Problem statement: Given a directed social graph, have to predict missing links to recommend users (Link Prediction in the graph) Data Overview Taken data from facebook's recruiting challenge on Kaggle: Kaggle Data contains two columns source and destination each edge in the graph - Data columns (total 2 columns): • source_node int64 • destination_node int64 Mapping the problem into supervised learning problem: Generated training samples of good and bad links from the given directed graph and for each link got some features like no. of followers, is he followed back, page rank, Katz score, Adar index, some svd features of adj matrix, some weight features, etc. and trained ML model based on these features to predict link. Some reference papers and videos: • Cornell • lichtenwalter2010 • YouTube Business objectives and constraints: • No low-latency requirement. • The probability of prediction is useful to recommend the highest probability links. The performance metric for supervised learning: • Both precision and recall are important so an F1 score is a good choice • Confusion matrix

This project was done as part of assignment for PGP _AI DataScience using Python. This uses Naive Bayes, CountVectorizer , TF/IDF to predict the sentiment of imdb comments.

This project is done as part of assessment - PGP AI Datascience with Python. This project uses nltk toolkit, CountVectorizer, TF/IDF and Naive Bayes to classify spam and ham messages.

Datascience Toolbox PGP

POST GRADUATE PROGRAM IN DATA SCIENCE in Purdue University

All Machine Learning and AI Coursework performed at University of Texas at Austin

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This repository contains the projects i have done as part of completing PGP in DataScience and business analytics

This repository contains working code and information about Hackathon-01 which was conducted by UpGrad-Insofe while pursuing PGP course on DataScience

This project was done as part of assignment - PGP AI Datascience using Python. This project does Exploratory Data Analysis for the NYC 311 service request calls data. This uses hypothesis testing to determine if average response time is similar for all complaint type. Also, Chi squared test is used to determine if the complaint type and location are related to each other.