KVSAkhilesh

Anomaly-detection-using-Anomalize

Anomaly detection using anomalize package and finding root cause for using Discriminate analysis.

Binary-Hierarchical-Classifier-

Ensemble of multiple classes using binary Hierarchical classifier using SVM

Channel_Attribution

Decathlon-Ariticle-prediction-

Topic modelling on decathlon sport articles using LDA(Latent Dirichlet allocation)

Diabetes-Readmission-prediction-

Hospital readmissions of diabetic patients are expensive as hospitals face penalties if their readmission rate is higher than expected and reflects the inadequacies in health care system. Most hospitals can agree that their main goals are centred on improving outcomes, creating more satisfied patients, and better value. For these reasons, it is important for the hospitals to improve focus on reducing readmission rates. Identify the key factors that influence readmission for diabetes and to predict the probability of patient readmission.

factor-analysis-shinyapp

Fisher-Discrimination-on-Iris-data

Fisher discrimination on IRIS data and comparing it with PCA

Fraud-detection

Fraud detection models using unsupervised learning models like LoF, Isolation forest and One class SVM

Prediction-of-Telegana-elections-results-2018-

We would like you to track and analyse the election chatter that happens in twitter, facebook and other social media channels, news sites and portals. Analyse trends and patterns and even predict the outcome of these elections. Choose a problem within the domain of 2018 assembly elections. For example, you may use different data collection methods as needed and collect different opinions from influencers and key opinion leaders on social media and analyse the sentiment of the voters. Or, you may choose to check the veracity of the opinion poll and exit poll data done by popular news channels by applying statistical concepts learnt. Whatever the problem you pick within the bounds of assembly elections, you are expected to leverage data visualization techniques learnt in the class room. Explore the data using visualization and do the first cut analysis and then deeper analysis. Apply text analytics to do various NLP tasks that help you derive election insights from social media and beyond. You can also run “Google Trends” to see the relevant trends on different elections for different time periods. Incorporate the trends in conjunction with the chatter from the media and do text analytics. Even you may do some big data analysis. You are welcome to choose any publicly available dataset of tweets, trends and posts. These questions are to generate curiosity in you.

Service-now-incidents-data

Clustering and categorization of service now incidents.

KVSAkhilesh/Prediction-of-Telegana-elections-results-2018-

We would like you to track and analyse the election chatter that happens in twitter, facebook and other social media channels, news sites and portals. Analyse trends and patterns and even predict the outcome of these elections. Choose a problem within the domain of 2018 assembly elections. For example, you may use different data collection methods as needed and collect different opinions from influencers and key opinion leaders on social media and analyse the sentiment of the voters. Or, you may choose to check the veracity of the opinion poll and exit poll data done by popular news channels by applying statistical concepts learnt. Whatever the problem you pick within the bounds of assembly elections, you are expected to leverage data visualization techniques learnt in the class room. Explore the data using visualization and do the first cut analysis and then deeper analysis. Apply text analytics to do various NLP tasks that help you derive election insights from social media and beyond. You can also run “Google Trends” to see the relevant trends on different elections for different time periods. Incorporate the trends in conjunction with the chatter from the media and do text analytics. Even you may do some big data analysis. You are welcome to choose any publicly available dataset of tweets, trends and posts. These questions are to generate curiosity in you.

KVSAkhilesh/Anomaly-detection-using-Anomalize

Anomaly detection using anomalize package and finding root cause for using Discriminate analysis.

KVSAkhilesh/Binary-Hierarchical-Classifier-

Ensemble of multiple classes using binary Hierarchical classifier using SVM

KVSAkhilesh/Channel_Attribution

KVSAkhilesh/Decathlon-Ariticle-prediction-

Topic modelling on decathlon sport articles using LDA(Latent Dirichlet allocation)

KVSAkhilesh/Diabetes-Readmission-prediction-

Hospital readmissions of diabetic patients are expensive as hospitals face penalties if their readmission rate is higher than expected and reflects the inadequacies in health care system. Most hospitals can agree that their main goals are centred on improving outcomes, creating more satisfied patients, and better value. For these reasons, it is important for the hospitals to improve focus on reducing readmission rates. Identify the key factors that influence readmission for diabetes and to predict the probability of patient readmission.

KVSAkhilesh/factor-analysis-shinyapp

KVSAkhilesh/Fisher-Discrimination-on-Iris-data

Fisher discrimination on IRIS data and comparing it with PCA

KVSAkhilesh/Fraud-detection

Fraud detection models using unsupervised learning models like LoF, Isolation forest and One class SVM

KVSAkhilesh/Service-now-incidents-data

Clustering and categorization of service now incidents.

KVSAkhilesh/K-means-clustering-

KVSAkhilesh/UDpipe

KVSAkhilesh/UDpipe-Nlp-workflow

KVSAkhilesh/UDpipe-ShinnyApp

Shinny APP for UDpipe text analytics

KVSAkhilesh/YouTube-Scrapping-

Scrapping of YouTube search results