✅ Exploratory Data Analysis (EDA) 🔍📊

EDA is the first and most important step in any data analytics or machine learning project. It helps you understand the data, spot patterns, detect outliers, and prepare for modeling.

1️⃣ Load and Understand the Data

import pandas as pd

df = pd.read_csv("sales_data.csv")
print(df.head())
print(df.shape)

Goal: Get the structure (rows, columns), data types, and sample values.

2️⃣ Summary and Info

df.info()
df.describe()

Goal:
• See null values
• Understand distributions (mean, std, min, max)

3️⃣ Check for Missing Values

df.isnull().sum()

📌 Fix options:
• df.fillna(0) – Fill missing values
• df.dropna() – Remove rows with nulls

4️⃣ Unique Values Frequency Counts

df['Region'].value_counts()
df['Product'].unique()

Goal: Understand categorical features.

5️⃣ Data Type Conversion (if needed)

df['Date'] = pd.to_datetime(df['Date'])
df['Amount'] = df['Amount'].astype(float)

6️⃣ Detecting Duplicates Removing

df.duplicated().sum()
df.drop_duplicates(inplace=True)

7️⃣ Univariate Analysis (1 Variable)

import seaborn as sns
import matplotlib.pyplot as plt

sns.histplot(df['Sales'])
sns.boxplot(y=df['Profit'])
plt.show()

Goal: View distribution and detect outliers.

8️⃣ Bivariate Analysis (2 Variables)

sns.scatterplot(x='Sales', y='Profit', data=df)
sns.boxplot(x='Region', y='Sales', data=df)

9️⃣ Correlation Analysis

sns.heatmap(df.corr(numeric_only=True), annot=True)

Goal: Identify relationships between numerical features.

🔟 Grouped Aggregation

df.groupby('Region')['Revenue'].sum()
df.groupby(['Region', 'Category'])['Sales'].mean()

Goal: Segment data and compare.

1️⃣1️⃣ Time Series Trends (If date present)

df.set_index('Date')['Sales'].resample('M').sum().plot()
plt.title("Monthly Sales Trend")

🧠 Key Questions to Ask During EDA:
• Are there missing or duplicate values?
• Which products or regions perform best?
• Are there seasonal trends in sales?
• Are there outliers or strange values?
• Which variables are strongly correlated?

🎯 Goal of EDA:
• Spot data quality issues
• Understand feature relationships
• Prepare for modeling or dashboarding

💬 Tap ❤️ for more!