Perform Data Transformation
Data transformation is a critical step within the EDA process because it enables you to prepare your statistics for similar evaluation and modeling. Depending on the traits of your information and the necessities of your analysis, you may need to carry out various ameliorations to ensure that your records are in the most appropriate layout.
Here are a few common records transformation strategies:
- Scaling or normalizing numerical variables to a standard variety (e.g., min-max scaling, standardization)
- Encoding categorical variables to be used in machine mastering fashions (e.g., one-warm encoding, label encoding)
- Applying mathematical differences to numerical variables (e.g., logarithmic, square root) to correct for skewness or non-linearity
- Creating derived variables or capabilities primarily based on current variables (e.g., calculating ratios, combining variables)
- Aggregating or grouping records mainly based on unique variables or situations
By accurately transforming your information, you could ensure that your evaluation and modeling strategies are implemented successfully and that your results are reliable and meaningful.
Encoding Categorical Variables
There are some models like Linear Regression which does not work with categorical dataset in that case we should try to encode categorical dataset into the numerical column. We can use different methods for encoding like Label encoding or One-hot encoding. pandas and sklearn provide different functions for encoding in our case we will use the LabelEncoding function from sklearn to encode the Gender column.
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
# fit and transform the "Senior Management" column with LabelEncoder
df['Gender'] = le.fit_transform(df['Gender'])
Steps for Mastering Exploratory Data Analysis | EDA Steps
Mastering exploratory data analysis (EDA) is crucial for understanding your data, identifying patterns, and generating insights that can inform further analysis or decision-making. Data is the lifeblood of cutting-edge groups, and the capability to extract insights from records has become a crucial talent in today’s statistics-pushed world. Exploratory Data Analysis (EDA) is a powerful method that allows analysts, scientists, and researchers to gain complete knowledge of their data earlier than projecting formal modeling or speculation testing.
It is an iterative procedure that entails summarizing, visualizing, and exploring information to find patterns, anomalies, and relationships that might not be apparent at once. In this complete article, we will understand and implement critical steps for performing Exploratory Data Analysis. Here are steps to help you master EDA:
Steps for Mastering Exploratory Data Analysis
- Step 1: Understand the Problem and the Data
- Step 2: Import and Inspect the Data
- Step 3: Handling Missing Values
- Step 4: Explore Data Characteristics
- Step 5: Perform Data Transformation
- Step 6: Visualize Data Relationships
- Step 7: Handling Outliers
- Step 8: Communicate Findings and Insights