learn_eda_for_data_science's Introduction

Learn_EDA_for_Data_Science

Univariate, Bivariate and Multi-variate Analysis

Data structure

Data Type Conversion

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

Remove Duplicates

Count of Duplicated Rows
print the duplicated rows
Drop Columns
Rename the weird columns

Outlier Detection

Box plot
Extracting Outliers
Fliers are Outliers
To get Whiskers

Descriptive Stats

Check for Balaced or Imbalanced Data in Categorical data

Bar Plot

Missing Values and Imputation

Mean 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

Transformation of Data

Creating Dummy Values for weather column

Normalization of the Data range(0 to 1)

Summarize Transform data

Standardize data (0 mean, 1 std) range(-3 sigma to +3 sigma)

Speed Up EDA Process

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