Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Challenge | Time Series Data Processing
Data Preprocessing

ChallengeChallenge

Tarea

Now, you know how to work with time-series data, and you are faced with the task of processing it:

  1. Convert column data type from str 'Month' to datetime.
  2. Fill NaN values using the interpolation method.
  3. Use the moving average method to remove noise from the data.

¿Todo estuvo claro?

Sección 4. Capítulo 6
toggle bottom row
course content

Contenido del Curso

Data Preprocessing

1. Brief Introduction

Data TypesData Processing MethodsDataset: Test and TrainingDeleting an "Extra" DataChanging the Data Type

2. Processing Quantitative Data

Data ScalingData Scaling vs Data NormalizationRemoving OutliersRemoving Missing ValuesData Augmentation: Synthetic Data

3. Processing Categorical Data

Methods for Encoding the Categorical DataOne-Hot EncodingOrdinal EncodingLabel Encoding of the Target VariableChallenge

4. Time Series Data Processing

Data Type ConversionData CleaningStationarityDenoisingTrain/Test Split & Cross ValidationChallenge

5. Feature Engineering

Technique IdeaRealizationFeature Extraction from TextFeature Extraction from ImagesFeature Extraction from Time SeriesChallenge

6. Moving on to Tasks

Challenge 1Challenge 2Challenge 3

Data Preprocessing

ChallengeChallenge

Tarea

Now, you know how to work with time-series data, and you are faced with the task of processing it:

  1. Convert column data type from str 'Month' to datetime.
  2. Fill NaN values using the interpolation method.
  3. Use the moving average method to remove noise from the data.

¿Todo estuvo claro?

Sección 4. Capítulo 6
toggle bottom row
some-alt