β”œβ”€β”€ πŸ“š Haris └── New Text Document.txt β”œβ”€β”€ πŸ“š Shujaat β”œβ”€β”€ New Text Document.txt └── Paper Improments.docx β”œβ”€β”€ πŸ“š Resources └── New Text Document.txt β”œβ”€β”€ ec9d5e39-fac3-4395-8e63-116953675469.png └── ReadMe.md /πŸ“š Haris/New Text Document.txt: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /πŸ“š Shujaat/New Text Document.txt: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /πŸ“š Resources/New Text Document.txt: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /πŸ“š Shujaat/Paper Improments.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dr-mushtaq/Smart-Road-Risk-Alert-System/main/πŸ“š Shujaat/Paper Improments.docx -------------------------------------------------------------------------------- /ec9d5e39-fac3-4395-8e63-116953675469.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dr-mushtaq/Smart-Road-Risk-Alert-System/main/ec9d5e39-fac3-4395-8e63-116953675469.png -------------------------------------------------------------------------------- /ReadMe.md: -------------------------------------------------------------------------------- 1 | ## **Smart Road Risk Alert System: Real-Time Risk Prediction and Notification Using AI and IoT πŸŽ“πŸ€–** 2 |

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5 | 6 | Welcome to theSmart Road Risk Alert System System repository! πŸš€ 7 | 8 | 9 | 10 | ## **Overview 🎯** 11 | 12 | The Real-Time Road Risk Alert System is a smart, AI-powered framework designed to enhance road safety by predicting accident risks based on real-time data from traffic sensors, weather APIs, GPS/mobile apps, and historical accident records. Using machine learning models like XGBoost, the system generates risk scores (low, medium, high) and delivers instant alerts via mobile apps, smart signage, or adaptive traffic lights. A cloud-based backend processes live data streams, serves model predictions, and logs events, while a web dashboard enables city authorities to monitor high-risk zones. This system aims to proactively prevent accidents and support smarter urban mobility. 13 | 14 | [Chatgp road Map](https://chatgpt.com/c/67f3a9b3-c77c-800e-bd30-9a01261407b1) 15 | 16 | ## **Objectives** 17 | 18 | 1- Implement **Natural Language Processing (NLP)** to enable smooth and intelligent conversations. 19 | 20 | 2- Provide **personalized learning assistance** based on user queries and academic profiles. 21 | 22 | 3- Support **multi-platform integration**, including web, mobile, and social media. 23 | 24 | 4- Offer **real-time analytics** for monitoring chatbot interactions and improving performance. 25 | 26 | 5- Enable **modular expansion** to integrate new features and enhance adaptability. 27 | 28 | ## **System Architecture 🎯** 29 | 30 | The system consists of three main components: 31 | 32 | 1- **Backend: AI-Powered NLP Model** 33 | 34 | - Utilizes Transformers, BERT, or GPT-based NLP models. 35 | 36 | - Machine learning models trained on educational data. 37 | 38 | - API-driven architecture for seamless integration. 39 | 40 | 2- **Front-End Chatbot Interface** 41 | 42 | - Web-based chatbot UI using Streamlit, Flask, or Django. 43 | 44 | - Interactive and user-friendly experience for students. 45 | 46 | - Handles question-answering, course guidance, and academic support. 47 | 48 | 3- **Data & Analytics Module** 49 | 50 | - Tracks student queries, chatbot performance, and response accuracy. 51 | 52 | - Provides insights for continuous model improvement. 53 | 54 | - Supports learning analytics for educators. 55 | 56 | ## **Key Features πŸ“Œ** 57 | 58 | 1- **AI-Driven Learning Assistant** 59 | 60 | - Engages students in natural conversations. 61 | 62 | - Answers course-related queries using context-aware NLP models. 63 | 64 | - Provides recommendations for learning resources and study materials. 65 | 66 | 2- **Multi-Platform Support** 67 | 68 | - Web-based chatbot, accessible via a university portal. 