Kapalı

Random Forest and logical regression

Need this to be done in 24 hours

A company has had a growing problem with increasing customer defections above industry average. The churn issue is most acute in the SME division and thus they want it to be the first priority.

Predict the customers which are most likely to churn.

Task:

Find the churn issue

Build the model and suggest actions

Data provided:

Training model – dataset which includes features of SME customers in Jan 2016, as well as the info about whether or not they have churned by Mar 2016.

The prices from 2015 for these customers.

How:

Use the trained model to “score” customers in the verification data set (provided in the file) and put them in descending order of the propensity to churn.

Classify these customers into two classes: those which you predict to churn are to be labelled "1" and the remaining customers should be labelled "0" in the result template.

Use the ROC curve and Brier scoring

Further:

Will a 20% discount offered to customers predict to be a good measure for churn (assuming it’s applied to all customers)?

(Table 1 describes all the data fields which are found in the data. You will notice that the contents of some fields are meaningless text strings. This is due to "hashing" of text fields for data privacy. While their commercial interpretation is lost as a result of the hashing, they may still have predictive power)

Beceriler: Python, İstatistiksel Analiz

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İşveren Hakkında:
( 96 değerlendirme ) Singapore, Singapore

Proje NO: #17733281

Bu iş için 24 freelancer ortalamada $207 teklif veriyor

suyashdhoot

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aki003iitr

My profile is as follows:- 4 years of experience in data science,machine learning and artificial intelligence solving complex problems for Fortune 500 companies - Proficient in R, Python, SQL,Matlab ; Hands on Daha Fazla

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liveexperts123

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davidngo817

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cyash61

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statistician71

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Statxn

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general985

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saikatkumardey

I've built churn prediction models that has been deployed in production for an MNC I used to work with. I can help you do this with perfection. I'll also document all the steps in Jupyter notebook using which you can r Daha Fazla

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Being in the telecom industry for more than 2 years, I have handled such analysis as a part of an everyday job description Questions: 1. Unable to find the data 2. Which industry are you targetting with this analy Daha Fazla

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kingCzar

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karanburied

I work on machine learning and has recently worked on similar case for fraud detection. Multiple machine learning techniques can be used based on whether the churn data is equal for both classes or not. For unbalance Daha Fazla

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