Feature Engineering

Data Scientists Spend 80% of Time Here

Raw data is messy. Models need clean, meaningful numbers. Feature engineering is the art of transforming raw data into features that help models learn. It’s the difference between feeding a model “March 15, 1995” (a string it can’t use) and feeding it “30 years old, built pre-2000, winter construction” (numbers that carry meaning). Here’s a truth that surprises beginners: a simple model with great features almost always beats a complex model with raw features. Feature engineering is where domain knowledge meets data science, and it’s the single highest-leverage activity in most ML projects.

The House Price Example

Raw data:

Address: "123 Main St, New York, NY 10001"
Built: "March 15, 1995"  
Description: "Cozy 3BR, renovated kitchen, near subway"
Price: $850,000

What a model needs:

{
    'bedrooms': 3,
    'city_encoded': 45,        # New York
    'year_built': 1995,
    'building_age': 30,
    'is_renovated': 1,
    'near_transit': 1,
    'zip_price_tier': 3        # Expensive area
}

Handling Missing Values

import pandas as pd
import numpy as np

# Sample data with missing values
df = pd.DataFrame({
    'age': [25, np.nan, 35, 40, np.nan],
    'income': [50000, 60000, np.nan, 80000, 90000],
    'education': ['Bachelor', 'Master', np.nan, 'PhD', 'Bachelor']
})

print("Missing values:")
print(df.isnull().sum())

Strategy 1: Drop Missing Values

# Drop rows with ANY missing values -- the nuclear option.
# Only safe when: (1) you have plenty of data, (2) missingness is random,
# and (3) the missing rows aren't systematically different from the rest.
df_clean = df.dropna()

# Safer: drop rows missing only in critical columns
df_clean = df.dropna(subset=['age'])

Only use when you have lots of data and missingness is truly random (called MCAR — Missing Completely At Random). If high-income people tend to skip the income question, dropping those rows biases your model toward low-income profiles. Check this by comparing the distributions of other features between “has missing” and “no missing” groups.

Strategy 2: Imputation

from sklearn.impute import SimpleImputer

# Numeric: fill with mean, median, or constant
numeric_imputer = SimpleImputer(strategy='mean')  # or 'median', 'most_frequent'
df['age'] = numeric_imputer.fit_transform(df[['age']])

# Categorical: fill with mode or 'Unknown'
categorical_imputer = SimpleImputer(strategy='most_frequent')
df['education'] = categorical_imputer.fit_transform(df[['education']])

Strategy 3: Indicator Variables

# Create a flag for missing values (can be informative!)
# Why? Because "missing" is often not random -- it carries signal.
# For example, if income is missing, the person might have refused
# to share it, which itself correlates with certain behaviors.
df['age_missing'] = df['age'].isnull().astype(int)
df['age'] = df['age'].fillna(df['age'].median())

Which imputation strategy should you use? Use median for numeric features with outliers (median is robust to extreme values). Use mean for normally distributed features. Use mode (most frequent) for categorical features. And always create a missingness indicator — it’s free information and tree-based models will use it if it’s predictive.

Encoding Categorical Variables

Label Encoding (for ordinal categories)

Use this when categories have a natural order — like education levels, satisfaction ratings, or T-shirt sizes. The numbers you assign should reflect the ranking.

from sklearn.preprocessing import LabelEncoder

# Ordinal: order matters -- PhD > Master > Bachelor > High School
# The numeric values (0,1,2,3) encode this ranking, which means
# the model can learn "higher education = different outcome."
education_order = ['High School', 'Bachelor', 'Master', 'PhD']
df['education_encoded'] = df['education'].map({
    'High School': 0,
    'Bachelor': 1,
    'Master': 2,
    'PhD': 3
})
# Common mistake: using label encoding for nominal categories (like color).
# The model would think red=0 < blue=1 < green=2, which is meaningless.

