min-max-scaler

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jeremy191 / clustering-based-anomaly-detection

This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets

anaconda clustering dataset kmeans-clustering lof anomaly-detection f1-score ids2017 normalized-mutual-info nsl-kdd isolation-forest missing-values onehot-encoder dbscan-algorithm clustering-algorithms min-max-scaler simple-imputer score-metrics

Updated Dec 5, 2019
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shanuhalli / Assignment-Clustering

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Perform Clustering (Hierarchical, K Means Clustering and DBSCAN) for the airlines and crime data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.

clustering exploratory-data-analysis dimensionality-reduction data-preprocessing dbscan-clustering dendogram k-means-clustering elbow-method min-max-scaler silhouette-score hirarchical-clustering standard-scaler assignment-07

Updated Dec 28, 2022
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vaitybharati / P23.-EDA-1

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EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).

python data exploratory-data-analysis transformations eda scatter-plot outlier-detection pandas-profiling categorical-data correlation-analysis numerical-data min-max-scaler missing-value-imputation sweetviz standard-scaler auto-eda data-type-conversion

Updated May 28, 2021
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sushantdhumak / Finding-Donors-for-Charity-using-Machine-Learning

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Machine Learning Nano-degree Project : To help a charity organization identify people most likely to donate to their cause

feature-selection supervised-learning accuracy confusion-matrix data-preprocessing data-normalization tuning-parameters gradient-boosting f1-score feature-importance gridsearchcv finding-donors naive-predictor skewed-data min-max-scaler fbeta-score

Updated Oct 19, 2019
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shanuhalli / Assignment-PCA

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Perform Principal component analysis and perform clustering using first 3 principal component scores both Heirarchial and K Means Clustering and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data.