W7-Kaggle_competition

Description

• Find the best machine learning model and params for a given dataset.

Instructions

Find the Kaggle competition with your cohort name, i.e. diamonds-datamad1022-part, link here

train.csv

• 1. Processing/cleaning the dataset: this should be later modularized in functions.

• 2. Train a model (fit & predict) with the data in train.csv. This file does contain a y.

     • Do train, test, split on train.csv if necessary.
     • Choose the best model regarding the metrics. In this case, the lowest MSE (error).

     2.1. Export the model: we don't want to invest time/RAM resources on training the model again in the future.

test.csv

• 3. Apply the same cleaning to test.csv. This files does NOT contain a y.
• 4. We'll apply the already trained model from step 2 to the text.csv file. With this we'lñl generate a new column with the predicted values.

my_submission.csv

• 5. Generate a submission.csv file with only two columns: the ID of the diamond & the predicted price (y).

Deliverables

• Jupyter notebooks where you show the process you followed to get to your submissions.

• A slide (.ppt, ipynb, etc) with a summary of the metrics you obtained and the rationale behind it.

  • Why do those params work better than others?

Tips

• Take advantage of the daily submissions. Try at least one today!