One-Hot Encoding (for nominal categories)

Use this when categories have no natural order — like colors, countries, or product types. Each category becomes its own binary column.

from sklearn.preprocessing import OneHotEncoder
import pandas as pd

# Using pandas -- simpler for exploration
df_encoded = pd.get_dummies(df, columns=['color'], prefix='color')

# Using sklearn -- better for pipelines (remembers categories from training)
encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')
# handle_unknown='ignore' is critical: if test data has a category
# the model never saw during training, it won't crash -- it just
# sets all one-hot columns to 0 (an "unknown" embedding).
encoded = encoder.fit_transform(df[['color']])

Before:

id  color
 red
 blue
 green

After:

id  color_red  color_blue  color_green
 1          0           0
 0          1           0
 0          0           1

Target Encoding (for high-cardinality categories)

When a categorical feature has hundreds or thousands of unique values (like zip codes or product IDs), one-hot encoding creates an explosion of columns. Target encoding replaces each category with the average target value for that category — essentially asking “what’s the typical outcome for this group?”

# Replace category with mean target value.
# For a city like "San Francisco," this becomes the average house price
# in San Francisco -- a single number that captures location value.
city_means = df.groupby('city')['price'].mean()
df['city_encoded'] = df['city'].map(city_means)

Data leakage warning: Target encoding uses the target variable to create features, which can leak future information into training. Always compute means on training data only, and consider using smoothed target encoding (blending category mean with global mean) to reduce overfitting on rare categories. Libraries like category_encoders handle this correctly.

Scaling Numerical Features

Why Scale?

Many algorithms (SVM, KNN, neural networks) are sensitive to scale:

Age: 0-100
Income: 0-1,000,000

Without scaling, income would dominate!

StandardScaler (Z-score normalization)

x_{scaled} = \frac{x - \mu}{\sigma}

Centers each feature at 0, scales to unit variance. The most common choice for algorithms that assume normally distributed features (logistic regression, SVM, neural networks).

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)  # fit on training data, transform both

# Result: mean=0, std=1
print(f"Mean: {X_scaled.mean():.4f}")
print(f"Std:  {X_scaled.std():.4f}")

MinMaxScaler (0-1 normalization)

x_{scaled} = \frac{x - x_{min}}{x_{max} - x_{min}}

Maps every feature to [0, 1]. Best when you need bounded values (e.g., neural network inputs, or when features are already uniformly distributed). Sensitive to outliers — one extreme value can squash everything else into a narrow range.

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)

# Result: values between 0 and 1
print(f"Min: {X_scaled.min():.4f}")
print(f"Max: {X_scaled.max():.4f}")

RobustScaler (for outliers)

Uses median and IQR instead of mean and std. If your data has outliers that you don’t want to remove, this is your best bet — the median and IQR are not affected by extreme values.

from sklearn.preprocessing import RobustScaler

scaler = RobustScaler()
X_scaled = scaler.fit_transform(X)

Quick decision guide for scaling:

Scaler	Use When	Avoid When
StandardScaler	Default choice; features are roughly Gaussian	Features have many outliers
MinMaxScaler	You need bounded [0,1] values; data is uniformly distributed	Outliers exist (they’ll distort the range)
RobustScaler	Outliers are present but meaningful	Your data is already clean
No scaling	Using tree-based models (Random Forest, XGBoost)	Using distance-based or gradient-based models

Creating New Features

Mathematical Transformations

# For skewed distributions (right-skewed data like income, prices)
# Log transform compresses the long tail, making the distribution
# more symmetric. This helps linear models that assume normality.
df['income_log'] = np.log1p(df['income'])  # log(1+x) to handle zeros safely

# Square root -- a milder compression than log, good for count data
df['rooms_sqrt'] = np.sqrt(df['rooms'])

# Polynomial features -- captures non-linear relationships.
# If the relationship between age and target is U-shaped (young and old
# are both high-risk), a linear model can't capture this with age alone.
# Adding age^2 gives it the curvature it needs.
df['age_squared'] = df['age'] ** 2

Interaction Features

These capture relationships between features that the model might not discover on its own. They encode domain knowledge: “the combination of these two things matters, not just each one individually.”

