, banks, and so on. With the help of this, you can find out if anyone breaches your security in your absence. We have to simply integrate our system into users existing surveillance systems. 

Module specification: 

  1. Raspberry pi
  2. Camera
  3. Server
  4. SNS
  5. S3  

Need Of Modules:

  • Raspberry pi as a Client to send frames to the server.
  • Camera to capture live video streams.
  • Server for processing frames and detecting humans.
  • SNS sends a multimedia message to a user when someone tries to breach security.
  • S3 to store a short video clip of the breach and send it to the user.

Non-Functional requirement.

EFFICIENCY REQUIREMENT :

When AI is taking care of your property then customers can relax and not have to worry about their security.

RELIABILITY REQUIREMENT :

 The system should provide a reliable environment for both the client and the server.

USABILITY REQUIREMENT :

The system is designed for a secure environment and ease of use.

IMPLEMENTATION REQUIREMENT :

Implementation of the system with pi, night vision camera, python, machine learning, and AI.

DELIVERY REQUIREMENT

The whole system is expected to be delivered in four months of time with a weekly evaluation by the project guide.

Limitations of the System:-

False Positives

Due to different light variant conditions and camera resolutions, sometimes the system detects humans as false when there is none but that can be neglected if there is a human and the system doesn’t detect it then there should be a problem.

Limited Processing Power

As we are using a microcontroller to send feeds to the server, it cannot handle multiple feeds at once and will be slower as the device increases.

Download Automatic Video Surveillance Management System Project Python Code, Documentation & report, Paper Presentation PPT

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Decision Model for Prediction of Movie Success Rate Data Mining J Component Project

ABSTRACT

The purpose of this Movie Success Rate Prediction project is to predict the success of any upcoming movie using Data Mining Tools. For this purpose, we have proposed a method that will analyze the cast and crew of the movie to find the success rate of the film using existing knowledge. Many factors like the cast (actors, actresses, directors, producers), budget, worldwide gross, and language will be considered for the algorithm to train and test the data. Two algorithms will be tested on our dataset and their accuracy will be checked.

 LITERATURE REVIEW

  • They developed a model to find the success of upcoming movies based on certain factors. The number of audience plays a vital role in a movie becoming successful
  • The factorization Machines approach was used to predict movie success by predicting IMDb ratings for newly released movies by combining movie metadata with social media data
  • Using the grossattribute as a training element for the model. The data are converted into .csv files after the pre-processing is done
  • Using S-PLSA – the sentiment information from online reviews and tweets, we have used the ARSA model for predicting the sales performance of movies using sentiment information and past box office performance.
  • A mathematical Model is used to predict the success and failure of upcoming movies depending on certain criteria. Their work makes use of historical data in order to successfully predict the ratings of movies to be released
  • According to them, Twitter is a platform that can provide geographical as well as timely information, making it a perfect source for spatiotemporal models.
  • The data they collected was gathered from Box Office Mojo and Wikipedia. Their data was comprised of movies released in 2016
  • Initially having a dataset of 3183 movies, they removed movies whose budget could not be found or missed key features in the end a dataset of 755 movies were obtained. After Key feature extraction was completed.
  • some useful data mining on the IMDb data, and uncovered information that cannot be seen by browsing the regular web frontend to the database.
  • According to their conclusion, brand power, actors or directors isn’t strong enough to affect the box office.
  • Their neural network was able to obtain an accuracy of 36.9% and compromising mistakes made within one category an accuracy of a whopping 75.2%
  • They divided the movies into three classes rise, stay, and fall finding that support vector machine SMO can give up to 60% correct predictions
  • The data was taken from the Internet Movie Database or IMDb as the data source, the data they obtained was from the years 1945 to 2017.
  • A more accurate classifier is also well within the realm of possibility, and could even lead to an intelligent system capable of making suggestions for a movie in preproduction, such as a change to a particular director or actor, which would be likely to increase the rating of the resulting film.
  • In this study, we proposed a movie investor assurance system (MIAS) to aid movie investment decisions at the early stage of movie production. MIAS learns from freely available historical data derived from various sources and tries to predict movie success based on profitability.
  • The data they gathered from movie databases was cleaned, integrated, and transformed before the data mining techniques were applied.
  • They used feature, extraction techniques, and polarity scores to create a list of successful or unsuccessful movies. This was done by gathering the data using IMDb and YouTube.