69 | 70 | - Mobile-friendly interface for real-time interactions. 71 | 72 | - Potential social media and messaging app integration. 73 | 74 | 3- **Customizable & Scalable** 75 | 76 | - Easily extendable to include more subjects and academic disciplines. 77 | 78 | - Can integrate with university LMS (Learning Management Systems). 79 | 80 | - Adaptive learning module for personalized student support. 81 | 82 | 4-**Data-Driven Insights** 83 | 84 | - Tracks frequently asked questions to improve response quality. 85 | 86 | - Provides real-time usage analytics and performance monitoring. 87 | 88 | - Helps educators identify knowledge gaps and improve teaching strategies. 89 | 90 | ## **Technology Stack πŸ“‚** 91 | 92 | 1- **Backend (AI/NLP)**: Transformers (BERT, GPT,RAG,RAAS), Scikit-learn 93 | 94 | 2- **Chatbot Development**: Rasa NLU, Dialogflow, OpenAI GPT API 95 | 96 | 3- **Front-End UI**: Streamlit, Flask, Django 97 | 98 | 4- **Database**: PostgreSQL, Firebase, MySQL 99 | 100 | 5- **Hosting**: AWS, Heroku, Google Cloud 101 | 102 | 6- **Integration**: WhatsApp, Telegram, Discord API (for chatbot communication) 103 | 104 | ## **Development Plan πŸ”** 105 | 106 | **Phase 1: Prototype Development (2–4 weeks)** 107 | 108 | - Train NLP model using educational datasets. 109 | 110 | - Develop a basic chatbot interface. 111 | 112 | - Deploy API for question-answering module. 113 | 114 | - Conduct initial testing with students. 115 | 116 | **Phase 2: Expansion (1–2 months)** 117 | 118 | - Improve AI responses with enhanced data collection. 119 | 120 | - Integrate with university LMS and mobile apps. 121 | 122 | - Conduct beta testing with a larger student audience. 123 | 124 | - Implement real-time performance tracking. 125 | 126 | **Phase 3: Full Deployment (3+ months)** 127 | 128 | - Roll out full-featured chatbot for academic assistance. 129 | 130 | - Optimize model performance with feedback-based training. 131 | 132 | - Expand to support multiple languages. 133 | 134 | - Introduce voice-based interactions using Speech-to-Text. 135 | 136 | ## **Sustainability & Future Roadmap 🀝** 137 | 138 | - Develop subscription-based models for advanced features. 139 | 140 | - Partner with universities and e-learning platforms. 141 | 142 | - Implement AI-driven personalized tutoring. 143 | 144 | - Expand integration to VR-based learning environments. 145 | 146 | ## **Next Steps πŸš€** 147 | 148 | 1- Deploy AI model and chatbot interface. 149 | 150 | 2- Test with a small group of students and educators. 151 | 152 | 3- Refine chatbot responses and NLP accuracy. 153 | 154 | 4- Expand to wider student networks and enhance chatbot capabilities. 155 | 156 | ## **πŸ“¬Contact** 157 | 158 | For inquiries and contributions, contact Dr. Mushtaq at: 159 | 160 | - Email: mushtaqmsit@gmail.com, Skype ID:themushtaq48 161 | 162 | - LinkedIn: [LinkedIn](https://www.linkedin.com/in/mushtaq-hussain-21417814/) 163 | 164 | - YouTube: [CoursesTeach](https://www.youtube.com/@coursesteach-mv5si/videos) 165 | 166 | - Website: [CoursesTeach](https://coursesteach.com/) 167 | 168 | We welcome contributions from developers, researchers, and educators! Let's revolutionize e-learning with AI-driven assistance. πŸš€ 169 | 170 | Star this repo if you find it useful ⭐ 171 | 172 |
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πŸ“šChapter: 1 -Literature Review

 174 | 175 | ## πŸ“šSection: 1 - **Samman Arooj** 176 | 177 | |Title| Published Date| Research Questions| Model performance metrics|Research Gap|Taking Notes|Input/Target Features|Journal name/ Category|Limitations|Future Directions| 178 | |---|---|---|---|---|---|---|---|---|---| 179 | |**🌐1- The development of a chatbot using Convolutional Neural Networks**| 2022 |1.How do different CNN architectures impact chatbot performance?|1.Accuracy 2.Training Speed|Did author compared CNN chatbot to simpler models or rule-based systems? How well does it handle complex or open ended questions?