Hinal Pandya

Big-Mart--Sales--Practice-Problem-

August 22, 2017 – August 22, 2017

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

Big-Mart-Sales-Practice-Problem-

August 22, 2017 – August 22, 2017

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

Big-Mart-Sales-Practice-Problem

August 22, 2017 – August 22, 2017

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

Insurance-Case-Study-Regression-Tree-using-CART

August 22, 2017 – August 22, 2017

In the insurance industry, a lot of times an Insurance company would like to assess what kind of claims, in terms of monetary value, their consumers make. This usually helps insurance companies evaluate the premiums being offered and the claims being made by the consumers. Once the insurance company has these details, they would be able calculate the Losses they may incur from each of the consumer. This case study is going to help an insurance company, which is into Motor Insurance, build a statistical model that in turn would help them in assessing their consumer base.

Machine-Learning--Housing-Problem

August 3, 2017 – August 3, 2017

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

Titenic

August 2, 2017 – August 3, 2017

1 Introduction 1.1 Load and Check data 2. Data Mining 2.1. Missing Value Tretment 2.2. Outlier Tretment 3. Freature Engineering 3.1 Pessenger Name 3.2 Identify Family Size 3.3 Introduce new variable 3.4 Creat Dummy Variable. 4. Model 4.1 Split into Train, Validation and Testsets 4.2 Building the Model 4.3 Predict on Validation Dataset 4.4 Confusion Matrix 4.5 ROC 4.6 Prediction 5 .Conclusion 1.Introduction This is my first step at a Kaggle script. I have chosen to work with Titanic dataset . I will do data mining and Freature Engineering to find few independent variable.I will use Logistic Regression to create a model predicting survival on the the Titanic. I am new to Machine Learning and hoping to learn a lot,So, feedback is very welcome. 1.1Load and Check Data #Package library('ggplot2') # visualization library('ggthemes') # visualization ibrary('scales') # visualization library(dplyr) # data manipulation library(glmnet)#model creation library(ROCR) #Roc curve let’s read in and take