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# System prepended metadata

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### How to represent the ML model step by step?

- Importing Libraries and data 
- Feature Engineering
    - Exploratory Data Analysis (EDA)
        - Numerial Feautures
            - Discrete Variables
                - Store the feature in a variable
                - Show the feaures
                - group by feature, select the target variable , take the median, plot a bar plot
            - Continuous Variables
                - Store the feature in a variable
                - Show the feaures
                - Histogram plot with mentioning bins
                - If histogram is not normally distributed, we can use log transform 
        - Categorical Features
            - Store the categorical features in a variable
            - Show the features along with count of unquie categories
            - group by feature, select the target variable , take the median, plot a bar plot
        - Temporal Feature(eg: DateTime features)
        - Missing value Detection
            - Mean
            - Median
            - Mode
            - Advanced ways as in Titanic Problem
        - Outlier Detection
    - Drop duplicates 
    - Handling Missing values
    - Handling Outliers
    - Scaling the data
        - Standardization
        - Normalization
    - Convert categorical features into numerical features (encoding)
    - Create features if necesary
- Feature Selection
- Model Building


