Kaggle Mastery
Being in the Data Science field its important that you keep practising your Problem Solving skills. Kaggle provides a platform to atleast practise the Feature Engineering & Moddelling aspect. Also, Kaggle provides a glimpse into Real World problems. To participate in Kaggle requires dedicated effort and focus to move up the progression and rank in any of the categories. Few months back I challenged myself to paticipate in Kaggle and move up ranks. In this Repository I plan to record all that I discover along the way.
Start by Becoming a Contributor on Kaggle Video Link
Important Links:
1/ Kaggle Datasets Uploaded
2/ Kernels for Learning
Pandas Basics (Use Working Code if you want write codes yourself)
Competition Solutions for Learning
Video Tutorials
Titanic Solution |- Titanic Basic Solution with Logistic Regression
- Titanic Solution : Random Forest
- Titanic Solution with basic H2O
- Titanic Solution with Auto H2O
Getting Started Machine Learning Codes [Basic]
- P1 : sklearn Logistic Regression | Video
- P2 : Logistic Regression - hyperparameter tuning | Video
- P1 : sklearn SVM Model | Video
Getting Started Machine Learning Codes [Intermediate]
Getting Started Machine Learning Codes [Intermediate]
- P1 : Develop your first XGBoost model on Python | Video
- P2: XGBoost - Train, Test and Predict
- P3: XGBoost Hyperparameter Tuning Python - v1
Follow me on Kaggle for Kaggle Adventure!
- My Kaggle Profile Link