| | 180 | |**🌐2- Machine learning algorithms for teaching AI chat bots** | 2021 |1.Β Which machine learning algorithms are most successful in training AI chatbots for various tasks?|N/A|The paper does not cover various methods for evaluating the effectiveness of chatbot training algorithms. How can we measure a chatbot's ability to hold natural conversations, understand user intent, and generate appropriate responses?|Microservice architecture is used and the speed of message processing and preparation of responses by the chatbot will not change depending on the load on the server and the number of incoming messages. | 181 | |**🌐3- Personified Robotic Chatbot Based On Compositional Dialogues** | 2022 |Research likely doesn't focus on specific questions but rather explores how compositional dialogues (where conversations are built from smaller elements) can be used to create a personified robotic chatbot.|N/A|1. How effectively can the level of personality be measured in these chatbots? 2.Is user perception the only metric, or can objective measures be developed?| | 182 | | **🌐4- Boosting the Accuracy of Optimization Chatbot by Random Forest with Halving Grid Search Hyperparameter Tuning**| 2023 |1.Can hyperparameter tuning with a Halving Grid Search method improve the accuracy of an optimization chatbot built using a Random Forest algorithm?|1.Accuracy 2.Precision 3.Recall |The paper proposes three chatbot models: 1.One without hyperparameter tuning 2.One with hyperparameter tuning using Halving Grid Search 3.One with hyperparameter tuning and the best performing settings| | 183 | |**🌐5- Developing a Chatbot using Machine Learning**| 2019|1.Can machine learning algorithms improve the ability of a chatbot to understand natural language queries? 2. How does the choice of machine learning model (e.g., recurrent neural networks, decision trees) impact the performance of a chatbot?|1. BLEU Score (BiLingual Evaluation Understudy) 2.Turing Test|This paper does not Investigate the impact of different visual design elements on user attention and engagement with the chatbot.| | 184 | |**🌐6- Designing a Chatbot for Contemporary Education: A Systematic Literature Review**| 2023|What are the steps for designing an educational chatbot for contemporary education?|N/A|It focuses on the development of chatbots for education, not the impact on learners or educators.| | 185 | |**🌐7- Research on the Design of Intelligent Chatbot Based on Deep Learning** | 2021 |It is likely centered around improving the response generation of chatbots built with deep learning techniques.|N/A|Research gaps could exist in areas like sentiment analysis and generation of emotionally responsive dialogue.|Paper proposes an improved two-way GRU + Attention model based on the idea of mutual information, and examines the quality of the model from the final response effect. | 186 | |**🌐8- Question Answering Model Based Conversational Chatbot using BERT Model and Google Dialogflow** | 2021 |N/A|N/A|The focus might be on question answering. Future research could explore integrating functionalities like sentiment analysis to tailor responses to user emotions or incorporating functionalities for completing tasks beyond just answering questions.|The focus of the paper seems to be on building and demonstrating the feasibility of a question-answering chatbot using BERT and Dialogflow. It describes the architecture and functionalities of the chatbot| 187 | |**🌐9- Chatbot : A Question Answering System for Student** | 2021|It suggests the research question that revolves around developing a chatbot system that effectively functions as a question answering system for students.|N/A|N/A|Paper discusses the design and development of such a chatbot, including the challenges of creating a system that can understand and answer student queries effectively.| 188 | |**🌐10-QAM: Question Answering System Based on Knowledge Graph in the Military** | 2020|How can a knowledge graph-based Question Answering System (QAM) be effectively designed to be used in the military domain?|N/A|Slice of words not included in the JIEBA will be divide, which cause that the following steps can’t accuracy judged. And some unclear words often led to the system failed to judge the right answer and return a wrong answer to the user.|Research used the tool of NEO4J to build the military KG as well python to construct QA system| 189 |
