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Food-Quality-Assessment
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Food-Quality-Assessment
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# Food-Quality-Assessment  ## Problem Description The food inspection department conducts regular inspection on food quality for various restaurants in the city. It’s a very well documented procedure and over time some good amount of data has been generated out of these inspections. The inspection department would like to predict where they should focus most in terms of their next inspection schedule, so that they can most optimize their time at hand to catch the worst offenders. Can the past inspection or any data that they have collected predict which facility will pass or fail. In this hackathon, MachineHack provides you with a subset of this dataset with information on food quality checks conducted on thousands of facilities that serve food across multiple cities. Your objective as a Data Scientist is to predict whether a facility will pass or fail the inspection based on a number of factors. ## Objective Build a predictive model that is capable of predicting the outcome of an inspection conducted in a facility based on the given set of features ## Features ID: A unique id for each inspection Date: The date at which the inspection was done in a particular facility LicenseNo: De-identified license number for a particular facility FacilityID: De-identified unique facility id for a facility FacilityName: The encoded name of a facility Type: The type of the facility being inspected Street: The encoded street where the facility is located City: The encoded city where the facility is located State: The encoded state where the facility is located LocationID: An encoded location feature. Reason: The primary reason for the inspection SectionViolations: Laws violated by the facility RiskLevel: The level of risk the facility possesses to the consumers. Geo_Loc: De-identified geo location of the facility Inspection_Results: The result of the inspection ## Target : The inspection results can have any of the following values - 0:’FACILITY CHANGED’ 1:’FAIL’ 2:’FURTHER INSPECTION REQUIRED’, 3:’INSPECTION OVERRULED’ 4:’PASS’ 5:’PASS(CONDITIONAL)’ 6:’SHUT-DOWN’ ## Data Sets: Data_Train.xlsx – 147443 rows × 15 columns Data_Test.xlsx – 49148 rows × 14 columns Sample_Submission.xlsx – 49148 rows × 7 columns ## Evaluation The leaderboard is evaluated using Multi Class Log loss (Cross-entropy loss) for the participant’s submission. 
