View Code? Open in Web Editor
NEW
Univariate, Bivariate and Multi-variate Analysis
learn_eda_for_data_science's Introduction
Learn_EDA_for_Data_Science
Univariate, Bivariate and Multi-variate Analysis
- coerce will introduce NA values for non numeric data in the columns
- if there are values that cannot be changed into numeric it will throw an error therefore the above statement
- Count of Duplicated Rows
- print the duplicated rows
- Drop Columns
- Rename the weird columns
- Box plot
- Extracting Outliers
- Fliers are Outliers
- To get Whiskers
Check for Balaced or Imbalanced Data in Categorical data
Missing Values and Imputation
Null values Imputation for categorical data/values
- Get the object values
- Missing value imputation for categorical value
- Join the data set with imputed object dataset
Scatter plot and Correlation Analysis
- Creating Dummy Values for weather column
Normalization of the Data range(0 to 1)
Standardize data (0 mean, 1 std) range(-3 sigma to +3 sigma)