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192 |

πŸ“šChapter:2 -Model metrics benchmarks

193 | 194 | 195 | ## πŸ“š Paper Title: 3 **Improving Road Safety in Smart Cities using Machine Learning Techniques** 196 | 197 | This study benchmarks classical and deep learning models for paper title Improving Road Safety in Smart Cities using Machine Learning Techniques (tuned) achieving the best performance. 198 | 199 | | Model | Accuracy (Default) | Accuracy (Tuned) | F1-Score (Default) | F1-Macro (Tuned) | F1-Micro (Tuned) | Parameters for Tuned Model | 200 | |-------------|-------------------:|-----------------:|-------------------:|-----------------:|-----------------:|----------------------------| 201 | | Random Forest | 0.7449 | 0.9963 | 0.72 | 0.9942 | 0.9963 | max_depth: none, n_estimators: 50 | 202 | | SVM | 0.6815 | 0.3568 | 0.64 | 0.1572 | 0.3568 | C: 1, kernel: rbf | 203 | | XGBoost | 0.7497 | 0.7724 | 0.73 | 0.574 | 0.7724 | max_depth: 3, n_estimators: 50 | 204 | | BiLSTM | 0.6998 | 0.384 | 0.66 | 0.358 | 0.384 | Units: 32, dropout: 0.3, epochs: 8 | 205 | | ATTR-LSTM | 0.6711 | 0.53 | 0.64 | 0.46 | 0.53 | Units: 32, dropout: 0.3, epochs: 8 | 206 | | KNN-LSTM | 0.6805 | 0.38 | 0.65 | 0.35 | 0.38 | Units: 32, dropout: 0.3, epochs: 8 | 207 | | W-CNN-LSTM | 0.6834 | 0.53 | 0.66 | 0.47 | 0.53 | Units: 32, dropout: 0.3, epochs: 8 | 208 | 209 | ## πŸ“‚ Resources 210 | πŸ”Ή [**Dataset-KSI**](https://github.com/dr-mushtaq/Smart-Road-Risk-Alert-System/blob/main/%F0%9F%93%9A%20Shujaat/KSILatest.csv) – A-Z guide to academic research, tutorials, datasets, and collaborative projects. 211 | πŸ”Ή [**Dataset**](https://github.com/dr-mushtaq/Deep-Learning) – Hands-on tutorials with TensorFlow & Keras. 212 | πŸ”Ή [**Code**](https://github.com/dr-mushtaq/Smart-Road-Risk-Alert-System/blob/main/%F0%9F%93%9A%20Shujaat/8_09_25_Enhancing_Road_Safety_in_Smart_Cities_Reivew.ipynb) – Beginner-friendly Python notes & examples. 213 | 214 |
215 | 216 |
217 |

πŸ“šLatest Deep Learning Models for Smart Road Risk ALert System

218 | 219 | | Name | Purpose | Strengths | Resource | Language Support | Use Cases | 220 | |---|---|---|---|---|---| 221 | | **🌐1 - Random Forest** | Classification & regression | High accuracy after tuning (Acc: 0.9963), robust to overfitting | [Scikit-learn RF](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) | Language-agnostic (depends on features) | General ML tasks, classification, feature importance | 222 | | **🌐2 - Support Vector Machine (SVM)** | Margin-based classification | Works well on smaller datasets, strong theoretical foundation | [Scikit-learn SVM](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) | Language-agnostic | Text classification, bioinformatics, anomaly detection | 223 | | **🌐3 - XGBoost** | Gradient boosting classifier | Strong performance (Acc: 0.7724 tuned), handles missing values | [XGBoost](https://xgboost.readthedocs.io/en/stable/) | Language-agnostic | Kaggle competitions, tabular data, ranking problems | 224 | | **🌐4 - BiLSTM** | Sequence modeling | Learns past & future context, but drops in tuned setting (Acc: 0.384) | [Keras LSTM](https://keras.io/api/layers/recurrent_layers/lstm/) | Text (any language with embeddings) | NLP tasks, chatbot dialogues, sequence classification | 225 | | **🌐5 - ATTR-LSTM** | Attention-enhanced sequence model | Captures context importance with attention (Acc: 0.53 tuned) | [Keras + Attention](https://keras.io/examples/nlp/neural_machine_translation_with_attention/) | Text-based tasks | Chatbots, translation, sentiment analysis | 226 | | **🌐6 - KNN-LSTM** | Hybrid similarity + sequence model | Combines nearest neighbors & LSTM for mixed patterns | Custom | Text data | Experimental NLP, hybrid classification | 227 | | **🌐7 - W-CNN-LSTM** | Convolution + recurrent hybrid | Captures local (CNN) + sequential (LSTM) features (Acc: 0.53 tuned) | [Keras CNN+LSTM](https://keras.io/examples/vision/video_classification/) | Multilingual (via embeddings) | Context-aware NLP, video/text classification, chatbots | 228 | 229 | 230 |