PROBLEM STATEMENT

in this Movie Success Rate Prediction project, The method of using the ratings of the films by the cast and crew has been an innovative and original way to solve the dilemma of film producers. Film producers have often trouble casting successful actors and directors and still trying to keep a budget. Looking at the average ratings of each actor and director together with all the films they participated in should be able to give the producer a good idea of who to cast and who not to cast in a film that is to be out right now.

Implementation:

  • Data Preprocessing & Correlation Analysis
  • Application of Decision Tree Algorithm
  • Application of Random Forest Algorithm

RESULTS & CONCLUSION

After testing both the algorithms on the IMDb dataset i.e. Decision Tree and Random Forest algorithm, we found that the Random Forest algorithm got a better accuracy (99.6%) on the data rather than the decision tree algorithm in which we obtained just 60% accuracy.

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Predict the Forest Fires Python Project using Machine Learning Techniques

Predict the Forest Fires Python Project using Machine Learning Techniques is a Summer Internship Report Submitted in partial fulfillment of the requirement for an undergraduate degree of  Bachelor of Technology In Computer Science Engineering. I submit this industrial training workshop entitled “PREDICT THE FOREST FIRES” to the University, Hyderabad in partial fulfillment of the requirements for the award of the degree of “Bachelor of Technology” in “Computer Science Engineering”. 

Apart from my effort, the success of this internship largely depends on the encouragement and guidance of many others. I take this opportunity to express my gratitude to the people who have helped me in the successful competition of this internship.

I would like to thank the respected faculties who helped me to make this internship a successful accomplishment.

I would also like to thank my friends who helped me to make my work more organized and well-stacked till the end.

OBJECTIVE OF THE PROJECT:

This is a regression problem with clear outliers which cannot be predicted using any reasonable method. A comparison of the three methods has been done :

(a) Random Forest Regressor,
(b) Neural Network,
(c) Linear Regression

The output ‘area’ was first transformed with an ln(x+1) function.

One regression metric was measured: RMSE and r2 score is obtained. An analysis of the regression error curve(REC) shows that the RFR model predicts more examples within a lower admitted error. In effect, the RFR model predicts better small fires, and the r2 score is obtained by using Linear Regression.

Best Algorithm for the project:

The best model is the Random Forest Regressor which has an RMSE value of 0.628 for which we are using GridSearchCV.

Scikit-learn has the functionality of trying a bunch of combinations and seeing what works best, built-in with GridSearchCV. The CV stands for cross-validation.

MODEL BUILDING

PREPROCESSING OF THE DATA:

Preprocessing of the data actually involves the following steps:

GETTING THE DATASET:

we can get the data from the client. we can get the data from the database.
https://archive.ics.uci.edu/ml/datasets/forest+fires

IMPORTING THE LIBRARIES:

We have to import the libraries as per the requirement of the algorithm.

IMPORTING THE DATA SET:

Pandas in python provide an interesting method read_csv(). The read_csv function reads the entire dataset from a comma-separated values file and we can assign it to a DataFrame to which all the operations can be performed. It helps us to access each and every row as well as columns and each and every value can be accessed using the data frame. Any missing value or NaN value has to be cleaned.

