General Guidebook

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🎯 Purpose

This guidebook extends your Exploratory Data Analysis (EDA) skills with advanced techniques to deepen your understanding of complex datasets. It focuses on practical methods to detect anomalies, understand feature interactions, and prepare data for sophisticated modeling.

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🔍 1. Deepen Data Understanding

✅ Review Basic Checks

Start with a quick recap of dataset structure and quality:

df.info()
df.describe(include='all')
df.isnull().sum()

Confirm data types, missing values, and summary stats before moving forward.

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🏷️ 2. Feature Engineering & Transformation

🔄 Create New Features

Generate features that capture important patterns: - Date/time components (year, month, day) - Interaction terms (e.g., product of two variables) - Aggregated features (group means, counts)

🔧 Transform Variables

Apply transformations to improve model performance:

df['log_feature'] = np.log1p(df['feature'])
df['binned_feature'] = pd.qcut(df['feature'], q=4)

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📊 3. Advanced Distribution & Relationship Checks

Multivariate Relationships

Explore how variables interact beyond simple correlations:

sns.pairplot(df, hue='target')
sns.heatmap(df.corr(), annot=True)

Look for nonlinear patterns and clusters.

Feature Interactions

Use group statistics and pivot tables to understand combined effects:

df.groupby(['cat1', 'cat2'])['num'].mean().unstack()

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🧪 4. Detecting Anomalies & Outliers

Practical Outlier Detection

• Use IQR or Z-score for initial checks.
• Apply Isolation Forest or Local Outlier Factor for complex patterns:

  from sklearn.ensemble import IsolationForest
  iso = IsolationForest(contamination=0.05)
  df['anomaly'] = iso.fit_predict(df.select_dtypes(include=[np.number]))
  

  Flag and review outliers carefully before deciding on removal or correction.

When to Use Advanced Methods

• Use Mahalanobis distance to detect multivariate outliers when variables are correlated.
• Isolation Forest works well on high-dimensional data without assuming distribution.

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🔄 5. Handling Missing Data

Visualize Patterns

Identify missingness structure:

sns.heatmap(df.isnull(), cbar=False, cmap='viridis')

Imputation Strategies

• Simple: fill with mean, median, or mode.
• Advanced: use KNN or model-based imputations when missingness is non-random.

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🔁 6. Scaling, Encoding & Dimensionality Reduction

Scaling

Standardize or normalize features when models are sensitive to scale:

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df.select_dtypes(include=[np.number]))

Encoding

Convert categorical variables to numeric: - One-hot encoding for nominal categories. - Ordinal encoding for ordered categories.

Dimensionality Reduction

Use PCA or t-SNE to simplify data and visualize patterns:

from sklearn.decomposition import PCA
pca = PCA(n_components=2)
components = pca.fit_transform(df_scaled)

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✅ Summary Checklist

Task	Tool / Method	Notes
Data review	info(), describe()	Confirm types, missing data
Feature engineering	Interaction terms, binning	Capture new patterns
Relationships	Pairplot, heatmap, group stats	Explore variable interactions
Outlier detection	IQR, Z-score, Isolation Forest	Detect anomalies, review carefully
Missing data	Heatmap, imputation	Understand and handle missingness
Scaling & encoding	StandardScaler, one-hot	Prepare data for modeling
Dimensionality reduction	PCA, t-SNE	Simplify and visualize complex data

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Let me know if you'd like practical examples or workflows tailored to specific modeling tasks such as classification, regression, or clustering!