Improve your model with these steps! Interesting ways of dealing with missing data, model optimisation and parameter tuning in order to get better results! Solve your first Kaggle Competition! From a csv file all the way to making predictions and deploying your results. Full end-to-end Tutorial on Machine Learning in Python. We start by explaining the Machine Learning Process. Then, we move on to the Data pre-processing phase where we clean and transform our data. We show some methods on how to identify the most important variables.
After that, we run the model and make predictions. Every time we run a model, we submit it into Kaggle to get our score on the evaluation dataset. Then, we go over a few methods on how to improve our results & predictions. We provide the raw data and the code! Hope you enjoy!
Data Analytics Course Link:
ipidata.teachable.com/
Raw Data and Code:
github.com/Pitsillides91/Pyth...
Part 1:
• Supervised Machine Lea...
Other Supporting Videos:
Video 1 - Download and Install Python - Numpy Tutorial:
• How to learn Python? -...
Video 2 - Pandas Tutorial:
• Complete PYTHON Tutori...
Video 3 - JOINs and UNIONs Tutorial:
• How to Merge DataFrame...
Video 4 - Data Visualizations with MatPlotLib:
• How to create Data Vis...
Video 5 - Data Visualizations with Seaborn:
• Complete Seaborn Tutor...
Video 6 - Machine Learning Example - Regression:
• Machine Learning Tutor...
Table of content:
- How to run machine learning in Python
- Kaggle competition example
- How to improve my machine learning model
- How to improve my accuracy
- How to deal with missing values
- How to deal with missing data
- How to optimise my machine learning model
- How to do hyper parameter tuning in machine learning
- Machine Learning XGBoost
- Machine Learning RFE
- Machine Learning Search CV Library
Yiannis Pitsillides on Social Media:
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18 июл 2022
