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Uncovering Patterns, Trends, and Insights Before
Modeling
🧠 Introduction
Before jumping into model building, a data scientist must
first explore the data — not just technically, but intellectually and
visually.
Exploratory Data Analysis (EDA) is the process of
analyzing datasets to summarize their main characteristics, detect anomalies,
and discover patterns using statistics and visualization.
EDA helps answer questions like:
This chapter will teach you how to:
📂 1. What is EDA?
Exploratory Data Analysis is the process of:
It’s non-linear and iterative — you may return to
data cleaning or feature engineering as you uncover insights.
🔍 2. Overview of the
Dataset
We’ll use the Titanic dataset as an example.
python
import
pandas as pd
df
= pd.read_csv('titanic.csv')
df.head()
Basic Info
python
df.info()
df.describe()
df.isnull().sum()
📊 3. Univariate Analysis
Analysis of individual features.
▶ Numerical Features
Histogram + KDE plot:
python
import
seaborn as sns
import
matplotlib.pyplot as plt
sns.histplot(df['Age'],
kde=True)
plt.title('Age
Distribution')
Boxplot for outliers:
python
sns.boxplot(x=df['Age'])
Summary stats:
python
df['Fare'].describe()
▶ Categorical Features
Bar chart for counts:
python
sns.countplot(x='Sex',
data=df)
Value counts:
python
df['Embarked'].value_counts(normalize=True)
🔗 4. Bivariate Analysis
Explore how one feature affects another, especially with
respect to the target variable.
▶ Categorical vs Target
python
sns.barplot(x='Sex',
y='Survived', data=df)
▶ Numerical vs Target
python
sns.boxplot(x='Survived',
y='Fare', data=df)
Groupby statistics:
python
df.groupby('Pclass')['Survived'].mean()
🔄 5. Multivariate
Analysis
Analyzing interactions between multiple variables.
▶ Pairplot
python
sns.pairplot(df[['Age',
'Fare', 'Survived']], hue='Survived')
▶ Heatmap of Correlation Matrix
python
corr
= df.corr()
sns.heatmap(corr,
annot=True, cmap='coolwarm')
What to look for:
📈 6. Feature vs. Feature
Sometimes you need to explore relationships between
features.
python
sns.scatterplot(x='Age',
y='Fare', hue='Survived', data=df)
▶ Grouped boxplot:
python
sns.boxplot(x='Pclass',
y='Age', hue='Survived', data=df)
🧪 7. Target Variable
Analysis
Understanding your target variable is crucial.
python
df['Survived'].value_counts(normalize=True).plot(kind='bar')
If imbalanced, consider:
📊 8. Dealing with Skewed
Features
python
from
scipy.stats import skew
skewness
= df['Fare'].skew()
print("Skewness:",
skewness)
Apply transformation:
python
df['Fare_log']
= np.log1p(df['Fare'])
sns.histplot(df['Fare_log'],
kde=True)
🧠 9. Questions to Ask
During EDA
Question |
Why it Matters |
Which variables are
strongly correlated? |
Helps with feature
selection and reduction |
Are any features heavily skewed? |
May require
transformation |
Is the target variable
imbalanced? |
Affects model
selection and evaluation |
Do any variables have many missing values? |
May be
excluded or filled |
Are there any
obvious outliers? |
May distort model
training |
✅ 10. Summary Table Example
Here’s a snapshot you might prepare from EDA:
Feature |
Type |
Missing % |
Skewness |
Correlation with
Target |
Age |
Numeric |
20% |
0.4 |
-0.08 |
Sex |
Category |
0% |
N/A |
0.54 |
Fare |
Numeric |
0% |
4.8 |
0.26 |
Pclass |
Ordinal |
0% |
0.8 |
-0.31 |
🧪 EDA Summary Example for
Titanic:
Answer: The data science workflow is a structured step-by-step process used to turn raw data into actionable insights or solutions. It ensures clarity, efficiency, and reproducibility from problem definition to deployment.
Answer: Not necessarily. While there is a general order, data science is iterative. You may go back and forth between stages (like EDA and feature engineering) as new insights emerge.
Answer: Data cleaning prepares the dataset by fixing errors and inconsistencies, while EDA explores the data to find patterns, trends, and relationships to inform modeling decisions.
Answer: You can build a baseline model early, but robust feature engineering often improves performance significantly. It's best to iterate and refine after EDA and feature transformations.
Answer: Popular tools include Python libraries like scikit-learn, XGBoost, LightGBM, and TensorFlow for building models, and metrics functions within sklearn.metrics for evaluation.
Answer: It depends on the problem:
Answer: Start with lightweight options like:
Answer: Use logging for predictions, track performance metrics over time, and set alerts for significant drops. Tools like MLflow, Prometheus, and AWS CloudWatch are commonly used.
Answer: Yes. For learning or portfolio-building, it's okay to stop after model evaluation. But deploying at least one model enhances your understanding of real-world applications.
Answer: Choose a simple dataset (like Titanic or housing prices), go through every workflow step end-to-end, and document your process. Repeat with different types of problems to build experience.
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