Master Data Cleaning and Preprocessing with the Titanic Dataset

Introduction

Ever wondered how to transform raw data into a goldmine of insights for machine learning models? Our course on “Data Cleaning and Preprocessing in Machine Learning” using the Titanic Dataset is your gateway to mastering the essentials of data preparation. From handling missing values to outlier detection and feature engineering, this course equips you with practical skills using Python and Pandas. Let’s dive into the key lessons and practices that will set you on the path to becoming a data wrangling expert!

Data Preprocessing: The Titanic Dataset Exploration

• Overview: Begin your journey by exploring the Titanic Dataset. Learn to preprocess data, filter based on age and fare, and debug dataset loading issues. [1] [2] [3] [4] [5][6][7]
• Key Activities:
  • Adjust Filtering to Age and Fare
  • Debug the Titanic Dataset Loading Code

Wrangling Missing Data: Techniques Applied to the Titanic Dataset

• Overview: Missing data can skew your analysis. This lesson covers handling missing values efficiently.
• Key Activities:
  • Handle Missing Data in the Titanic Dataset
  • Update Titanic Dataset Handling Missing Data Code
  • Data Cleaning in Titanic Dataset

Outlier Detection and Handling in the Titanic Dataset

• Overview: Outliers can significantly impact model performance. Learn to detect and handle outliers using various methods.
• Key Activities:
  • Detecting Outliers in Titanic Dataset Using Standard Deviation
  • Detecting Outliers in Titanic Dataset Using IQR Method
  • Identifying and Handling Outliers using the IQR Method

Data Transformation with the Titanic Dataset

• Overview: Transforming data is crucial for model performance. This lesson covers scaling and encoding techniques.
• Key Activities:
  • Applying MinMaxScaler to Multiple Features
  • Applying One-Hot Encoding to Categorical Features

Data Preprocessing: Mastering Normalization and Standardization Techniques

• Overview: Normalize and standardize data to improve model accuracy. Focus on the ‘age’ and ‘fare’ columns with missing values.
• Key Activities:
  • Normalize the ‘age’ Column
  • Standardize the ‘fare’ Column with NaN values
  • Normalize and Standardize ‘age’ and ‘fare’ Columns with Missing Values

Feature Engineering: Enhancing the Titanic Dataset for Survival Predictions

• Overview: Enhance your dataset with feature engineering techniques to improve model predictions.
• Key Activities:
  • Implement Log Transformation on ‘fare’ Feature
  • Implement Binary Encoding on ’embark_town’ Feature
  • Implement One-Hot Encoding on ‘class’ Feature

Training a Machine Learning Model with the Titanic Dataset

• Overview: Apply your preprocessing skills to train and evaluate a machine learning model. Understand feature importance in logistic regression.
• Key Activities:
  • Preprocessing Train and Test data
  • Fix the Titanic Machine Learning Model
  • Evaluate the Model with Different Metrics
  • Understand Feature Importance in Logistic Regression

Conclusion

Mastering data cleaning and preprocessing is a fundamental step in building robust machine learning models. Our course, using the Titanic Dataset, offers a practical and thorough approach to these essential skills. Whether you’re handling missing data, detecting outliers, or transforming features, this learning path will empower you to prepare your data like a pro.