A Data Pre Processing Package
Project description
This is a Data pre processing package where you can Treat 1) Missing Values using Traditional method - Mean , Median, Mode,Knn Method 2) Outlier Treatment using - IQR , Zscore 3) Feature Scaling using - Standard Scalar , Min Max Scalar, Robust Scalar, Max absolute scalar.
——————Missing Value Treatment———————————
Mean - treat_mean(dataframe) Median - treat_median(dataframe) Mode - treat_mode(dataframe) KNN - treat_knn(dataframe,int) # int specify nearest neighbour by default 1
———Get information of a dataframe ———————————
info(dataframe)
————————–Outlier Treatment—————————————————–
IQR — ot_iqr(dataframe,column_name)
Zscore– ot_zscore(dataframe,column_name)
————————————–Feature Scaling—————————————
Standard Scalar — f_standardscalar(dataframe)
Min Max Scalar — f_minmax(dataframe)
Robust Scalar —- f_robustscalar(dataframe)
Max absolute Scalar — f_maxabs(dataframe)
——————Data Visualization———————————————————————-
bar(df) heatmap(df) matrix(df) dendrogram(df) geoplot(df)
- ——————————You can also use the GUI version of our package—————————————
———–We’ll love it if give it a try——————-
https://share.streamlit.io/mohammed-muzzammil/data_pre_processing/main/st1.py
————————-More Information on our Website—————————————– https://mohammed-muzzammil.github.io/dataprepreps
Change Log
0.0.1 (22/11/2020)
First Release
0.0.5 (29/11/2020)
-Fifth Realease
Added Data Visualization
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