himnsuk / habitability-score-ml

Identify the habitability score of a property

Identify the habitability score of a property

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Problem Statement

Finding the correct property to live in is a crucial task while moving to a new city/location. An inappropriate property can make our life miserable. Can AI help us find better places?

Task

You have given a relevant dataset about various properties in the USA. Your task is to identify the habitability score of the property.   

Dataset description

The dataset contains the following files: 

• train.csv: 39499 x 15
• test.csv: 10500 x 14
• sample_submission.csv: 5 x 2 

 The columns provided in the dataset are as follows:

Column	Description
Property_ID	Represents a unique identification of a property
Property_Type	Represents the type of the property( Apartment, Bungalow, etc)
Property_Area	Represents the area of the property in square feets
Number_of_Windows	Represents the number of windows available in the property
Number_of_Doors	Represents the number of doors available in the property
Furnishing	Represents the furnishing type ( Fully Furnished, Semi Furnished, or Unfurnished )
Frequency_of_Powercuts	Represents the average number of power cuts per week
Power_Backup	Represents the availability of power backup
Water_Supply	Represents the availability of water supply ( All time, Once in a day - Morning, Once in a day - Evening, and Once in two days)
Traffic_Density_Score	Represents the density of traffic on a scale of  1 to  10
Crime_Rate	Represents the crime rate in the neighborhood ( Well below average, Slightly below average, Slightly above average, and  Well above average )
Dust_and_Noise	Represents the quantity of dust and noise in the neighborhood ( High, Medium, Low )
Air_Quality_Index	Represents the Air Quality Index of the neighborhood
Neighborhood_Review	Represents the average ratings given to the neighborhood by the people
Habitability_score	Represents the habitability score of the property

Evaluation metric

score = max( 0, 100*(metrics.r2_score(actual , predicted))

Result submission guidelines

• The index is "Property_ID" and the target is the "Habitability_score" column. 
• The submission file must be submitted in .csv format only.
• The size of this submission file must be 10500 x 2.

Note: Ensure that your submission file contains the following:

• Correct index values as per the test.csv file
• Correct names of columns as provided in the sample_submission.csv file

Instructions: 

• Click Download dataset to download the dataset.
• Solve the problem in your local environment.
• Save the submission in a .csv file.
• Click Upload File (under the Upload File section) to upload your prediction file (.csv).
• Add any instructions or comments in the Your Answer section.
• Click Submit.

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Upload Prediction File

Please upload the prediction file in the format as stated in the problem.

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