Conteúdo do Curso

Data Preprocessing

1. Brief Introduction

Data Types Data Processing Methods Dataset: Test and Training Deleting an "Extra" Data Changing the Data Type

2. Processing Quantitative Data

Data Scaling Data Scaling vs Data Normalization Removing Outliers Removing Missing Values Data Augmentation: Synthetic Data

3. Processing Categorical Data

Methods for Encoding the Categorical Data One-Hot Encoding Ordinal Encoding Label Encoding of the Target Variable Challenge

4. Time Series Data Processing

Data Type Conversion Data Cleaning Stationarity Denoising Train/Test Split & Cross Validation Challenge

5. Feature Engineering

Technique Idea Realization Feature Extraction from Text Feature Extraction from Images Feature Extraction from Time Series Challenge

6. Moving on to Tasks

Challenge 1 Challenge 2 Challenge 3

Data Preprocessing

Challenge 1

Tarefa

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

Read the dataset 'adult-census.csv'
Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
Remove rows with missing values
Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
For numeric data ('age', 'hours-per-week'), you will need to scale the data
Print processed data

Tudo estava claro?

Seção 6. Capítulo 1

Conteúdo do Curso

Data Preprocessing

1. Brief Introduction

Data Types Data Processing Methods Dataset: Test and Training Deleting an "Extra" Data Changing the Data Type

2. Processing Quantitative Data

Data Scaling Data Scaling vs Data Normalization Removing Outliers Removing Missing Values Data Augmentation: Synthetic Data

3. Processing Categorical Data

Methods for Encoding the Categorical Data One-Hot Encoding Ordinal Encoding Label Encoding of the Target Variable Challenge

4. Time Series Data Processing

Data Type Conversion Data Cleaning Stationarity Denoising Train/Test Split & Cross Validation Challenge

5. Feature Engineering

Technique Idea Realization Feature Extraction from Text Feature Extraction from Images Feature Extraction from Time Series Challenge

6. Moving on to Tasks

Challenge 1 Challenge 2 Challenge 3

Data Preprocessing

Challenge 1

Tarefa

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

Read the dataset 'adult-census.csv'
Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
Remove rows with missing values
Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
For numeric data ('age', 'hours-per-week'), you will need to scale the data
Print processed data

Tudo estava claro?

Seção 6. Capítulo 1