# Combine features -- each ratio tells a specific story
df['price_per_sqft'] = df['price'] / df['sqft']           # Property value density
df['income_per_person'] = df['household_income'] / df['household_size']  # Individual buying power
df['age_income_ratio'] = df['age'] / df['income']          # Career progression proxy

Date Features

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

# Extract components
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day_of_week'] = df['date'].dt.dayofweek
df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)
df['quarter'] = df['date'].dt.quarter

# Time since event
df['days_since_purchase'] = (pd.Timestamp.now() - df['date']).dt.days

Text Features

# Length
df['description_length'] = df['description'].str.len()
df['word_count'] = df['description'].str.split().str.len()

# Contains specific words
df['has_discount'] = df['description'].str.contains('discount|sale|offer', case=False).astype(int)

Binning Continuous Variables

# Age groups
df['age_group'] = pd.cut(
    df['age'],
    bins=[0, 18, 35, 50, 65, 100],
    labels=['youth', 'young_adult', 'middle_age', 'senior', 'elderly']
)

# Equal-frequency binning (quantiles)
df['income_quantile'] = pd.qcut(
    df['income'],
    q=4,
    labels=['low', 'medium', 'high', 'very_high']
)

Handling Outliers

import numpy as np

def detect_outliers_iqr(data):
    Q1 = np.percentile(data, 25)
    Q3 = np.percentile(data, 75)
    IQR = Q3 - Q1
    lower = Q1 - 1.5 * IQR
    upper = Q3 + 1.5 * IQR
    return (data < lower) | (data > upper)

# Cap outliers (winsorization)
def cap_outliers(data, lower_percentile=1, upper_percentile=99):
    lower = np.percentile(data, lower_percentile)
    upper = np.percentile(data, upper_percentile)
    return np.clip(data, lower, upper)

df['income_capped'] = cap_outliers(df['income'])

Feature Selection

More features is not always better. Think of it like packing for a trip: bringing everything “just in case” makes your suitcase impossibly heavy and you can never find what you need. Feature selection is choosing to pack only what you’ll actually wear. Irrelevant features add noise, slow training, and can even hurt accuracy by diluting the signal.

Correlation Analysis

import seaborn as sns
import matplotlib.pyplot as plt

# Correlation matrix -- the first thing to check.
# Look for: (1) features highly correlated with target (good!)
# and (2) features highly correlated with each other (redundant -- drop one).
corr = df.corr()

# Heatmap
plt.figure(figsize=(12, 8))
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)
plt.title('Feature Correlations')
plt.show()

# Drop highly correlated features
def drop_correlated_features(df, threshold=0.9):
    corr_matrix = df.corr().abs()
    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
    to_drop = [column for column in upper.columns if any(upper[column] > threshold)]
    return df.drop(columns=to_drop)

Model-Based Selection

from sklearn.feature_selection import SelectKBest, f_classif, RFE
from sklearn.ensemble import RandomForestClassifier

# Select top k features by ANOVA F-score.
# f_classif tests whether each feature's mean differs across classes.
# It's fast but only catches linear relationships -- a feature with
# a U-shaped relationship to the target might score low.
selector = SelectKBest(f_classif, k=10)
X_selected = selector.fit_transform(X, y)

# Get selected feature names
selected_features = X.columns[selector.get_support()]
print("Selected features:", list(selected_features))

Recursive Feature Elimination

RFE works like a talent show elimination: train a model, eliminate the weakest feature, retrain, repeat. It’s slower but catches feature interactions that univariate tests miss.

from sklearn.feature_selection import RFE

model = RandomForestClassifier(n_estimators=50, random_state=42)
rfe = RFE(model, n_features_to_select=10)
rfe.fit(X, y)

# Get rankings
for name, rank in zip(X.columns, rfe.ranking_):
    print(f"{name}: Rank {rank}")

Feature Engineering Pipeline

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer

# Define column types
numeric_features = ['age', 'income', 'credit_score']
categorical_features = ['education', 'occupation', 'city']

# Create transformers
numeric_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='constant', fill_value='Unknown')),
    ('encoder', OneHotEncoder(handle_unknown='ignore', sparse_output=False))
])

# Combine
preprocessor = ColumnTransformer([
    ('num', numeric_transformer, numeric_features),
    ('cat', categorical_transformer, categorical_features)
])