231 | 232 |
233 |

πŸ“šChapter:5 - Implementation

234 | 235 | ## πŸ“šChapter:3.1-**Model Training** 236 | 237 | | Title | Code | Performance |Dataset | Researcher Name|Date|Status| 238 | |---|---|---|---|---|---|---| 239 | |**Development of Chatbot System to decrease workload in e-learning system**| [![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/Model_Training_Saad.ipynb) | ANN,LSTM=0.97,Bidirectional LSTM=0.98|[intentsnew (6)](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/intentsnew%20(6).json)|Saad|---|β˜‘| 240 | |**B14-Extraneous Comment Management (ECM) Elearning**| [![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/Model_Training.ipynb) | ANN=0.98|[intentsnew](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/intents.json)|Izhar, Mazhar etc|3-Augest-2023|β˜‘| 241 | |**Development of Chatbot System to decrease workload in e-learning system**| [![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/Model_Training_Samman_V1.ipynb) | ANN=0.98|[intents_Samman_V1](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/intents_Samman_V1.json)|Samman|3-Augest-2023|InProgress| 242 | |**Development of Chatbot System to decrease workload in e-learning system**| [![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/Model_Training_Mushtaq_V1.ipynb) | LSTM,ANN,RF,BERT|[intents_Samman_V1](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/intents_Samman_V1.json)|Dr.Mushtaq|3-Augest-2023|InProgress| 243 | |**Development of Chatbot System to decrease**| [![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/Model_Training_Mushtaq_V1.ipynb) |Dialogpt|[intents_said_V1](https://github.com/dr-mushtaq/Chatbot-in-e-learning-system/blob/main/Model_Training_Saad.ipynb)|Dr.Said|3-Augest-2023|InProgress| 244 | 245 | ## πŸ“šChapter:3.2- **Apps Details** 246 | 247 | |Title| Public_URL| Deployed Repository link| Tools Details|Notbook|Dataset|Medium|Diagram| 248 | |---|---|---|---|---|---|---|---| 249 | |[**🌐1- Thesis**](https://medium.com/@Coursesteach/machine-learning-part-1-31bdf37404ee) | [1](https://drive.google.com/file/d/1JyDUmJ9U6mUlCvwBvC6crxVpdxbup9iH/view?usp=sharing)[-2](https://www.youtube.com/watch?v=sVsF_Ne_J6c&list=PLRKtJ4IpxJpDxl0NTvNYQWKCYzHNuy2xG&index=10)[-2] 250 | 251 | 252 | ## πŸ“šChapter:3.3-**Tools and Techniques Details** 253 | | Topic Name/Tutorial | Video | Code | 254 | |---|---|---| 255 | | [**🌐-1-Preprocessing in Machine Learning**](https://medium.com/@Coursesteach/guide-to-supervised-learning-with-scikit-learn-part-4-501068cf021) | [1](https://drive.google.com/file/d/14MyKUWqykavcOp2MNIgQjGVU1TOyqwGg/view) [-2](https://drive.google.com/file/d/19Sx937C_K5JWQYvdv7h2J2aRdiHiucAS/view?usp=sharing)| | 256 | |[**🌐2- Importing the Data Set Using Scikit-Learn**](https://medium.com/@Coursesteach/guide-to-supervised-learning-with-scikit-learn-part-6-importing-the-dataset-6b7e133fca66)|---|[![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/hussain0048/Machine-Learning/blob/master/Data_Processing_in_Python_.ipynb)| 257 | |[**🌐3-Handling missing data**](https://medium.com/@Coursesteach/supervised-learning-with-scikit-learn-part-7-handling-missing-data-b1b6263ce996)|[1](https://drive.google.com/file/d/1dN_YRnwuUf8QpUWeSnLEqHm-PtIWoPuF/view)|[![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/hussain0048/Machine-Learning/blob/master/Data_Processing_in_Python_.ipynb)| 258 | |[**🌐4-Data