hello guys, I am new in machine learning and I have recently started to learn ML so there are many confusions. I want to take \"Parkinson's Freezing of Gait Prediction\" as my university project. So please guide me, on how I can prove this project is new by showing some novelty and unique points. I also want to know which ML model I should use to get good results and also want to know from where I should learn the basics of ML with coding Please help me. I shall be thankful to you for this act of kindness.
\nThanks
hello guys, I am new in machine learning and I have recently started to learn ML so there are many confusions. I want to take \"Parkinson's Freezing of Gait Prediction\" as my university project. So please guide me, on how I can prove this project is new by showing some novelty and unique points. I also want to know which ML model I should use to get good results and also want to know from where I should learn the basics of ML with coding Please help me. I shall be thankful to you for this act of kindness.
\nThanks
Hello, here in the \"Getting Started\" section of FAQ you can find a lot of information about ML and sample notebooks and data about ML, specifically used by Kagge.
\nLink: https://www.kaggle.com/docs/competitions#resources-for-getting-started
The best models for this particular competition are the Regression models. If it's for a university project you should talk to your professor about the competition and data, and even if you can't compile a full notebook yourself, you can look through public notebooks made specifically for the competition. You can find data visualisation and sharing threads here in the Discussion yourself and see what others have done to make this work. Analyzing someone's code might be even more valuable than writing one yourself. You could try out some basic things and see if your prediction will score anything, and even failure will be a good show to your professor about what you've learned. Just remember to credit if you've based anything upon the publicly shared data and notebooks.
\nCheers
", "rawMarkdown": "Hello, here in the \"Getting Started\" section of FAQ you can find a lot of information about ML and sample notebooks and data about ML, specifically used by Kagge.\nLink: [https://www.kaggle.com/docs/competitions#resources-for-getting-started](url)\n\nThe best models for this particular competition are the Regression models. If it's for a university project you should talk to your professor about the competition and data, and even if you can't compile a full notebook yourself, you can look through public notebooks made specifically for the competition. You can find data visualisation and sharing threads here in the Discussion yourself and see what others have done to make this work. Analyzing someone's code might be even more valuable than writing one yourself. You could try out some basic things and see if your prediction will score anything, and even failure will be a good show to your professor about what you've learned. Just remember to credit if you've based anything upon the publicly shared data and notebooks.\n\nCheers", "votes": null } ], "comments": [ { "id": 2282485, "author_name": "solkaczmarek", "author_url": "", "post_date": "05/31/2023 15:53:29", "content": "Hello, here in the \"Getting Started\" section of FAQ you can find a lot of information about ML and sample notebooks and data about ML, specifically used by Kagge.
\nLink: https://www.kaggle.com/docs/competitions#resources-for-getting-started
The best models for this particular competition are the Regression models. If it's for a university project you should talk to your professor about the competition and data, and even if you can't compile a full notebook yourself, you can look through public notebooks made specifically for the competition. You can find data visualisation and sharing threads here in the Discussion yourself and see what others have done to make this work. Analyzing someone's code might be even more valuable than writing one yourself. You could try out some basic things and see if your prediction will score anything, and even failure will be a good show to your professor about what you've learned. Just remember to credit if you've based anything upon the publicly shared data and notebooks.
\nCheers
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2254697": "hello guys, I am new in machine learning and I have recently started to learn ML so there are many confusions. I want to take \"Parkinson's Freezing of Gait Prediction\" as my university project. So please guide me, on how I can prove this project is new by showing some novelty and unique points. I also want to know which ML model I should use to get good results and also want to know from where I should learn the basics of ML with coding Please help me. I shall be thankful to you for this act of kindness.\nThanks", "2282485": "Hello, here in the \"Getting Started\" section of FAQ you can find a lot of information about ML and sample notebooks and data about ML, specifically used by Kagge.\nLink: [https://www.kaggle.com/docs/competitions#resources-for-getting-started](url)\n\nThe best models for this particular competition are the Regression models. If it's for a university project you should talk to your professor about the competition and data, and even if you can't compile a full notebook yourself, you can look through public notebooks made specifically for the competition. You can find data visualisation and sharing threads here in the Discussion yourself and see what others have done to make this work. Analyzing someone's code might be even more valuable than writing one yourself. You could try out some basic things and see if your prediction will score anything, and even failure will be a good show to your professor about what you've learned. Just remember to credit if you've based anything upon the publicly shared data and notebooks.\n\nCheers" }, "source": "meta" }