Enter Kaggle's Tabular Competition 2022
Enter Kaggle's Tabular Competition 2022
This course is designed to take the learner through all eleven Kaggle monthly tabular comprtitions in 2022.
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What you'll learn
- Learn how to navigate the Kaggle website.
- Learn how to enter a competition on the Kaggle website.
- Learn how to use machine learning to make predictions on a classification problem.
- Learn how to make predictions on a regression problem.
- Learn how to use machine learning to undertake a clustering problem.
This course is designed to take the learner through all eleven Kaggle monthly tabular comprtitions in 2022.
The course is composed of three sections, being:-
1. Introduction
2. Kaggle competitions
3. Summary
In the introductory section of the course, I cover an introduction to Kaggle, a machine learning website, and an introduction to how to enter a competition using a motorcycle prediction competition question.
In the Kaggle competitions, I cover the eleven monthly tabulat competitions for the year 2022. These competitions include classification problems, regression problems, clustering problems, and multi-target problems. It is important to note that each competition question is more difficult than the last. It would be wise, therefore, for the learner to take the two prerequisite courses before endeavouring to take this course. The two prerequisite courses, created by myself, are How to Enter a Kaggle competition and Enter Kaggle's Tabular Competition 2022.
In the Summary part of the course, I cover the code in the motorcycle prediction competition question to give the learner insight into how to improve his score.
It is important to know machine learning techniques as a precurser to this course. The basic principles of the logic to follow to undertake a machine learning project are:-
1. Import libraries
2. Load datasets into program
3. Read the datasets and convert them to dataframes
4. Clean the data by imputing and null values in the data
5. Analyse the target
6. Analyse the independent variables and how they relate to the dependent variables
7. Remove any outliers if necessary
8. Encode the data if necessary
9. Employ feature selection techniques if necessary
10. Define the dependent and independent variables
11. Normal and standardise the independent variables if necessary
12. Split the dataset into training and validation sets
13. Define the model, training and fitting the data into it
12. Make the predictions on the validation set and test set
13. Prepare the submission to be submitted to Kaggle
14. Submit the predictions to Kaggle for scoring