Imbalanced problem**](https://medium.com/@Coursesteach/supervised-learning-with-scikit-learn-part-8-data-imbalanced-problem-9e307c368a4d)|[1](https://drive.google.com/file/d/1Dcu0uZfT_zFmPrMUS1DkeDNKgA83Nodt/view?usp=sharing)|[![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/hussain0048/Machine-Learning/blob/master/Data_Processing_in_Python_.ipynb)| 259 | |[**🌐5-Data Transformation**](https://medium.com/@Coursesteach/supervised-learning-with-scikit-learn-part-9-data-transformation-b83ba14b1a2d)|[1](https://drive.google.com/file/d/14MyKUWqykavcOp2MNIgQjGVU1TOyqwGg/view)[-2](https://drive.google.com/file/d/1uY6x3O2G2f_jhngzdjUrXmwVB1o5QhEO/view?usp=sharing)|[![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/hussain0048/Machine-Learning/blob/master/Data_Processing_in_Python_.ipynb)| 260 | |[**🌐4-Centering and scaling.**](https://medium.com/@Coursesteach/supervised-learning-with-scikit-learn-part-10-centering-and-scaling-08c914162f81)|[1](https://drive.google.com/file/d/1gG742Q_qVbDuRbPMzJjGT_Hx1d-Joz4j/view)[-2](https://drive.google.com/file/d/1ivw7tVzaiecaJRpzoei6azhBDQySySbJ/view?usp=sharing)|[![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/hussain0048/Machine-Learning/blob/master/Data_Processing_in_Python_.ipynb)| 261 | |[**🌐5-Removing Outliers**](https://medium.com/@Coursesteach/supervised-learning-with-scikit-learn-part-11-removing-outliers-with-scikit-learn-59d6a2051d02)|[1](https://drive.google.com/file/d/1NhUQQx0e2s-oG6oLJKpgNxn-_NBXRQVl/view?usp=sharing)[-2](https://drive.google.com/file/d/1DdFCHKbJm8LU5mqDE9J6PbaepGxGUglY/view?usp=sharing)|[![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/hussain0048/Machine-Learning/blob/master/Data_Processing_in_Python_.ipynb)| 262 | |[**🌐6-Data Splitting**](https://medium.com/@Coursesteach/supervised-learning-with-scikit-learn-part-12-data-splitting-07658730bb01)|[1](https://drive.google.com/file/d/1vpTQiPWqO-_kb18Tt3L01ZMamFEAG6eT/view)[-2](https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=8)[-3](https://drive.google.com/file/d/1nhSSLP2bejY_49r-5m2yCZuaTyv2FO38/view)[-4](https://www.youtube.com/watch?v=ivVeqv4oShk&list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3&index=61)|[![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/hussain0048/Machine-Learning/blob/master/Data_Processing_in_Python_.ipynb)| 263 | |[**🌐7-Pipelines in scikit-learn**](https://medium.com/@Coursesteach/supervised-learning-with-scikit-learn-part-14-pipelines-in-scikit-learn-dc408eb152d1)|[1](https://drive.google.com/file/d/1A00SABP9KsLGwET-sSA03G7M3byA2j8u/view)[-2](https://www.youtube.com/watch?v=MuPmbW0ln6g&list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3&index=31)|[![Colab icon](https://img.shields.io/badge/Colab-Open-blue.svg?logo=colab&logoColor=white)](https://github.com/hussain0048/Machine-Learning/blob/master/Pipelines_in_scikit_learn.ipynb)| 264 | 265 | ## πŸ“šChapter:4-**Latest Deep Learning Models for Chatbot** 266 | | Name| Puerpose | Strengths | 267 | |---|---|---| 268 | |**🌐-1-Sentence-BERT**|better for semantic similarity matching|Great for identifying similar intents or paraphrased questions.| 269 | |**🌐2- Rasa NLU**|for intent classification|Open-source, customizable pipelines, good for structured intents.| 270 | |**🌐3-Fine-tuned DistilBERT**|for faster training|Lightweight, fast inference, ideal for limited-resource environments.| 271 | |**🌐4-GPT-4 Turbo (OpenAI)**|Conversational AI|Handles complex queries, follows context, excels in dynamic conversations.| 272 | |**🌐5-Mistral 7B**|Open-source LLM|Lightweight, efficient for real-time chatbot responses.| 273 | |**🌐6-Claude 3 (Anthropic)**|Safe AI assistant|Good for educational chatbot applications| 274 | |**🌐7-RAG (Retrieval-Augmented Generation)**|Knowledge retrieval|Combines chatbot responses with real-time database queries.| 275 | |**🌐8- BERT (Base/Multilingual)**|Text classification, Q&A | Strong baseline for intent classification and works in many languages.| 276 | |**🌐9- OpenChatKit**|Open-source conversational model|Designed for building customizable chatbots with intent understanding.| 277 |
