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2: Prepare Data for Training

Objective

In this lab, we will:

  • Load the Processed Dataset
  • Define Features and Target Variable
  • Prepare the Data for Model Training

Guide

Step 1 - Find and Open the Jupyter Notebook

In directory "workshop_materials/bike_demand_forecasting", please open the second notebook, "02_model_training.ipynb", and follow the instructions below to complete this task.

Step 2 - Load the Processed Data

In this task, we take the cleaned dataset from the last task (data/processed) and prepare it for model training.

Add the cleaned data for the first two month in the appropriate cell: 

data_2011_01.csv
data_2011_02.csv

step 3 - Set the Categorical Features

We already have set the numerical features.

You only need to set these categorical features: 

['season', 'holiday', 'workingday', 'weathersit']

step 4 - Split the DataSet into Training and Test DataSets

In order to split the dataset into training and testing datasets, data scientists usually use train_test_split function from sklearn.model_selection module.

Just add this line of code in the appropriate cell, without any changes: 

train_test_split(X_input, y_input, test_size=0.3, random_state=42)

step 5 - Inspect the Split Data

Check the shape of your training and test datasets to confirm the split was successful:

Just paste this line in a cell under the same notebook and run it. 

print(X_train.shape, X_test.shape)

(Optional) You can also print out the first 5 rows of both training and test data:

print(X_train.head(), X_test.head())

✅ We use these Train and Test datasets to train a machine learning model in the next exercise Model Training & Experiment Tracking.