Data Cleaning and Handling Missing Values
Data cleaning is an essential step in the data preprocessing phase. It involves identifying and handling missing values, outliers, and other anomalies in the dataset. Handling missing values is crucial because many machine learning algorithms cannot handle incomplete data. Common techniques for handling missing values include:
- Deleting rows or columns with missing values
- Imputing missing values using mean, median, or mode
- Using advanced imputation techniques such as regression imputation
Feature Selection and Feature Engineering
Feature selection is the process of selecting the most relevant features from the dataset for model training. It helps reduce dimensionality and focuses on the features that contribute the most to the target variable. Feature engineering involves creating new features or transforming existing ones to improve model performance. Some techniques for feature selection and engineering include:
- Univariate feature selection
- Recursive feature elimination
- Principal Component Analysis (PCA)
- Creating interaction features
- Encoding categorical variables
Exploratory Data Analysis
Exploratory Data Analysis (EDA) involves analyzing and summarizing the main characteristics of the dataset. It helps uncover patterns, relationships, and potential insights that can guide further analysis. EDA techniques include:
- Summary statistics: Mean, median, mode, standard deviation, etc.
- Data distribution analysis: Histograms, box plots, etc.
- Correlation analysis: Heatmaps, scatter plots, correlation coefficients, etc.
- Outlier detection: Tukey’s fences, z-scores, etc.
Data Visualization Techniques
Data visualization is a powerful tool for understanding and communicating patterns and insights in the dataset. It provides a visual representation of the data, making it easier to identify trends, anomalies, and relationships. Common data visualization techniques include:
- Scatter plots
- Line charts
- Bar charts
- Histograms
- Heatmaps
- Box plots
- Pair plots
