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https://github.com/anuraganalog/kaggles-30-days-of-ml

Kaggle's 30 Days of ML
https://github.com/anuraganalog/kaggles-30-days-of-ml

30 challenges days kaggle ml notebooks of

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Kaggle's 30 Days of ML

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# Kaggle's 30 Days of Machine Learning



This is a four-week challenge that started on 2nd Aug, 2021



## Week1



### Day1



* [Follow the instructions in this notebook](https://www.kaggle.com/alexisbcook/getting-started-with-kaggle?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-1) to get started with Kaggle

* [Join 30 Days of ML Discord Community](https://discord.com/invite/f8g8bDq8Vv) and introduce yourself in the #introductions channel



### Day2



* [Read this tutorial](https://www.kaggle.com/colinmorris/hello-python?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-2) (from Lesson 1 of the Python course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-syntax-variables-and-numbers/edit) (from Lesson 1 of the Python course)



### Day3



* [Read this tutorial](https://www.kaggle.com/colinmorris/functions-and-getting-help?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-3) (from Lesson 2 of the Python course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-functions-and-getting-help/edit) (from Lesson 2 of the Python course)



### Day4



* [Read this tutorial](https://www.kaggle.com/colinmorris/booleans-and-conditionals?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-4) (from Lesson 3 of the Python course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-booleans-and-conditionals/edit) (from Lesson 3 of the Python course)



### Day5



* [Read this tutorial](https://www.kaggle.com/colinmorris/lists?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-5) (from Lesson 4 of the Python course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-lists/edit) (from Lesson 4 of the Python course)

* [Read this tutorial](https://www.kaggle.com/colinmorris/loops-and-list-comprehensions?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-5) (from Lesson 5 of the Python course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-loops-and-list-comprehensions/edit) (from Lesson 5 of the Python course)



### Day6



* [Read this tutorial](https://www.kaggle.com/colinmorris/strings-and-dictionaries?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-6) (from Lesson 6 of the Python course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-strings-and-dictionaries/edit) (from Lesson 6 of the Python course)



### Day7



* [Read this tutorial](https://www.kaggle.com/colinmorris/working-with-external-libraries?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-7) (from Lesson 7 of the Python course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-working-with-external-libraries/edit) (from Lesson 7 of the Python course)



## Week2



### Day8



* [Read this tutorial](https://www.kaggle.com/dansbecker/how-models-work?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-8) (from Lesson 1 of the Intro to ML course)

* [Read this tutorial](https://www.kaggle.com/dansbecker/basic-data-exploration?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-8) (from Lesson 2 of the Intro to ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-explore-your-data/edit) (from Lesson 2 of the Intro to ML course)



### Day9



* [Read this tutorial](https://www.kaggle.com/dansbecker/your-first-machine-learning-model?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-9) (from Lesson 3 of the Intro to ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-your-first-machine-learning-model/edit) (from Lesson 3 of the Intro to ML course)

* [Read this tutorial](https://www.kaggle.com/dansbecker/model-validation?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-9) (from Lesson 4 of the Intro to ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-model-validation/edit) (from Lesson 4 of the Intro to ML course)



### Day10



* [Read this tutorial](https://www.kaggle.com/dansbecker/underfitting-and-overfitting?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-10) (from Lesson 5 of the Intro to ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-underfitting-and-overfitting/edit) (from Lesson 5 of the Intro to ML course)

* [Read this tutorial](https://www.kaggle.com/dansbecker/random-forests?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-10) (from Lesson 6 of the Intro to ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-random-forests/edit) (from Lesson 6 of the Intro to ML course)



### Day11



* [Read this tutorial](https://www.kaggle.com/alexisbcook/machine-learning-competitions?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-11 ) (from Lesson 7 of the Intro to ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-machine-learning-competitions/edit) (from Lesson 7 of the Intro to ML course)



### Day12



* [Read this tutorial](https://www.kaggle.com/alexisbcook/introduction?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-12) (from Lesson 1 of the Intermediate ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-introduction/edit) (from Lesson 1 of the Intermediate ML course)

* [Read this tutorial](https://www.kaggle.com/alexisbcook/missing-values?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-12) (from Lesson 2 of the Intermediate ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-missing-values/edit) (from Lesson 2 of the Intermediate ML course)

* [Read this tutorial](https://www.kaggle.com/alexisbcook/categorical-variables?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-12) (from Lesson 3 of the Intermediate ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-categorical-variables/edit) (from Lesson 3 of the Intermediate ML course)



### Day13



* [Read this tutorial](https://www.kaggle.com/alexisbcook/pipelines?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-13) (from Lesson 4 of the Intermediate ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-pipelines/edit) (from Lesson 4 of the Intermediate ML course)

* [Read this tutorial](https://www.kaggle.com/alexisbcook/cross-validation?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-13) (from Lesson 5 of the Intermediate ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-cross-validation/edit) (from Lesson 5 of the Intermediate ML course)



### Day14



* [Read this tutorial](https://www.kaggle.com/alexisbcook/xgboost?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-14) (from Lesson 6 of the Intermediate ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-xgboost/edit) (from Lesson 6 of the Intermediate ML course)

* [Read this tutorial](https://www.kaggle.com/alexisbcook/data-leakage?utm_medium=email&utm_source=gamma&utm_campaign=thirty-days-of-ml&utm_content=day-14) (from Lesson 7 of the Intermediate ML course)

* [Complete this exercise](https://www.kaggle.com/anurag1817/exercise-data-leakage/edit) (from Lesson 7 of the Intermediate ML course)



## Week3 & Week4



Week3 and week4 is a invite only challenge

> As soon as the challenge finishs, It will upload the link of the challenge



**Welcome to the [30 Days of ML](https://www.kaggle.com/c/30-days-of-ml/) competition!**



For the final two weeks of the program, you will work on this competition, open only to people who have [signed](https://www.kaggle.com/thirty-days-of-ml) up for the [30 Days of ML program](https://www.kaggle.com/c/30-days-of-ml/). The top 10 teams will receive swag as prizes!



**Data Description**



For this competition, you will be predicting a continuous `target` based on a number of feature columns given in the data. All of the feature columns, `cat0` - `cat9` are categorical, and the feature columns `cont0` - `cont13` are continuous.



**Files**



* train.csv - the training data with the target column

* test.csv - the test set; you will be predicting the target for each row in this file

* sample_submission.csv - a sample submission file in the correct format