[SOLVED] 代写 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 Netflix’s 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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