278 | 279 | 280 | 281 |
282 |

πŸ“šChapter:5-Resources

283 | 284 | - [**Chat with Dataframe - Streamlit Chatbot using Langchian Agent and Ollama | Generative AI | LLM**](https://www.youtube.com/watch?v=u3SGDvOVyO4) 285 | - [**Build a Large Language Model (From Scratch)**](https://github.com/rasbt/LLMs-from-scratch/tree/main) 286 | - [**How to Make a Chatbot In Python? A Step-by-Step Guide with Source Code**](https://medium.com/@pies052022/how-to-make-a-chatbot-in-python-a-step-by-step-guide-with-source-code-c0f0b1c73378) 287 | - [**4 Chatbot Project with python**](https://amankharwal.medium.com/4-chatbot-projects-with-python-5b32fd84af37) 288 | - [**Awesome Chatbot Projects**](https://github.com/fendouai/Awesome-Chatbot) 289 | - [**The Super Duper NLP Repo**](https://notebooks.quantumstat.com/?trk=puboslic_pt-text) 290 | - [**Innovative-Chatbot-using-1-Dimensional-Convolutional-Layers**](https://github.com/Bharath-K3/Innovative-Chatbot-using-1-Dimensional-Convolutional-Layers) 291 | - [**3XFake AI Technology: AI, ML, ANNs, Generative AI, LLMs, GPT-5, AGI**](https://www.linkedin.com/pulse/3xfake-technology-ai-anns-generative-llms-gpt-5-agi-azamat-abdoullaev-pbq6f/?fbclid=IwAR1ey2lh8VwC-uh7DTpyqLN3b6mApJF94udSbRq0IPT9KdsXmyFuZNzEqXs_aem_AahJbqC_2_s8m_QppuzUw6CJgneZCNRhHkJBv99BKHgpLOR9m3Prt698gqtUQ24zghC_D6jVcCnk8dTm3jOA3lro) 292 | - [**Spam Detection using Machine Learning Methods**](https://medium.com/@Coursesteach/spam-detection-using-machine-learning-methods-dd5dbc799b6b) 293 | - [**Top AI Agents**](https://github.com/SamurAIGPT/Best-AI-Agents) 294 | - [**Learn to Train and Deploy a Real-Time Financial Advisor**](https://github.com/iusztinpaul/hands-on-llms?tab=readme-ov-file) 295 | - [**Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection**](https://arxiv.org/abs/2310.11511?fbclid=IwAR1to5thaF8Of2zfNI6O5NeYj8yy7O7QPMzWfvvdMY2x11Hwf85gEgZ2wX8_aem_AVeTQAylHY0xJANFFpZqJBNS0yVMNX2EbYd3Yjvi1rN4o5dqcOX-VNthfqh7Ukv89EFfri29Eibfqa4vCwVY7fMi) 296 | - [**Advanced RAG 08: Self-RAG**](https://ai.gopubby.com/advanced-rag-08-self-rag-c0c5b5952e0e) 297 | - [**Build a chatbot with always updated data sources using Pathway + LlamaIndex + Streamlit(Github)**](https://github.com/pathway-labs/realtime-indexer-qa-chat?ref=blog.streamlit.io&fbclid=IwAR0HkaSIQTDCTL7Vw-V6sUMjR1L4vT9movAm3bCfX3ctqLTeALSPkmLMLRY) 298 | - [**Practical NLP Code Chatbot-BERT(github)**](https://github.com/practical-nlp/practical-nlp-code/tree/master/Ch6) 299 | - [**Anaconda and Vscode configuration for Python**](https://medium.com/@Coursesteach/anaconda-and-vscode-configuration-b353db76165d) 300 | - [**Get started with conda environments**](https://www.dataschool.io/intro-to-conda-environments/) 301 | - [**Getting Started with GitHub Pages**](https://www.youtube.com/watch?v=QyFcl_Fba-k&list=PL4cUxeGkcC9jjuXmnTyPSMo5NZ8dANHSW) 302 | - [**How to deploy ML models painlessly**](https://www.realworldml.net/blog/how-to-deploy-ml-models-painlessly?fbclid=IwAR2TknQt1Y5e3o2GyHcbW-z4piQnG0lspPWGrN39BN4PjNkb5Lc4DKWINvI) 303 | - [**Deploy Machine Learning models with FastAPI, Docker, and Heroku**](https://morioh.com/a/a592ccba33fa/deploy-machine-learning-models-with-fastapi-docker-and-heroku) 304 | - [**End to End Machine Learning project implementation (Part 1)**](https://medium.datadriveninvestor.com/end-to-end-machine-learning-project-implementation-part-1-980162aea228) 305 | - [**Sentiment Analysis classifier with NLTK's VADER and Huggingface Roberta Transformers**](https://morioh.com/a/d2e99f468750/building-a-python-sentiment-analysis-project-with-nltk-and-transformer#google_vignette) 306 | - [**A Practical Guide to Transfer Learning using PyTorch**](https://www.kdnuggets.com/2023/06/practical-guide-transfer-learning-pytorch.html?fbclid=IwAR0gJIzXV1TNAenfEwN4HbkFjpRwPCRAzOrI8-6FApEwpyXQW6C71OhRydk) 