### 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