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   Jupyter Notebook 6

2. Using-Word-Embeddings-for-Twitter-Sentiment-Analysis Using-Word-Embeddings-for-Twitter-Sentiment-Analysis Public

   The project researches sentiment analysis on Twitter, with the goal of evaluating the positivity, negativity or neutrality of comments. Using Word Embeddings, an advanced method in natural language…

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3. Using-Support-Vector-Machine-To-Automatically-Take-Class-Attendance Using-Support-Vector-Machine-To-Automatically-Take-Class-Attendance Public

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4. Using-deep-learning-to-classify-fruits-using-the-VGG16-model Using-deep-learning-to-classify-fruits-using-the-VGG16-model Public

   Classify 8 types of fruit using the VGG16 model with accuracy up to 90%

   Jupyter Notebook 5

5. Traffic-sign-classification-using-transfer-learning-with-ResNet152V2 Traffic-sign-classification-using-transfer-learning-with-ResNet152V2 Public

   Utilizing ResNet152V2 for Traffic Sign Classification: Achieve High Accuracy in Identifying 52 Sign Types with 99% Precision

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6. Detect-Yoga-Poses-And-Correction-In-Real-Time-Using-Machine-Learning-Algorithms Detect-Yoga-Poses-And-Correction-In-Real-Time-Using-Machine-Learning-Algorithms Public

   Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusti…

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