Overview

Teaching: 20 min
Exercises: 0 min

Questions

• How to participate in a Kaggle online compeition

Objectives

• Download Kaggle data and apply some algorithm technique that you have learnt to solve the actual data

9. Kaggle online competition: Supervised Learning

This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition.

https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview

Project description:

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.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

For simpilicity: I downloaded the data for you and put it here: https://github.com/vuminhtue/SMU_Data_Science_workflow_R/tree/master/data/Kaggle_house_prices

9.1 Understand the data

There are 4 files in this folder:

• train.csv: the trained data with 1460 rows and 81 columns. The last column “SalePrice” is for output with continuous value
• test.csv: the test data with 1459 rows and 80 columns. Note: There is no “SalePrice” in the last column
• data_description.txt: contains informations on all columns
• sample_submission.csv: is where you save the output from model prediction and upload it to Kaggle for competition

Objective:

• We will use the train.csv__ data to create the actual train/test set and apply several algorithm to find the optimal ML algorithm to work with this data
• Once model built and trained, apply to the test.csv__ and create the output as in format of sample_submission.csv
• Write all analyses in Rmd format.

9.2 Create the Rmd format with following Data Science workflow:

Step 1: Load library, Load data

Step 2: Select variables.

• Since there are 80 input variables, we should not use all of them to avoid collinearity.
• For simplicity, select the following columns: “OverallQual”,”OverallCond”,”YearBuilt”,”X1stFlrSF”,”FullBath”,”GarageCars”,”SaleCondition”,”SalePrice”
• Visualize the importancy of variables

Step 3: Create partition for the data

Step 4: Apply 1 ML algorithm to the data and calculate prediction

Step 5: Evaluate the model output

Step 6: Knit the documentation

! Solution

Key Points

• Kaggle