HANDLING MISSING VALUES:

OBSERVATION:

As we can see there are no missing values in the given dataset of forest fires

DATA VISUALIZATION:

  • scatterplots and distributions of numerical features to see how they may affect the output ‘area’
  • Boxplot of how categorical column day affects the outcome
  • Boxplot of how categorical column month affects the outcome

CATEGORICAL DATA:

  • Machine Learning models are based on equations, we need to replace the text with numbers. So that we can include the numbers in the equations.
  • Categorical Variables are of two types: Nominal and Ordinal
  • Nominal: The categories do not have any numeric ordering between them. They don’t have any ordered relationship between each of them. Examples: Male or Female, any color
  • Ordinal: The categories have a numerical ordering between them. Example: Graduate is less than Post Graduate, Post Graduate is less than Ph.D. customer satisfaction survey, high low medium
  • Categorical data can be handled by using dummy variables, which are also called indicator variables.
  • Handling categorical data using dummies: In the panda’s library, we have a method called get_dummies() which creates dummy variables for those categorical data in the form of 0’s and 1’s.
  • Once these dummies got created we have to concat this dummy set to our data frame or we can add that dummy set to the data frame.
  • Categorical data-column ‘month
  • dummy set for column ‘month’
  • Categorical column-‘day’
  • dummy set for column ‘day’
  • Concatenating dummy sets to a data frame
  • Getting dummies using label encoder from scikit learn package
  • We have a method called label encoder in scikit learn package. we need to import the label encoder method from scikitlearn package and after that, we have to fit and transform the data frame to make the categorical data into dummies.
  • If we use this method to get dummies then in place of categorical data we get the numerical values (0,1,2….)
  • importing label encoder and one hot encoder
  • Handling categorical data of column month
  • Handling categorical data of column day

TRAINING THE MODEL:

  • Splitting the data: after the preprocessing is done then the data is split into train and test set
  • In Machine Learning in order to access the performance of the classifier. You train the classifier using a ‘training set’ and then test the performance of your classifier on an unseen ‘test set’. An important point to note is that during training the classifier only uses the training set. The test set must not be used during the training of the classifier. The test set will only be available during the testing of the classifier.
  • training set – a subset to train a model. (Model learns patterns between Input and Output)
  • test set – a subset to test the trained model. (To test whether the model has correctly learned)
  • The amount or percentage of Splitting can be taken as specified (i.e. train data = 75%, test data =25% or train data = 80%, test data= 20%)
  • First we need to identify the input and output variables and we need to separate the input set and output set
  • In scikit learn library we have a package called model_selection in which the train_test_split method is available. we need to import this method
  • This method splits the input and output data to train and test based on the percentage specified by the user and assigns them to four different variables(we need to mention the variables)

 EVALUATING THE CASE STUDY:

Building the model (using splitting):

First, we have to retrieve the input and output sets from the given dataset

  • Retrieving the input columns
  • Retrieving output column

MODEL BUILDING:

  • Defining Regression Error Characteristic (REC)

Download the complete project Code, Report on Predict the Forest Fires using Project using Machine Learning Techniques

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Audio Classification On Cat’s And Dog’s Python Project

Our Audio Classification project illustrates a straightforward audio classification model supported by deep learning. we tend to address the matter of classifying the sort of sound-supported short audio signals and their generated spectrograms, from classifying dog’s audio to cat’s audio throughout model training. So as to satisfy this challenge, we tend to use a model-supported Convolutional Neural Network (CNN). The audio was processed with Mel-frequency Cepstral Coefficients (MFCC) into what is unremarkably called Mel spectrograms, and hence, was reworked into a picture. Our final CNN model achieved 89% accuracy on the testing dataset.

Project Overview :

The input to our model, in this project, is cats and associated dogs recording audio go in WAV kind. It lies below the supervised machine learning class. Thus, a dataset is also present as well as a target class. Hence, the intention here is to classify if the given input wav file is that of a cat or dog. Each of the dog and cat sounds is incredibly distinguished like in their pitch and frequency level since completely different| sounds have different sample rates. By default, Librosa mixes all audio to mono and resamples them to 22050 cycles/second at load time. For music and audio analysis, Librosa is associated ASCII text file python package. The info and the sampling rate are provided by Librosa. Audio or sound is in its raw kind, and the data provided should be pre-processed to extract significant and meaningful features so we implemented an algorithm i.e., MFCC (Mel Frequency Cepstral Coefficients) rule. Then, when audio extraction is done, the information is fed and the dataset is split into training and test set. So, after the preprocessing, a Convolutional Neural Network model is designed using tensor flow. For every code and model building, Keras API was used to implement Google colab.