307 | - [**Running Flask App On Colab With Ngrok| [ Latest Way ]**](https://www.youtube.com/watch?v=bHtxDiIl0wg) 308 | - [**Deploy Streamlit app on Google Colaboratory as public app | Ngrok | Python**](https://www.youtube.com/watch?v=Y-lUz7npEGo) 309 | - [**Building a Simple Chatbot with Python and Transformers**](https://jasminbharadiya.medium.com/building-a-simple-chatbot-with-python-and-transformers-875aec2f05d8) 310 | - [**Let's reproduce GPT-2 (124M)**](https://www.youtube.com/watch?v=l8pRSuU81PU) 311 | - [**Web-LLM Assistant: Bridging Local AI Models With Real-Time Web Intelligence**](https://pub.towardsai.net/web-llm-assistant-bridging-local-ai-models-with-real-time-web-intelligence-9376b0ba1c38) 312 | - [**Building Your First Chatbot: A Hands-On Tutorial with Open-Source Tools**](https://machinelearningmastery.com/building-your-first-chatbot/?utm_source=drip&utm_medium=email&utm_campaign=MLM+Newsletter+November+22%2C+2024&utm_content=Building+Your+First+Chatbot+with+Open-Source+Tools+%E2%80%A2+Demystifying+Ensemble+Methods) 313 | - [**Building an End-to-End FAQ Chatbot with Continuous Training & Deployment: Pre RAG Era**](https://substack.com/home/post/p-158016922) 314 | - [**Intent Classification with Small Transformer**](https://github.com/nlptown/nlp-notebooks/blob/master/Intent%20Classification%20with%20Small%20Transformers.ipynb) 315 | - [**Advanced Q&A Features with DistilBERT**](https://machinelearningmastery.com/advanced-qa-features-with-distilbert/) 316 | - [**A Practical Guide to Building Local RAG Applications with LangChain**](https://machinelearningmastery.com/a-practical-guide-to-building-local-rag-applications-with-langchain/) 317 | - [**What is knowledge distillation?**](https://www.ibm.com/think/topics/knowledge-distillation) 318 |
319 | 320 | 321 | ## πŸ’» Workflow: 322 | 323 | - Fork the repository 324 | 325 | - Clone your forked repository using terminal or gitbash. 326 | 327 | - Make changes to the cloned repository 328 | 329 | - Add, Commit and Push 330 | 331 | - Then in Github, in your cloned repository find the option to make a pull request 332 | 333 | > print("Start contributing for Machine Learning") 334 | > 335 | ## βš™οΈ Things to Note 336 | 337 | * First Fork this repository and then sent request before sent request kindly share your result to me. 338 | * You can only work on issues that have been assigned to you. 339 | * If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue. 340 | * If you have modified/added code work, make sure the code compiles before submitting. 341 | * Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder. 342 | * Do not update the **[README.md](https://github.com/prathimacode-hub/ML-ProjectKart/blob/main/README.md).** 343 | 344 | πŸ” **Explore moreπŸ‘‹πŸ›’** 345 | 346 | Explore cutting-edge tools and Python libraries, access insightful slides and source code, and tap into a wealth of free online courses from top universities and organizations. Connect with like-minded individuals on Reddit, Facebook, and beyond, and stay updated with our YouTube channel and GitHub repository. Don’t wait β€” enroll now and unleash your Machine Learning potential!” 347 | 348 | * [**Supervised learning with scikit-learn**](https://coursesteach.com/enrol/index.php?id=21) 349 | * [**Fundamental of Machine Learning**](https://coursesteach.com/enrol/index.php?id=6) 350 | 351 | 352 | 353 | 354 | 355 | ## **✨Top Contributors** 356 | We would love your help in making this repository even better! If you know of an amazing Chatbot course and Resource that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request. 357 | 358 | Together, let's make this the best AI learning hub website! πŸš€ 359 | 360 | Thanks goes to these Wonderful People. Contributions of any kind are welcome!πŸš€ 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | --------------------------------------------------------------------------------