I am a Data Science Researcher having more than 10 year experience in IT industry. I have worked on several classical, rule base and AI projects by using Machine Learning, Natural Language Processing, Transformers and Deep Learning, Spark, Kafka, AWS Cloud, Python, SQL and Distributed Programming Experience in NLP, Transformer, LIME, SHAP, ML, BERT, Keras, Spark, FAISS, Elastic Search, Scikit, numpy, pandas, Python, Statistic modeling and DL. My motto is sharing my experience, best-practices and learning with everyone. Please subscribe and support the
@@dev3446 I get this error when I enter sentiment in the flask application website: ValueError: could not convert string to float: " Must watch movie.. Breathtaking, Benchmark in Kannada industry, Story, Screenplay, Casting full marks. Actors Screen presence is mind blowing. Cinematography is remarkable. Hats off to director's imagination. International standard movie" 127.0.0.1 - - [20/Oct/2024 13:30:25] "POST /predict HTTP/1.1" 500 -
@@dev3446 I get this error when I enter sentiment in the flask application website: ValueError: could not convert string to float: " Must watch movie.. Breathtaking, Benchmark in Kannada industry, Story, Screenplay, Casting full marks. Actors Screen presence is mind blowing. Cinematography is remarkable. Hats off to director's imagination. International standard movie" 127.0.0.1 - - [20/Oct/2024 13:30:25] "POST /predict HTTP/1.1" 500 -
Hi Sir, in phase 3 the scripts was working fine until reaching the model_7, however after executing "Evaluation of FineTuned Logsitic Regression Classifier" , I got kernel died and restarted automatically. It's really confusing as it didn't returning any error messages. When I repeat it again from the beginning and I skip model_7, same issue happened for model_8 and model_9. Kindly your comments and how to fix this.
This playlist goal is to give you idea how you can develop a ML solution end to end. From Data preprocessing to cloud deployment. Further you can try different models, also generative ai modals as well
Please follow the instructions as per videos. Because for Productionize a model we need end to end pipeline. So that, all preprocessing, features engineering steps would be included in pipeline
It took around 46 mins for ada boost classifier, but for other models my laptop took same time as yours. So why for ada boost model it's taking so much time?
Ada boost required more computation. That why it take time. First option, You can reduce hyper parameter range to cut training time. Second option high config machine
Sir i am facing an issue in vectorization code snippet There is a lookup error in line : X_train_count = countvect.fit_transform(train['Reviews_clean']).toarray()
Thanks 🙏🏻, In RAG system we can upload multiple type of doc through documents loader like pdf, text, markdown etc. for more info please refer to this video Create a Chatbot with RAG and Opensource LLM with Lang Chain and Flask for Beginners ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-LiZLJAUz_7A.html
Sir one last thing can you tell is part 3 is enough for our mini project as we have to submit project like smester project till part 3 is enough or not mean they meet our all requirements for project sentimenal analysis or not kindly explain
Hi Sir Sir I want to know how we adjust the other language mean other than english how they effect our results and what the purpose of other language please give satisfying answer and long ans so i can better uderstan please ans....
If you have any requirements to work on other languages then you can apply same techniques like feature engineering and models. You just need to understand those languages so that you can do better analysis
Hi Sir Sir I want to know how we adjust the other language mean other than english how they effect our results and what the purpose of other language please give satisfying answer and long ans so i can better uderstan please ans....
1. If we have project requirements to train ML model then we need training data for particular language. Only training/test data change rest technique would be same as we do data analysis, feature engineering for English language. Please try to understand fundamentals of ML/DL.
00:02 Deployment of the base model onto the local server 01:34 Introduction to sentiment analysis framework and its modules 02:57 Using pipeline for data preprocessing 04:28 Using SQL pipeline and TFID vectorizer for data cleaning and model deployment 05:53 Deploying machine learning model using Flask for sentiment analysis project 07:26 User input for sentiment analysis prediction 08:57 Deploying sentiment analysis model with Flask 10:47 API feeds data for sentiment analysis and creates a pipeline for base models Crafted by Merlin AI.
one quick question" @16.54 min why are step 3 and 4 having the same indentation level? In a normal function, step4 is at the same indentation as step 8 : func() Kindly help me understand
Thank you for the video, a very detailed one. One quick question, why is the return keyword not indented same as print("Below is the nested function")? or in otherwords why is return is the same indentation as def function_returned? Kindly help me understand
@kdpr007 in step 3 we are defining inner functions and step 4 we are returning values. So, Both are inside the parent function with same level indentation
00:02 Model evaluation with Shap and LIME 02:27 Model evaluation and corrective actions for bias and errors 04:44 Model evaluation using multiple matrices resulted in high F1 score and accuracy. 07:02 Understanding confusion matrix and feature contributions in prediction analysis 09:19 Analyzing feature contributions for sentiment analysis using Shap values 11:44 Evaluating feature contributions and applying thresholding techniques for sentiment analysis. 00:00 Analyzing feature contributions using Shap and LIME 18:16 Explaining feature impact on predictions using regression models 20:43 Improving model accuracy in real time 23:08 Explaining model evaluation using Shap and LIME for sentiment analysis project Crafted by Merlin AI.
