Day 13: Advanced EDA Techniques

Welcome to Day 13! Today, we’re building on the foundational EDA techniques we explored yesterday by diving into advanced EDA methods. These approaches help us uncover deeper insights, especially in larger or more complex datasets.

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Advanced EDA Techniques

1. Correlation Analysis

Correlation analysis is used to understand the linear or monotonic relationships between numerical features.

Key Types of Correlation:

• Pearson Correlation:

  • Measures the linear relationship between features.

  • Ranges from -1 (perfect negative) to +1 (perfect positive).

  • Suitable for numerical features with linear relationships.

• Spearman Correlation:

  • Measures the strength of monotonic relationships (increasing or decreasing trends).

  • Suitable for non-linear but monotonic data.

How to Apply:

1. Compute the Correlation Matrix:

    corr = df.corr()  # Pearson correlation by default
   

2. Visualize Using a Heatmap:

    import seaborn as sns
    import matplotlib.pyplot as plt
   
    plt.figure(figsize=(10, 8))
    sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')
    plt.title('Correlation Matrix')
    plt.show()
   

Benefits:

• Identifies highly correlated features, which might be redundant.

• Provides insights into feature relationships for feature selection or engineering.

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2. Pair Plots

Pair plots provide pairwise scatter plots and histograms for all numerical features, making it easier to identify relationships, clusters, and outliers.

How to Apply:

1. Create pair plots using Seaborn:

    sns.pairplot(df, hue='job_category')
   

   • hue: Color the plots by a categorical variable to show differences across groups.

2. Analyze trends or clusters:

   • Linear trends indicate possible correlations.

   • Clusters may suggest groupings or segmentation in data.

Benefits:

• Useful for exploring interactions between numerical features.

• Highlights group-specific patterns using the hue parameter.

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3. Feature Distributions Across Categories

Analyzing the distribution of numerical features across categorical groups helps in understanding how data varies by category.

Common Techniques:

1. Box Plots:

   • Show distribution, spread, and potential outliers.

    sns.boxplot(x='job_category', y='salary', data=df)

2. Violin Plots:

   • Combine box plots and KDEs (Kernel Density Estimations) for a richer visualization.

    sns.violinplot(x='job_category', y='salary', data=df)

Benefits:

• Identifies differences in distribution across categories.

• Highlights categorical groups with higher variability or distinct patterns.

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4. Time Series Analysis

Time series analysis explores trends, seasonality, and patterns in timestamped data. This is especially useful for datasets involving dates (e.g., job posting times or application rates).

Key Steps:

1. Convert to Datetime:

    df['posting_date'] = pd.to_datetime(df['posting_date'])
   

2. Aggregate by Date:

    df.groupby(df['posting_date'].dt.date)['job_id'].count().plot(kind='line')
   

   • Analyze the trend in job postings over time.

3. Seasonality and Cyclic Patterns:

   • Use rolling averages to smooth data for better visualization:

    df['rolling_avg'] = df['metric'].rolling(window=7).mean()

Benefits:

• Highlights upward/downward trends in data.

• Detects periodicity or seasonality (e.g., peaks in job postings).

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5. Dimensionality Reduction (PCA)

Principal Component Analysis (PCA) reduces high-dimensional datasets into fewer dimensions (principal components) while retaining most of the variance.

Steps:

1. Standardize Data: PCA is sensitive to scale; standardize numerical features to ensure all have equal importance:

    from sklearn.preprocessing import StandardScaler
   
    scaler = StandardScaler()
    scaled_data = scaler.fit_transform(df.select_dtypes(include='number'))
   

2. Apply PCA:

   • Reduce dimensions to 2 or more components:

    from sklearn.decomposition import PCA

    pca = PCA(n_components=2)
    pca_result = pca.fit_transform(scaled_data)
    df['PCA1'], df['PCA2'] = pca_result[:, 0], pca_result[:, 1]

3. Visualize PCA:

    sns.scatterplot(x='PCA1', y='PCA2', hue='job_category', data=df)
   

Benefits:

• Reduces complexity in high-dimensional datasets.

• Enables visualization of clusters or group separations in reduced dimensions.

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Summary of Benefits

By incorporating these techniques, you can gain a deeper understanding of complex datasets, prepare features for modeling, and uncover meaningful patterns for decision-making.

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Reflection on Day 13

Today's advanced techniques highlighted the power of visualization and statistical methods in data exploration. These tools allow us to distill vast amounts of information into actionable insights.

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What’s Next?

Tomorrow, we’ll consolidate everything from this week and focus on reviewing the techniques we’ve learned so far, tying them back to practical applications in the jobs project and beyond.

Thank you for joining Day 13 — let’s keep pushing forward! 🚀