¿Cómo hacer el ANÁLISIS EXPLORATORIO DE DATOS?: guía paso a paso

Exploratory Data Analysis in Machine Learning

Introduction to Exploratory Data Analysis (EDA)

• The previous video discussed the stages involved in developing a machine learning project, emphasizing that data preparation takes 60-70% of development time.

• This video focuses on providing a step-by-step guide for conducting exploratory data analysis (EDA), which is crucial yet lacks clear online resources.

Purpose and Importance of EDA

• EDA aims to understand the dataset before selecting specific techniques or models in data science, helping identify patterns and relationships among variables.

• It involves organizing data, understanding its content, handling missing values and outliers, and drawing conclusions from the analysis.

Steps Involved in EDA

• The EDA process can be summarized into seven steps:

1. Define the question to be answered.

2. Get a general idea of the dataset.

3. Identify types of variables present.

4. Choose appropriate descriptive statistics and visualizations.

5. Analyze interactions between variables.

6. Draw conclusions from the analysis.

Case Study: Titanic Dataset

• The classic Titanic dataset will be used as an example, containing passenger information such as names, ages, gender, and survival status.

• The first question posed is about identifying which types of people had higher survival probabilities during the Titanic disaster.

Analyzing Variable Types

• Step three involves defining variable types: numerical (discrete or continuous) and categorical (nominal, binary, ordinal).

• Numerical variables include discrete values like age or continuous values like ticket fare; categorical variables include nominal labels like gender or binary outcomes like survival status.

Descriptive Statistics

• Step four focuses on statistical description based on variable type using measures of central tendency (mean and median).

• While mean provides an average value susceptible to outliers, median offers a more robust measure by indicating the middle value within sorted data distributions.

Understanding Data Distribution and Variability

Dividing Data for Analysis

• The process of dividing data into two halves is essential for calculating measures like the median, particularly for ordinal or discrete data such as ticket categories or age.

• Organizing data in ascending order allows us to find a value that separates the lower half from the upper half, but knowing just the mean or median isn't sufficient.

Measures of Variability

• To understand how clustered or dispersed our data is, we utilize measures of variability, primarily standard deviation and interquartile range (IQR).

• Standard deviation indicates how much individual data points deviate from the mean; however, it is sensitive to outliers.

• The IQR, calculated as the difference between the 75th percentile and 25th percentile values, provides a more robust measure against outliers.

Visualizing Data Distributions

• Percentiles divide distributions into quartiles: Q1 (0-25%), Q2 (25-50%), Q3 (50-75%), and Q4 (75-100%).

• Histograms can be used to visualize continuous and discrete data by grouping them into bins; they help identify normal distribution patterns.

Limitations of Histograms

• While histograms are useful for visualizing distributions, they may obscure outliers. Box plots serve as an alternative visualization method that highlights these outliers effectively.

Box Plots Explained

• A box plot displays percentiles with whiskers extending to 1.5 times the IQR beyond Q1 and Q3. This helps in identifying outliers visually.

• By overlaying original data on box plots for variables like age and fare, one can interpret ranges and identify patterns in ticket pricing.

Analyzing Categorical Data

Visual Representation of Categorical Variables

• For categorical data analysis, bar charts can illustrate occurrences within each category or their percentage representation in total datasets.

Insights from Survival Rates

• Analyzing survival rates through bar graphs reveals that non-survivors were more numerous than survivors in a given dataset—indicating potential biases in predictive modeling due to imbalanced datasets.

Exploring Relationships Between Variables

Bivariate Analysis Techniques

• Moving beyond univariate analysis involves examining interactions between two variables using scatter plots to assess linear relationships.

Correlation Coefficients

• Calculating correlation coefficients helps determine relationships: values close to 1 indicate positive correlation while those near -1 suggest negative correlation; values around 0 imply no linear relationship.

Comparing Numerical with Categorical Variables

• When comparing numerical variables like fare with categorical ones such as survival status, bar graphs or violin plots can reveal insights about their interrelationship. Violin plots provide additional density information alongside traditional box plot features.

Exploratory Data Analysis Techniques

Comparing Categorical Variables

• A continuous graph of the histogram allows for comparison between two categorical variables, such as passenger title and survival status, using stacked bar charts.

• The analysis reveals that most female passengers categorized as "Miss" survived the sinking.

Multivariate Analysis

• In multivariate analysis, all possible pairs of variables are compared to identify relationships.

• The correlation index is calculated for different variable pairs and displayed in a correlation matrix.

• Values on the main diagonal of this matrix equal 1, indicating self-comparison; however, off-diagonal values reveal more interesting relationships.

Insights from Correlation

• The dataset shows no relationship between passenger class and survival probability.

• Detailed pairwise analysis can uncover more significant relationships among variables.

Summarizing Observations

• The final phase of exploratory data analysis involves summarizing key observations into concise statements.

• This summary helps identify correlated or relevant features crucial for subsequent project stages like data preprocessing and model development.

Importance of Missing Data Handling

• While discussing exploratory data analysis phases, it’s noted that handling missing data is a critical step not yet covered in detail.

• Future videos will address these important aspects of exploratory data analysis.