[SOLVED] algorithm KAGGLE LAUNCH Applied Analytics: Frameworks and Methods 1

KAGGLE LAUNCH Applied Analytics: Frameworks and Methods 1
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History: $1 Million Prize
Make an improvement of 10% over Netflixs Cinematch recommendation algorithm
Cinematch rmse: .9514
To Win rmse: .8572 &
Read more on Wikipedia
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Why Do People Kaggle
Sport and Bragging rights
Get a Job with competition sponsor
Showcase skills to recruiters
Prize Money
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Why are you going to Kaggle?
Bragging rights
Experience with building models Points
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ABOUT THE COMPETITION
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About the Competition Description
People interested in renting an apartment or home share information about themselves and their property on Airbnb. Those who end up renting the property share their experiences through reviews. The dataset contains information on 90 variables related to the property, host, and reviews for over 35,000 Airbnb rentals in New York.
Goal
Construct a model using the dataset supplied and use it to predict the price of a set
of Airbnb rentals included in scoringData.csv.
Metric
Submissions will be evaluated based on RMSE (root mean squared error). Lower the RMSE, better the model.
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Deliverables
Submit Predictions on Kaggle site
Kaggle Presentation
Kaggle Report
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Grading Criteria
Commitment to the Project (20 points)
Worked consistently on the Project. Prediction Accuracy (100 points)
Accuracy of predictions at the end of the Project. Quality of Modeling (30 points)
Demonstrated adequate knowledge of data exploration, suitably prepared data for analysis, used a variety of predictive analysis techniques and communicated results effectively.
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GETTING STARTED
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Registration
Registration opens on October 14th
To register for the Kaggle Competition, click here and following directions.
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First Submission
Download data from Kaggle
Read Data
Construct Model
Read scoring Data and apply model to generate predictions
Construct submission from predictions
Upload to Kaggle
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First Submission Code
# For the following code to work, ensure analysisData.csv and scoringData.csv are in your working directory.
# Read data and construct a simple model
data = read.csv(analysisData.csv)
model = lm(price~minimum_nights+review_scores_rating,data)
# Read scoring data and apply model to generate predictions
scoringData = read.csv(scoringData.csv) pred = predict(model,newdata=scoringData)
# Construct submission from predictions
submissionFile = data.frame(id = scoringData$id, price = pred) write.csv(submissionFile, sample_submission.csv,row.names = F)
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Kaggle Timeline
October 14th: Kaggle Registration Opens
November 1st: Deadline for entering first submission
November 19th: Kaggle Competition Closes
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Good Luck
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