00:03 Model selection for sentiment analysis. 02:15 Logistic regression showed high precision and low latency 04:23 Decision tree takes significantly longer training time compared to logistic regression. 06:31 Addressing overfitting and accuracy challenges through hyperparameter tuning 08:49 Random forest to AdaBoost comparison and hyperparameter tuning 11:03 Hyperparameter tuning in logistic regression for sentiment analysis 13:13 Hyperparameter tuning for Random Forest Classifier 15:32 Achieving best F1 score through hyperparameter tuning Crafted by Merlin AI.00:03 Model selection for sentiment analysis. 02:15 Logistic regression showed high precision and low latency 04:23 Decision tree takes significantly longer training time compared to logistic regression. 06:31 Addressing overfitting and accuracy challenges through hyperparameter tuning 08:49 Random forest to AdaBoost comparison and hyperparameter tuning 11:03 Hyperparameter tuning in logistic regression for sentiment analysis 13:13 Hyperparameter tuning for Random Forest Classifier 15:32 Achieving best F1 score through hyperparameter tuning Crafted by Merlin AI.
@@dev3446 sir is there any other way i can do lemmatization because i dont think i will be able to download wordnet. The code cell keeps running for a very long time and then it gives an error. i will not be able to send the error to u bcoz it takes very long to try downloading again. although if u want to see it i will try once again. but is there any other way ? I dont know why youtube is not letting me comment so if i can connect to u on gmail or anything else it would be great!
sirr i followed from vid 1 and started vid 2 but the doubt is in which app should i type this code 🥲. I am a complete beginner sir, i don't know like whether should i just type the code directly in vs code and run it
Initially you can start coding for Data Cleaning, Data Analysis, FeatureEngineering and model evaluation in Jupiter notebook. Then you can switch to visual studio code for Model Deployment coding
Please start from video 1 in this playlist. This playlist contains end to end project from data processing to cloud deployment. Just follow the video instructions
Sirr i followed from vid 1 and started vid 2 but the doubt is in which app should i type this code 🥲. I am a complete beginner sir, i don't know like whether should i just type the code directly in vs code and run it 😑@@dev3446
NLP is used in data analysis and feature engineering videos . As we have textual data, so we used NLP library nltk for data cleaning and vectorizer to covert textual data into into vector form
I have shared details steps to deploy a model to Ubuntu instance on AWS in this video ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-921aFe381ro.htmlsi=kj520aeunwNsBgZj
HI sir , can you help me with the final part of my project , I want to draw a trend analysis using the sentiment analysis tool , can you please help me , I will be very thankful to you.
@@dev3446 After reaching the ui side , when i hit the get sentimnet button I m getting this (NameError: name 'classifier' is not defined) also I am getting the exception /home/tavneet.manchanda/Desktop/Sentiment_Analysis_Case_Study/sentimentanalysis/models/model Error loading pickle file: invalid load key, 'v'.
Can you make a video on credit fraud detection project & flask app building & deploying it to pythonanywhere website ... Please it would help me as i am beginner in ML
Sure I will try to create videos related to Fraud Detection. Further, Incase of Credit Fraud detection only Feature Engineering and Modelling part would be changed, Rest Flask App building and development steps would be same.
Yes, Ankit it give you predictions for any kind of text. Because in the pipeline text convert into embedding. Embedding is used by model for prediction. But as model trained on movie reviews data. So, it would provide better predictions if text contains similar features(words)
Yes Hitesh if you know python and basic of ML. Then you can easily do this project. Further I have provided all code and GitHub link in description. Incase of any issues you can directly contact Me
Thanks Ana for your support. 1. Github for repository for Sentiment Analysis Project: github.com/OptimalXAI/Sentiment_Analysis_Case_Study.git 2. In case of Python Playlist: you can find google Colab link in every video description Further, we will add readme in Github Repository to install all prerequisite dependencies easily by tomorrow.
