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Notebook","readme":"# Learn_EDA_for_Data_Science\n## Univariate, Bivariate and Multi-variate Analysis\n### Data structure\n### Data Type Conversion \n+ coerce will introduce NA values for non numeric data in the columns\n+ if there are values that cannot be changed into numeric it will throw an error therefore the above statement\n### Remove Duplicates\n+ Count of Duplicated Rows\n+ print the duplicated rows\n+ Drop Columns\n+ Rename the weird columns\n### Outlier Detection\n+ Box plot\n+ Extracting Outliers\n+ Fliers are Outliers\n+ To get Whiskers\n### Descriptive Stats\n### Check for Balaced or Imbalanced Data in Categorical data\n+ Bar Plot\n### Missing Values and Imputation\n+ Mean Imputation\n### Null values Imputation for categorical data/values\n+ Get the object values\n+ Missing value imputation for categorical value\n+ Join the data set with imputed object dataset\n### Scatter plot and Correlation Analysis\n### Transformation of Data\n+ Creating Dummy Values for weather 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