# Full pipeline with model -- this is the professional way to build ML systems.
# The pipeline guarantees that preprocessing steps are applied identically
# during training and prediction, eliminating a whole class of production bugs.
from sklearn.ensemble import RandomForestClassifier

full_pipeline = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', RandomForestClassifier(n_estimators=100, random_state=42))
])

# Use it -- raw data goes in, predictions come out.
# Cross-validation, grid search, and joblib.dump all work
# seamlessly with pipelines because they're a single object.
full_pipeline.fit(X_train, y_train)
predictions = full_pipeline.predict(X_test)

Common Mistakes

Data Leakage

Problem: Using test data info during trainingFix: Always fit transformers on train data only

# Wrong
scaler.fit(X)  # Uses all data

# Right
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

Scaling After Split

Problem: Scaling before train-test splitFix: Split first, then scale

# Right order:
# 1. Train-test split
# 2. Fit scaler on train
# 3. Transform both

🚀 Mini Projects

Project 1: E-commerce Feature Engineer

Transform raw transaction data into predictive features

Project 2: Date-Time Feature Factory

Extract powerful temporal features from timestamps

Project 3: Text Feature Extractor

Convert text data into numerical features

Project 4: Automated Feature Pipeline

Build an end-to-end feature engineering pipeline

Project 1: E-commerce Feature Engineer

Transform raw e-commerce transaction data into features that predict customer churn.

Project 2: Date-Time Feature Factory

Extract powerful temporal features from timestamp data.

Project 3: Text Feature Extractor

Convert text data into numerical features for machine learning.

Project 4: Automated Feature Pipeline

Build an end-to-end feature engineering pipeline that handles multiple data types.

Key Takeaways

Handle Missing Data

Impute or create indicator variables

Encode Categories

One-hot for nominal, ordinal for ordered

Scale Features

StandardScaler or MinMaxScaler for most algorithms

Create Features

Domain knowledge creates the best features

🧹 Real-World Messy Data: Complete Guide

Handling Every Type of Data Problem

Missing Values Decision Tree

Is data missing?
├── < 5% missing
│   └── Safe to drop rows OR simple imputation (mean/median)
├── 5-30% missing
│   ├── Is missingness random?
│   │   ├── Yes → Impute with mean/median/mode
│   │   └── No (informative) → Create "is_missing" indicator + impute
│   └── Consider multiple imputation for important analyses
└── > 30% missing
    ├── Is the feature important?
    │   ├── Yes → Advanced imputation (KNN, iterative)
    │   └── No → Consider dropping the feature
    └── Investigate WHY data is missing

# Production-ready missing value handler
def handle_missing_values(df, missing_threshold=0.3):
    """
    Handle missing values with best practices.
    """
    report = []
    
    for col in df.columns:
        missing_pct = df[col].isnull().mean()
        
        if missing_pct == 0:
            continue
        elif missing_pct > missing_threshold:
            report.append(f"⚠️ {col}: {missing_pct:.1%} missing - consider dropping")
        elif df[col].dtype in ['float64', 'int64']:
            # Numeric: impute with median (robust to outliers)
            df[f'{col}_missing'] = df[col].isnull().astype(int)
            df[col].fillna(df[col].median(), inplace=True)
            report.append(f"✓ {col}: imputed with median, created indicator")
        else:
            # Categorical: impute with mode or 'Unknown'
            df[col].fillna(df[col].mode()[0] if len(df[col].mode()) > 0 else 'Unknown', inplace=True)
            report.append(f"✓ {col}: imputed with mode/Unknown")
    
    return df, report

Outlier Detection & Treatment

import numpy as np
from scipy import stats

def detect_and_handle_outliers(df, columns, method='iqr', action='cap'):
    """
    Detect outliers using IQR or Z-score, then handle them.
    
