Description

Data Science –Machine Learning Projects

Kindly complete all the projects , upload in Github and

 submit the link in MS-word file .

Dataset url

https://github.com/dsrscientist/Data-Science-ML-Capstone-Projects

1    Predict the Automobile Insurance claim

The purpose of an Insurance is to provide protection against the risk of any financial loss. Insurance is a form of risk management in which an insurer agrees to take the risk of the insured entity against future events, uncertain loss due to Tsunami, earthquake or damage against the vehicle or personal property. Here you will be provided with Automobile insurance claim dataset.

One has to predict the claim amount in the Automobile insurance dataset.

https://github.com/dsrscientist/Data-Science-ML-Capstone-Projects/Auto_Insurance_claims_amount.csv

Or

One has to predict the insurance fraud  in the Automobile insurance dataset.

https://github.com/dsrscientist/Data-Science-ML-Capstone-Projects/Automobile_insurance_fraud.csv

2     Predict The Flight Ticket Price 

Flight ticket prices can be something hard to guess, today we might see a price, check out the price of the same flight tomorrow, it will be a different story. We might have often heard travellers saying that flight ticket prices are so unpredictable. Here you will be provided with prices of flight tickets for various airlines between the months of March and June of 2019 and between various cities.

Size of training set: 10683 records

Size of test set: 2671 records

FEATURES:

Airline: The name of the airline.

Date_of_Journey: The date of the journey

Source: The source from which the service begins.

Destination: The destination where the service ends.

Route: The route taken by the flight to reach the destination.

Dep_Time: The time when the journey starts from the source.

Arrival_Time: Time of arrival at the destination.

Duration: Total duration of the flight.

Total_Stops: Total stops between the source and destination.

Additional_Info: Additional information about the flight

Price: The price of the ticket

3    Predict A Doctor's Consultation Fee 

We have all been in situation where we go to a doctor in emergency and find that the consultation fees are too high. As a data scientist we all should do better. What if you have data that records important details about a doctor and you get to build a model to predict the doctor’s consulting fee.? This is the hackathon that lets you do that.

Size of training set: 5961 records

Size of test set: 1987 records

FEATURES:

Qualification: Qualification and degrees held by the doctor

Experience: Experience of the doctor in number of years

Rating: Rating given by patients

Profile: Type of the doctor

Miscellaeous_Info: Extra information about the doctor

Fees: Fees charged by the doctor

Place: Area and the city where the doctor is located.

4    Predicting Restaurant Food Cost 

Who doesn’t love food? All of us must have craving for at least a few favourite food items, we may also have a few places where we like to get them, a restaurant which serves our favourite food the way we want it to be. But there is one factor that will make us reconsider having our favourite food from our favourite restaurant, the cost. Here in this hackathon, you will be predicting the cost of the food served by the restaurants across different cities in India. You will use your Data Science skills to investigate the factors that really affect the cost, and who knows maybe you will even gain some very interesting insights that might help you choose what to eat and from where.

Size of training set: 12,690 records

Size of test set: 4,231 records

Size of training set: 12,690 records

Size of test set: 4,231 records

FEATURES:

TITLE: The feature of the restaurant which can help identify what and for whom it is suitable for.

RESTAURANT_ID: A unique ID for each restaurant.

CUISINES: The variety of cuisines that the restaurant offers.

TIME: The open hours of the restaurant.

CITY: The city in which the restaurant is located.

LOCALITY: The locality of the restaurant.

RATING: The average rating of the restaurant by customers.

VOTES: The overall votes received by the restaurant.

COST: The average cost of a two-person meal.