How to enter a Kaggle community competition

How to enter a Kaggle community competition, available at Free, has an average rating of 2.25, with 8 lectures, based on 2 reviews, and has 965 subscribers.

You will learn about Learners will learn what Kaggle, the data science website is. Learners will learn about machine learning. Learners will be able to enter a Kaggle community competition. Learners will be able to make predictions on a supervised learning dataset. Learners will be able to submit their predictions to Kaggle for scoring. This course is ideal for individuals who are This course is suitable for people interested in data science. or This course is suitable for people interested in machine learning. or This course is suitable for beginner Python developers who want to improve upon their skill set. It is particularly useful for This course is suitable for people interested in data science. or This course is suitable for people interested in machine learning. or This course is suitable for beginner Python developers who want to improve upon their skill set.

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Summary

Title: How to enter a Kaggle community competition

Price: Free

Average Rating: 2.25

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Learners will learn what Kaggle, the data science website is.
Learners will learn about machine learning.
Learners will be able to enter a Kaggle community competition.
Learners will be able to make predictions on a supervised learning dataset.
Learners will be able to submit their predictions to Kaggle for scoring.

Who Should Attend

This course is suitable for people interested in data science.
This course is suitable for people interested in machine learning.
This course is suitable for beginner Python developers who want to improve upon their skill set.

Target Audiences

This course is suitable for people interested in data science.
This course is suitable for people interested in machine learning.
This course is suitable for beginner Python developers who want to improve upon their skill set.

In this course the learner will be educated in machine learning by going into the Kaggle website’s community competitions and joining a tabular community competition. The competition that has been selected for this course is the TAMS AIS Winter 2022 competition, which is a regression problem.

the student will learn how to enter a competition and follow the machine learning process from beginning to end, to include the following steps:-
1. Define the problem statement.

2. Import libraries used in the program.

3. Load csv files used in the program.

4. Use pandas to read the csv files and concert them to dataframes.

5. Check the train and test dataframes for null values.

6. Define the target variable and use seaborn to analyse it.

7. Drop the label from the train dataframe.

8. Define the dataframe, combi, which is the test dataframe appended to the train dataframe.

9. Check the combi dataframe for the number of unique values.

10. Drop any unnecessary features from combi.

11. Create a heatmap of combi.

12. Normalise combi.

13. Define the dependent and independent variables.

14. Split the X and y variables into training and validation sets.

15. Select the model: in this instance it will be linear regression.

16. Make predictions on the validation and test set.

17. Measure model performance by calculating the error.

18. Compare actual values against predicted values and plot on a graph.

19. Prepare submission and submit to Kaggle for scoring.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Intro to Kaggle

Chapter 2: TAMS AIS Winter 2022 competition

Lecture 1: Enter competition

Lecture 2: Analyse target

Lecture 3: Combine datasets

Lecture 4: Split datasets

Lecture 5: Submit predictions to Kaggle

Chapter 3: Congratulations on completing the course

Lecture 1: Congratulations on completing course

Instructors

Tracy Renee
Data Scientist

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 0 votes
5 stars: 0 votes

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