https://github.com/moindalvs/learn_eda_for_data_science
Univariate, Bivariate and Multi-variate Analysis
https://github.com/moindalvs/learn_eda_for_data_science
bivariate-analysis correlation-analysis data-science data-transformation data-type-conversion data-types-and-structures data-visualization duplicates-removal exploratory-data-analysis imputation missing-values multi-variate-analysis normalization outlier-detection pandas-profiling standardization univariate-analysis
Last synced: 7 months ago
JSON representation
Univariate, Bivariate and Multi-variate Analysis
- Host: GitHub
- URL: https://github.com/moindalvs/learn_eda_for_data_science
- Owner: MoinDalvs
- Created: 2022-03-26T09:53:39.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-03-26T10:04:04.000Z (over 3 years ago)
- Last Synced: 2025-01-18T00:44:06.199Z (9 months ago)
- Topics: bivariate-analysis, correlation-analysis, data-science, data-transformation, data-type-conversion, data-types-and-structures, data-visualization, duplicates-removal, exploratory-data-analysis, imputation, missing-values, multi-variate-analysis, normalization, outlier-detection, pandas-profiling, standardization, univariate-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 443 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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