This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets
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Updated
Dec 5, 2019 - Jupyter Notebook
This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets
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.
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).
Machine Learning Nano-degree Project : To help a charity organization identify people most likely to donate to their cause
Evaluating the reviews and sorting them correcly in Amazon
Data Science - PCA (Principal Component Analysis)
To predict which customer is most likely to convert
News channel CNBE wants to analyze recent elections. This survey was conducted on 1525 voters with 9 variables. Model is built to predict which party a voter will vote for on the basis of the given information, to create an exit poll that will help in predicting overall win and seats covered by a particular party.
Predicting Customer Response to Telemarketing Campaigns for Term Deposit. Output variable Whether the client has subscribed a term deposit or not.
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.
Data Science - Clustering Work
Machine Learning course of Piero Savastano 6: scaling, MinMaxScaler, KNeighborsClassifier, accuracy_score
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