Simple statistical functions that are useful for exploratory spatial data analysis (ESDA) on-the-fly in JavaScript

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.

Data Science - PCA (Principal Component Analysis)

Data Science - Clustering Work

Evaluating the reviews and sorting them correcly in Amazon

Machine Learning course of Piero Savastano 6: scaling, MinMaxScaler, KNeighborsClassifier, accuracy_score

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.

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.

Predicting Customer Response to Telemarketing Campaigns for Term Deposit. Output variable Whether the client has subscribed a term deposit or not.

To predict which customer is most likely to convert

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).

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

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