    Parameters:
    - method: 'iqr' (robust) or 'zscore' (assumes normality)
    - action: 'cap' (winsorize), 'remove', or 'flag'
    """
    for col in columns:
        if method == 'iqr':
            Q1, Q3 = df[col].quantile([0.25, 0.75])
            IQR = Q3 - Q1
            lower = Q1 - 1.5 * IQR
            upper = Q3 + 1.5 * IQR
        else:  # zscore
            z = np.abs(stats.zscore(df[col].dropna()))
            lower = df[col].mean() - 3 * df[col].std()
            upper = df[col].mean() + 3 * df[col].std()
        
        outliers = (df[col] < lower) | (df[col] > upper)
        n_outliers = outliers.sum()
        
        if n_outliers > 0:
            if action == 'cap':
                df[col] = df[col].clip(lower=lower, upper=upper)
                print(f"  {col}: capped {n_outliers} outliers to [{lower:.2f}, {upper:.2f}]")
            elif action == 'remove':
                df = df[~outliers]
                print(f"  {col}: removed {n_outliers} outlier rows")
            else:  # flag
                df[f'{col}_is_outlier'] = outliers.astype(int)
                print(f"  {col}: flagged {n_outliers} outliers")
    
    return df

Handling Skewed Distributions

from sklearn.preprocessing import PowerTransformer

def handle_skewness(df, columns, threshold=1.0):
    """
    Apply log or Yeo-Johnson transform to skewed features.
    """
    from scipy.stats import skew
    
    for col in columns:
        col_skew = skew(df[col].dropna())
        
        if abs(col_skew) > threshold:
            if (df[col] > 0).all():
                # Log transform for positive data
                df[f'{col}_log'] = np.log1p(df[col])
                print(f"  {col}: skew={col_skew:.2f} → log transform applied")
            else:
                # Yeo-Johnson for any data
                pt = PowerTransformer(method='yeo-johnson')
                df[f'{col}_transformed'] = pt.fit_transform(df[[col]])
                print(f"  {col}: skew={col_skew:.2f} → Yeo-Johnson applied")
    
    return df

🔗 Math → ML Connection

Feature engineering connects to these mathematical concepts:

Technique	Math Concept	Why It Works
Standardization	Z-scores from statistics	Makes gradient descent converge faster
One-hot encoding	Orthogonal basis vectors	Each category becomes a dimension
Log transforms	Properties of logarithms	Linearizes exponential relationships
Polynomial features	Polynomial functions	Captures nonlinear patterns
PCA features	Eigenvalue decomposition	Finds directions of max variance
Interaction terms	Cross-products	Models combined effects

The Linear Algebra course covers why these transformations work geometrically.

🚀 Going Deeper (Optional)

Advanced Feature Engineering Techniques

Target Encoding (for High-Cardinality Categoricals)

When a categorical has 1000+ unique values, one-hot encoding creates too many features:

from sklearn.model_selection import KFold

def target_encode(df, cat_col, target_col, n_splits=5):
    """
    Replace category with mean target value (using cross-validation to prevent leakage).
    """
    df[f'{cat_col}_target_enc'] = np.nan
    kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
    
    for train_idx, val_idx in kf.split(df):
        # Calculate means only on training fold
        means = df.iloc[train_idx].groupby(cat_col)[target_col].mean()
        # Apply to validation fold
        df.loc[df.index[val_idx], f'{cat_col}_target_enc'] = \
            df.iloc[val_idx][cat_col].map(means)
    
    # Fill any remaining NaN with global mean
    df[f'{cat_col}_target_enc'].fillna(df[target_col].mean(), inplace=True)
    
    return df

Time-Based Features

def create_time_features(df, date_col):
    """
    Extract rich features from datetime columns.
    """
    df[date_col] = pd.to_datetime(df[date_col])
    
    # Basic extractions
    df['year'] = df[date_col].dt.year
    df['month'] = df[date_col].dt.month
    df['day'] = df[date_col].dt.day
    df['dayofweek'] = df[date_col].dt.dayofweek  # 0=Monday
    df['hour'] = df[date_col].dt.hour
    
    # Cyclical encoding (preserves continuity: Dec → Jan)
    df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
    df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
    df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
    df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
    
