Machine Learning Challenge from Chaordic

1. Gathering information: research similar problems and published articles

2. Knowing the data: play with the datasets and see how it is structured

3. Cleaning the data: split the dataset and remove irrelevant information

4. Analysing the data: find correlations between possible features and the target data

5. Training the model: split the data for train and test and try different models of classifiers

6. Evaluating the model: compare the results with an appropriate metric for a binary classification

7. Submitting the answer: generate csv file with the target data scored with the best trained model

8. Iterating over the process: go back to step 4, filter the relevant features, remove outliers, balance the trainig data, combine some variables, do some magic, ...

1. Gathering information: google

2. Knowing the data: gedit and bash commands

3. Cleaning the data: bash scripts and OpenRefine

4. Analysing the data: jupyter notebooks and python libraries (pandas, numpy, matplotlib, ...)

5. Training the model: sklearn modules

6. Evaluating the model: sklearn functions and methods inside classes

7. Submitting the answer: python code

8. Iterating over the process: try different tools like azure machine learning studio

• data/ : directory to store the datasets

  • data/split_data.sh : bash script to split dataset

    • e.g.:$./split_data.sh data

  • data/stats.sh : bash script to count masculine/feminine gender

    • e.g.:$./stats.sh data

  • data/test* : file with samples of the datasets' content

  • data/users : all target users ids

• research/ : directory with articles related to the challenge

• playground.ipynb : notebook used to learn how to use python packages (pandas, sklearn, ...)

• Igor_final.ipynb : notebook with the workflow of the solution.

• final_answer.csv : file containing challenge's answer