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arrrrrayss

bank-note-project

Tensorflow Project

cvru

cvrubhilai

data-science

Exploratory data analysis of publicly available data from the website LendingClub.com from the year 2007 - 2010 using Seaborn, Matplotlib and Pandas built in visualization libraries. Data visualization resulted in finding some relationships/patterns in the data amongst the categories like FICO score, interest rate, annual income, credit policy, etc. Predicted whether or not the borrower paid back their loan in full using Random Forest algorithm so that investors can invest in people who showed a profile of having a high probability of paying you back.

grailsapp

imbalance_class_sklearn

Address imbalance classes in machine learning projects.

lca-dataset

linkedlistsstacksqueuestreesgraphs

recccccurrrsions

titanic-machine-learning-from-disaster

Explored the dataset by creating visualizations using Seaborn, Matplotlib and Pandas built in visualization libraries and cleaned the data by filling null values with relevant data. Performed analysis using cross tabulation and data segregation for the survivors against various factors such as ticket class, sex of the passenger, marital status, size of the family of the person onboard, name, ticket fare etc. to study the pattern of the survival rate. Concluded that the best fitted model for survival as response variable is based on ticket class, marital status, family size as independent variables.