Motivation

Machine learning can be used in image processing, understanding speech, and musical instruments, speech-to-text, environmental sound classification, and many more. And as for our project, we implemented a class of speech processing i.e, audio classification. Converting sound waves into audio and spectrograms which is a visual representation of frequencies with the help of function provided by machine learning.

There are many techniques to classify images as many different in-built neural networks under CNN are already there, especially if it is related to images. And it’s straightforward to extract options from pictures as a result of pictures already being available in the shape of numbers, because the formation of a picture may be an assortment of pixels, and pixels area units within the sort of numbers. When we have data as text, we use the sequential encoder and decoder-based techniques to find features. But if it is to sound recognition or audio it is more difficult compared to text because it is based on frequency and time. Therefore a proper model is to be made to extract the frequency and pitch of that audio so as to make it easier to later recognize it.

Flow Chart:

Preliminaries and Background 

Related work

Machine learning: Image classification of cats and dogs – Before a decade, in computer notion, many problems had been saturating in accordance with their precision. However, the accuracy of those troubles significantly stepped forward with the boom of deep gaining knowledge of strategies. The majority of the problems that arise from image class is that it is defined as predicting the distinct categories a photo can belong to. Hence, for the supplied enter/ photograph detection with the aim of accomplishing high precision, a state-of-the-art approach is incorporated, i.e., a convolutional neural network turned into the build for the photo category mission of puppies and cats. A dataset become given from Kaggle comprising a total of 25000 pix of each dog and cat.

Machine learning: Audio classification of different bird species – Here, the methodology and results of using deep learning to assist in the classification of birds by their sounds are presented. As birds indicate the health of an ecosystem, hence this topic is of high importance. Random Forest Classification and custom-made six CNN models from the literature were performed on a dataset of ten birds that were composed of xeno-canto.org. The highest accuracy was achieved at around 65% by the Random Forest and at about 58% for the CNN model.

conclusion and future work 

In this report, we first briefly explained the overview of this project and showed some referred project work already established. Then, we precisely illustrated our task, including the learning task and the performance task. After that, we explained the approach we are heading toward in order to classify the datasets. The approach/model we used is a neural network which is an implementation of the deep network which is a trainable model by which we were able to classify the dog’s and cat’s audio. The highest accuracy we got was 89.6%.

  1. In the future, we will try to implement the different high-level models in order to achieve much higher
  2. We’ll build a system that can directly intake a live raw
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Fake Disaster Tweet Detection Web-App Python Machine Learning Project

This project “Fake Disaster Tweet Detection” aims to help predict, whether a tweet weather it is fake or real. It uses the Multinomial Naïve Bayes approach for detecting fake or real tweets from existing datasets available on Kaggle. The classifier will be trained only on text data. Traditionally text analysis is performed using Natural Language Processing also known as NLP. Natural language processing is a field that comes under Artificial Intelligence. Its main focus is on letting computers understand human language and process it. NLP helps recognize and predict diseases using speech, it helps in sentiment analysis, cognitive assistant, spam detection, the healthcare industry, etc. In this project Training Data is pre-processed, then sent to the classifier, then and the classifier predicts weather the tweet is real or fake.

This project is made on Jupyter Notebook which is a part of Anaconda Navigator. This project ran successfully on Jupyter Notebook. The dataset was successfully loaded into the notebook. All the extra python packages which were required for project completion were also loaded into the notebook. The model is also deployed successfully using HTML, CSS, python, and flask.

The accuracy score on test data is 77.977%. average recall value is 0.775 and the average precision score is 0.775. Precision is used to calculate a number of correct positive predictions made by the model. The recall is used to calculate the number of correct positive predictions made out of all the positive predictions that could have been made.

System Design

System Flowchart

System Flowchart

Problem: To detect disaster tweets whether it’s fake or real using a machine learning algorithm. In this, the concept of Natural language Processing is used.

