sherylphilip Goto Github PK

course-2--data-visualization

This repository contains one of the pre-requisite notebooks for my internship as a Data Analyst at Technocolabs. It includes some of the micro-courses from Kaggle.

course-3---data-manipulation

This repository contains one of the pre-requisite notebooks for my internship as a Data Analyst at Technocolabs. It includes some of the micro-courses from Kaggle.

course-4---machine-learning---intro-and-intermediate

This repository contains one of the pre-requisite notebooks for my internship as a Data Analyst at Technocolabs. It includes some of the micro-courses from kaggle.

course-5---machine-learning-explainability

This repository contains one of the pre-requisite notebooks for my internship as a Data Analyst at Technocolabs. It includes some of the micro-courses from kaggle.

course-6--feature-engineering

This repository contains one of the pre-requisite notebooks for my internship as a Data Analyst at Technocolabs. It includes some of the micro-courses from Kaggle.

couse-1---data-cleaning

This repository contains one of the pre-requisite notebooks for my internship as a Data Analyst at Technocolabs. It includes some of the micro-courses from Kaggle.

loan_defaulter_prediction

An individual applies for an loan from a bank so their financial record goes through a thorough check to guarantee that whether they are sufficiently proficient to take care of the advance (in this industry it is alluded to as credit-value). The backers have a bunch of model/s and rule/s set up which take data in regards to their present monetary standing, past financial record and some different factors as info and yield a metric which gives a proportion of the danger that the guarantor will possibly assume giving the advance. The action is for the most part as a likelihood and is the danger that the individual will default on their advance (called the likelihood of default) later on. In view of the measure of hazard that the backer will take (in addition to some different variables) they settle on a cutoff of that score and use it to take a choice in regards to if to pass the credit. This is a method of overseeing credit hazard. The entire interaction by and large is alluded to as endorsing.

optimizing-lending-club-financial-risk

sherylphilip

Config files for my GitHub profile.

the-sparks-foundation-internship

The Spark Foundation Data Science and Business Analytics internship tasks repository. Task 1 : StudentScoresPrediction To predict the score of a student based on the number of hours studied Used Linear regression model to univariate regression of independent variable Hours to predict the dependable variable Scores and further used this regression model to predict the score of a student who studies for 9.25 hrs/ day. The model has been evaluated with Goodness of Fitness - R2, MSE to evaluate the model.