Data Preprocessing (Numeric features)

This repo is created just for personal use (feel free to use any part of the code)

StandardScaler-example This is an example for StandardScaler (for Numeric features )
RobustScaler-example This is an example for RobustScaler (for Numeric features)
Normalization And Cosine Similarity
Factor Analysis (and Classification)
Outlier Detection using Local Outlier Factor (LOF)
Outlier Detection using Isolation Forest
Outlier Detection using EllipticEnvelope
Artificial-Datasets (To generate sample data for classification, regression, clustering, etc.)
Data pre-processing: in this notebook, I have tried to find out the hidden missing values and impute them with the mean. Besides, a classification model has been used for creating a prediction model for diabetes dataset.

Name		Name	Last commit message	Last commit date
Latest commit History 52 Commits
.ipynb_checkpoints		.ipynb_checkpoints
datasets		datasets
.DS_Store		.DS_Store
01-StandardScaler.ipynb		01-StandardScaler.ipynb
02-RobustScaler.ipynb		02-RobustScaler.ipynb
03-NormalizationAndCosineSimilarity.ipynb		03-NormalizationAndCosineSimilarity.ipynb
04-FactorAnalysis_Classification.ipynb		04-FactorAnalysis_Classification.ipynb
05-OutlierDetection_Local-Outlier-Factor-(LOF).ipynb		05-OutlierDetection_Local-Outlier-Factor-(LOF).ipynb
06-OutlierDetection_Isolation-Forest.ipynb		06-OutlierDetection_Isolation-Forest.ipynb
07-OutlierDetection.ipynb		07-OutlierDetection.ipynb
08-Artificial-Datasets.ipynb		08-Artificial-Datasets.ipynb
09-Data-pre-processing.ipynb		09-Data-pre-processing.ipynb
10-AutoDataCleaner.ipynb		10-AutoDataCleaner.ipynb
README.md		README.md
diabetes.png		diabetes.png

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