Identification of data: In this project, I have used a dataset available on Kaggle competition based on Natural language processing. This project works only on text data. It has five columns:

  1. Id: It tells the unique identification of each tweet
  2. Text: It tells the tweet in text form
  3. Location: It tells the place from where the tweet was sent and it can be blank
  4. Keyword: It tells a particular word in the tweet and it can be blank
  5. Target: It tells the actual value of the tweet weather it’s a real tweet or Fake

Data-preprocessing: First the preprocessing is done in the dataset which includes the removal of punctuations, then the removal of URLs, digits, non-alphabets, and contractions, then tokenization and removing Stopwords, and removing Unicode. Then lemmatization is done on the dataset. After preprocessing Countvectorizer is used to convert text data into numerical data as the classifier only works for numerical data. The dataset is then split into 70% training data and 30% test data.

Definition of Training Data: The training dataset which contains 70% of the whole dataset is used for training the model.

Algorithm Section: In this project Multinomial Naïve Bayes classifier algorithm is used for detecting disaster tweets whether they are fake or real.

Evaluation with test set: Several text samples are passed through the model to check whether the classification algorithm gives the correct result or not.

Prediction Model

Implementation Work Details

The data-set which is used in this project “Fake disaster tweet detection” is taken from the Kaggle competition “Natural Language Processing with Disaster Tweets”. The data set contains 7613 samples. This project works only on text data. It has five columns:

  • Id: It tells the unique identification of each tweet
  • Text: It tells the tweet in text form
  • Location: It tells the place from where the tweet was sent and it can be blank
  • Keyword: It tells a particular word in the tweet and it can be blank
  • Target: It tells the actual value of the tweet weather it’s a real tweet

Step 2: Data-Preprocessing

  1. Removing Punctuations: Punctuations are removed with the help of the following python code
  1. Removing URLs, digits, non-alphabets, _: True means it has HTTP, and False means it does not have HTTP
  1. Removing Contraction: It expands the words which are written in short form like can’t is expanded into cannot, I’ll is expanded into I will, etc.
  1. Lowercase the text, tokenize them, and remove Stopwords: Tokenizing means splitting the text into a list of tokens. Stopwords are the words in the text which does not provide additional meaning to the text.
  1. Lemmatizing: It converts any word into its root form like running, ran into a run.
  1. Countvectorizer:

Text cannot be used to train our model, it has to be converted into numbers that our computer can understand, so far in this project, Countvectorizer is used. Countvectorizer counts the number of times each word appears in a document. Countvectorizer works as:

Step1: It first identifies unique words in the complete dataset.

Step 2: then it will create an array of zeros for each sample of the same length as above Step 3: It then takes each word at a time and find its occurrence in each sample in the dataset. The number of times the word appears in the sample will replace the zero positioned at the word in the list. This will repeat for every word. 

Step 3: Model Used:

In this project, the Multinomial Naïve Bayes approach is used for detecting fake or real tweets from existing datasets available on Kaggle. Naïve Bayes classifier is based on the probability theorem “Bayes Theorem” and also has an assumption of conditional independence among every pair.

System Testing

This project is made on Jupyter Notebook which is a part of Anaconda Navigator. This project ran successfully on Jupyter Notebook. The dataset was successfully loaded into the notebook. All the extra python packages which were required for project completion were also loaded into the notebook. The model is also deployed successfully using HTML, CSS, python, and flask.

The machine learning model is evaluated we normally use classification accuracy which is the number of correct predictions divided by the total number of predictions.

This accuracy measuring technique works well when there is an equal number of samples in the dataset belonging to each class. The accuracy score on test data is 77.977%. average recall value is 0.775 and the average precision score is 0.775. Precision is used to calculate a number of correct positive predictions made by the model. The recall is used to calculate the number of correct positive predictions made out of all the positive predictions that could have been made.

  • Precision = True Positives / (True Positives + False Positives)
  • Recall = True Positives / (True Positives + False Negatives)

Conclusion

In this project only one classification algorithm is used which is Multinomial Naïve Bayes. First, the preprocessing is done in the dataset which includes the removal of punctuations, then removal of URLs, digits, non-alphabets, and contractions, then tokenization and removing Stopwords, and removing Unicode. Then lemmatization is done on the dataset. After preprocessing Countvectorizer is used to convert text data into numerical data as the classifier only works for numerical data. The dataset is then split into 70% training data and 30% test data. The accuracy score on test data is 77.977%. average recall value is 0.775 and the average f1 score is 0.775.

