shalinsaleem Goto Github PK

allstate

A regression problem for predicting insurance severity

allstate-claims-severity-data

Predictive Analysis on Allstate claims Severity data in Python

allstate-insurance-claim-severity

We aspire to demonstrate insight into better ways to predict claims severity for the chance to be part of Allstate’s efforts to ensure a worry-free customer experience. The goal is to predict the loss based on the severity of the claims

allstate-insurance-predicting-claim-severity-xgboost-ml

This is a solution to a Kaggle competition on predicting claim severity for Allstate Insurance using the Extreme Gradient Boosting (XgBoost) algorithm in R

allstate_capstone

Allstate Kaggle Competition ML Capstone Project

aws-machinelearning-rename-columns-prediction-issue

Demonstration of Amazon Machine Learning issue that produces different model predictions when the name of the columns change in the data.

basemap

coursera-university-of-washington

University of Washington

dash

Interactive, Reactive Web Apps for Python. Dash Is Productive™

datacamp_facebook_live_ny_resolution

In this Facebook live code along session with Hugo Bowne-Anderson, you're going to check out Google trends data of keywords 'diet', 'gym' and 'finance' to see how they vary over time.

datasets

A collection of publicly available datasets

deltaviz

deltaViz dashboard extension for Qlik® Sense

dsg_final

Final of The Data Science Game - (2nd /143 universities) Using XGBoost to classify car insurance quotes

house-price-prediction

Predicting house prices using Linear Regression and GBR

ibmwatson

irisqliksense

Qlik Sense R Example with Iris Data

kaggle-allstate-claims-severity

2nd place solution for Allstate Claims Severity competition at Kaggle

kaggle-house-prices-advanced-regression-techniques

Kaggle: House Prices: Advanced Regression Techniques

kaggle-otto-group

lasso-regression-to-predict-the-school-connectedness-of-a-student

The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. The lasso does this by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward zero. Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. Variables with non-zero regression coefficients variables are most strongly associated with the response variable. Explanatory variables can be either quantitative, categorical or both.

losspredictor

math_of_machine_learning

This is the code for "Mathematcs of Machine Learning" by Siraj Raval on Youtube

matplotlib

matplotlib: plotting with Python

models

Models and examples built with TensorFlow

oasis

openwind

A python package for estimating high resolution wind from SAR images

predicting-allstate-claims-severity

My submission for Kaggle's "Allstate Claims Severity" competition.

predicting-health-insurance-cost

Predicting health insurance cost from Morality data using Machine Learning techniques

predictliverpatientwithrandomforestandlogisticregression

Predict Liver Patient With Random Forest And Logistic Regression. Written in Python (with Scikit, Pandas, Seaborn).

pyglmnet

Python implementation of elastic-net regularized generalized linear models