データ処理と特徴量エンジニアリング（121～150）– モデルの性能を最大限に引き出すためのデータ処理技術を学びます。 –

Recap: Methods for Imputing Missing Values In the previous lesson, we discussed methods for handling missing values using the mean, median, and mode. We explained when and how to apply each method to mitigate the negative impact of missi...

Recap: SMOTE for Over-Sampling In the previous lesson, we explored SMOTE (Synthetic Minority Over-sampling Technique), an effective method for addressing data imbalance by generating synthetic data for the minority class. SMOTE helps bal...

Recap: Addressing Data Imbalance In the previous lesson, we discussed various sampling techniques for handling imbalanced data in classification tasks. We covered over-sampling to increase the minority class data and under-sampling to re...

Recap: Applications of Dimensionality Reduction In the previous lesson, we learned about dimensionality reduction techniques like t-SNE and UMAP, which transform high-dimensional data into lower dimensions for better visualization and im...

Recap: Correlation Analysis In the previous lesson, we explored Correlation Analysis, a method used to measure the relationships between features and the target variable. By examining correlation coefficients and visualizing data through...

Recap: Feature Selection Techniques In the previous lesson, we covered Feature Selection Techniques, which involve removing unnecessary features to improve model performance. We discussed three main approaches: Filter Methods, Wrapper Me...

Recap: Feature Engineering In the previous lesson, we learned about Feature Engineering, which involves creating new features from data to improve model performance. We explored various techniques, such as mathematical operations, encodi...

Recap: Scaling Numerical Data In the previous lesson, we learned about Min-Max Scaling and Standardization. These techniques help adjust the range and variability of data, enhancing the efficiency and performance of machine learning mode...

Recap: Preprocessing Text Data In the previous lesson, we explored tokenization, stemming, and lemmatization—techniques for preprocessing text data. These methods transform natural language data into a format suitable for machine learnin...

Recap: Handling Categorical Variables In the previous lesson, we discussed Label Encoding and One-Hot Encoding, methods for converting categorical variables into numerical formats. Since categorical data cannot be directly input into mac...

Recap: Data Distribution and Statistical Measures In the previous lesson, we explored statistical measures for understanding the center and spread of data. We covered concepts like the mean, median, standard deviation, and variance, and ...

Recap: Detecting Anomalies In the previous lesson, we covered methods for identifying anomalies in data, using techniques like Z-score, IQR (Interquartile Range), and box plots. These tools help pinpoint data points that deviate signific...

Recap: Data Visualization In the previous lesson, we covered Data Visualization, explaining how using methods such as bar charts, line charts, and scatter plots can make it easier to intuitively understand data patterns and trends. We al...

Recap: Summary and Review of Chapter 4 In the previous lesson, we reviewed the content covered in Chapter 4 and conducted a summary and review quiz to deepen our understanding. The chapter covered a wide range of topics, from the basics ...