Handling Missing Data and Categorical Features

Handling Missing Data and Categorical Features

By: Bindeshwar Singh Kushwaha

---

Data Preprocessing Flow

• Raw Data → Handle Missing Values → Encode Categorical Variables → Feature Scaling → Preprocessed Data

---

Overview of Data Preprocessing

• Load Titanic dataset from CSV file
• Handle missing values using various techniques
• Encode categorical data for machine learning
• Save the cleaned dataset to a new CSV file

---

Step 1: Load the Titanic Dataset

import pandas as pd
df = pd.read_csv('titanic.csv')

---

Step 2: View the First Few Rows

print(df.head())

Use df.head() to preview the dataset structure.

---

Step 3: Checking for Missing Values

print(df.isnull().sum())

This helps identify missing data in columns.

---

Step 4: Handle Missing Values

df['Age'].fillna(df['Age'].median(), inplace=True)
df.dropna(subset=['Embarked'], inplace=True)

Fill missing Age values with median, drop rows with missing ‘Embarked’.

---

Step 5: Verify Missing Values

print(df.isnull().sum())

Ensure no missing values remain.

---

Step 6: Data Overview

print(df.describe())

Summary statistics for numerical columns.

---

Step 7: Encoding Categorical Data

df['Sex'] = df['Sex'].map({'male': 0, 'female': 1})

Convert gender into numerical form for model compatibility.

---

Step 8: Save the Cleaned Dataset

df.to_csv('cleaned_titanic.csv', index=False)

Store the cleaned data for future use.

---

Python Libraries Used

• pandas – Data manipulation
• numpy – Numerical operations
• scikit-learn – Machine learning preprocessing tools

---

PDF

PreprocessingML-2

Video

Reach PostNetwork Academy

• Website: www.postnetwork.co
• YouTube: youtube.com/@postnetworkacademy
• Facebook: facebook.com/postnetworkacademy
• LinkedIn: linkedin.com/company/postnetworkacademy
• GitHub: github.com/postnetworkacademy

---

Thank You