Future Scope

In the future, some other classification algorithms can also be tried on this dataset like KNN, Support vector machine (SVM), Logistic Regression, and even Deep learning algorithms can also be used which give very high accuracy. Vectorizing can be done using other methods like word2vec, Tf-Idf vectorizer, etc.

Download the Complete Project on ake Disaster Tweet Detection Web Application Python-based Machine Learning Project.

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Body Fitness Prediction using Random Forest Classifier Project

Purpose of the Project

To avoid several health issues, we should monitor our body fitness by using various fitness prediction gadgets like smartwatches, oximeters, B-P machines, etc. we can monitor our B-P, calories burnt, bone weight, etc. the devices work with smart device technology to exchange data via Bluetooth communication protocol. Here, in this project, we import the data which consists of (date, step count, mood, calories burned, hours of sleep, bool of activity, and weight in kg) and split the dataset into the testing set and training set. We are using a random forest classifier in this project.

Existing problem

Body fitness prediction play’s a key role in leading a healthy life. Fitness is a state of health and well-being, more specifically the ability to perform daily activities body fitness is generally achieved through proper nutrition and physical exercise, and rest. By this, we are losing our body fitness and it leads to various chronic issues

Proposed solution

Importing Dataset

Exploratory Data Analysis ]: df. shape

Here, in this project, we import the data which consists of (date, step count, mood, calories burned, hours of sleep, bool of activity, and weight in kg) and split the dataset into the testing set and training set. We are using a random forest classifier in this project.

EXPERIMENTAL INVESTIGATIONS

Dataset:

We will use the body fitness prediction dataset which was retrieved from Kaggle.com.

  • Check if there are associations between physical activity (by counting steps), caloric expenditure, body weight hours of sleep, and the feeling of feeling active and/or inactive.
  • Compare caloric expenditure between the categories of mood and self-perceived activity (active and inactive)
  • Compare the hours of sleep between the categories of mood and self-perceived activity (active and inactive)
  • Compare body weight between categories of self-perceived activity (active and inactive)
  • Database The database has 96 observations, and 7 columns. Its quantitative variables are “number of steps” (step_count), “caloric expenditure” (calories_burned), “hours of sleep” (hours_of_sleep and “body weight” (weight_kg). And qualitative variables “dates” (date), “mood” “(mood), self-perceived activity” active or inactive “(bool_of_active). The variable” humor “was assigned the value” 300 “to mean” Happy “, the value” 200 “for” Neutral “and” 100 “for” sad “and for the variable” self-perceived activity
  • Contingency tables of categorical variables will be exposed.
  • A correlation matrix between variables will be presented
  • Bar charts and violins to demonstrate the distribution of quantitative variables by categories
  • Scatter plot for analysis of the possible linear relationship between two variables

RESULT

Output result

RANDOM FOREST CLASSIFIER

Random Forest Classifier

CORRELATION PLOT

Correlation Plot

FINAL RESULT:

Body fitness prediction Output

APPLICATIONS

There are so many different kinds of applications used to predict the fitness of Human beings today.

TRAINING AND TESTING:

Splitting the data:

We use sklearn. ensemble module train_test_split which is used for the training and testing part.

Dependent and Independent variables:

Independent variables contain a list of variables on which the bool of activity is dependent.

The dependent variable is the variable that is dependent on the other variable’s values.

Independent variables are mood,step_count, calories burned, hours of sleep,weightkg.

The dependent variables are bool_of_active.

MODEL BUILDING:

We use Random Forest Classifier for predicting Body Fitness Prediction. Because it gives an accurate prediction.

CONCLUSION

We have analyzed the Body fitness prediction Data and used Machine Learning to Predict the fitness of a human being. We have used a Random Forest classifier and its variations, to make predictions and compared their performance. xgboost regressor has the lowest RMSE and is a good choice for this problem.