Seeking Help to Improve Model Accuracy in Kaggle Competition

Dear fellow Julius users,

I hope this message finds you well. I’m reaching out to the community today with a humble request for assistance. Recently, I participated in a Kaggle competition using an Julius, and while I managed to achieve a decent 79% accuracy level, I fell short of my goal.

Another participant in the competition attained a remarkable 100% accuracy with their model, securing a spot in the top 3 rankings.

While I’m proud of my efforts, I can’t help but feel a pang of competitiveness and a desire to improve. Therefore, I’m seeking guidance and collaboration from other Julius users who might be willing to lend their expertise and insights. My ultimate aim is to enhance my model’s performance and achieve that coveted 100% accuracy level, proving that Julius is just as competent as traditional python.

If you have any experience or suggestions regarding model optimization, feature engineering, algorithm selection, or any other aspect that could contribute to boosting accuracy, I would be incredibly grateful for your input. Let’s work together to push the boundaries of what Julius can achieve and showcase its potential in the competitive world of data science.

Thank you for considering my request, and I look forward to any assistance or advice you can offer.

This is the conversation Julius AI | Your AI Data Analyst

This is the link of competition : Natural Language Processing with Disaster Tweets | Kaggle

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Please treat this message with highest priority and help me out