    # Business features
    df['is_weekend'] = (df['dayofweek'] >= 5).astype(int)
    df['is_month_start'] = df[date_col].dt.is_month_start.astype(int)
    df['is_month_end'] = df[date_col].dt.is_month_end.astype(int)
    
    return df

Automated Feature Engineering

# Using featuretools for automated feature generation
import featuretools as ft

# Define entity set
es = ft.EntitySet(id='customers')
es.add_dataframe(dataframe_name='transactions', dataframe=transactions_df,
                 index='transaction_id', time_index='timestamp')
es.add_dataframe(dataframe_name='customers', dataframe=customers_df,
                 index='customer_id')

# Create relationship
es.add_relationship('customers', 'customer_id', 'transactions', 'customer_id')

# Automatically generate features
features, feature_names = ft.dfs(
    entityset=es,
    target_dataframe_name='customers',
    agg_primitives=['sum', 'mean', 'count', 'max', 'min', 'std'],
    trans_primitives=['month', 'year', 'weekday'],
    max_depth=2
)

What’s Next?

Now you know how to prepare data. But how do you find the best hyperparameters?

Continue to Module 9: Hyperparameter Tuning

Learn Grid Search, Random Search, and Bayesian Optimization

​Feature Engineering

​Data Scientists Spend 80% of Time Here

​The House Price Example

​Handling Missing Values

​Strategy 1: Drop Missing Values

​Strategy 2: Imputation

​Strategy 3: Indicator Variables

​Encoding Categorical Variables

​Label Encoding (for ordinal categories)

​One-Hot Encoding (for nominal categories)

​Target Encoding (for high-cardinality categories)

​Scaling Numerical Features

​Why Scale?

​StandardScaler (Z-score normalization)

​MinMaxScaler (0-1 normalization)

​RobustScaler (for outliers)

​Creating New Features

​Mathematical Transformations

​Interaction Features

​Date Features

​Text Features

​Binning Continuous Variables

​Handling Outliers

​Feature Selection

​Correlation Analysis

​Model-Based Selection

​Recursive Feature Elimination

​Feature Engineering Pipeline

​Common Mistakes

Data Leakage

Scaling After Split

​🚀 Mini Projects

Project 1: E-commerce Feature Engineer

Project 2: Date-Time Feature Factory

Project 3: Text Feature Extractor

Project 4: Automated Feature Pipeline

​Project 1: E-commerce Feature Engineer

​Project 2: Date-Time Feature Factory

​Project 3: Text Feature Extractor

​Project 4: Automated Feature Pipeline

​Key Takeaways

Handle Missing Data

Encode Categories

Scale Features

Create Features

​🧹 Real-World Messy Data: Complete Guide

​Missing Values Decision Tree

​Outlier Detection & Treatment

​Handling Skewed Distributions

​🔗 Math → ML Connection

​🚀 Going Deeper (Optional)

​Target Encoding (for High-Cardinality Categoricals)

​Time-Based Features

​Automated Feature Engineering

​What’s Next?

Continue to Module 9: Hyperparameter Tuning

Feature Engineering

Data Scientists Spend 80% of Time Here

The House Price Example

Handling Missing Values

Strategy 1: Drop Missing Values

Strategy 2: Imputation

Strategy 3: Indicator Variables

Encoding Categorical Variables

Label Encoding (for ordinal categories)

One-Hot Encoding (for nominal categories)

Target Encoding (for high-cardinality categories)

Scaling Numerical Features

Why Scale?

StandardScaler (Z-score normalization)

MinMaxScaler (0-1 normalization)

RobustScaler (for outliers)

Creating New Features

Mathematical Transformations

Interaction Features

Date Features

Text Features

Binning Continuous Variables

Handling Outliers

Feature Selection

Correlation Analysis

Model-Based Selection

Recursive Feature Elimination

Feature Engineering Pipeline

Common Mistakes

🚀 Mini Projects

Project 1: E-commerce Feature Engineer

Project 2: Date-Time Feature Factory

Project 3: Text Feature Extractor

Project 4: Automated Feature Pipeline

Key Takeaways

🧹 Real-World Messy Data: Complete Guide

Missing Values Decision Tree

Outlier Detection & Treatment

Handling Skewed Distributions

🔗 Math → ML Connection

🚀 Going Deeper (Optional)

Target Encoding (for High-Cardinality Categoricals)

Time-Based Features

Automated Feature Engineering

What’s Next?