⚒️ Data Preprocessing Techniques ✨

Data Preprocessing is that step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it. In other words, the features of the data now become Algorithm interpretable.

By the end of this ,you will be equiped to data handle gracefully.so lets gets started 🏃‍♀️

Why Data Preprocessing ⚡

✅Accuracy: To check whether the data entered is correct or not.
✅Believability: The data should be trustable.
✅Completeness: To check whether the data is available or not recorded.
✅Consistency: To check whether the same data is kept in all the places that do or do not match.
✅Interpretability: The understandability of the data.
✅Timeliness: The data should be updated correctly.

Primary Tasks 🎯

Libraries required

Use the package manager pip to install below

pip install numpy
pip install pandas
pip install sklearn

Table of Content:

No	Topics	Code Link 🔗
1	Cardinality Encoding	Code
2	Delete Missing Values	Code
3	Delete outliers	Code
4	Feature Discreatization	Code
5	Feature Rescaling	Code
6	Handling Imbalance	Code
7	Data Imputation - Mean	Code
8	Imputation Missing Labels	Code
9	Normalization	Code
10	One Hot Encoding	Code
11	Outliers Dealing	Code
12	Pandas Categorical with Sklearn	Code
13	Preprocess Categorical Features	Code
14	Standardize IRIS	Code

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@misc{Charged Neuron,
    author       = {Roja Achary},
    title        = {Data Preprocessing Techniques},
    Credits      = {websites,CA,me,AV},
    month        = {November},
    year         = {2021}
}