To get the full 16 points, you will need to complete the following tasks:
- Load and pre-process data (2pts)
- Extract quick summary (3pts)
- Generate the plot (4pts)
- Create your own analysis (4pts)
- Make the flask app run properly (3pts)
You'll find a file called requirements.txt. The file contains dependencies for this project.
Run the following command to create a new conda environment from requirements.txt:
Step 1: Prepare a new "blank" environment then activate it
conda env create -n <ENV_NAME> python=<PYTHON_VERSION>
conda activate <ENV_NAME>
Step 2: Navigate to the folder with your requirements.txt
cd <PATH_TO_REQUIREMENTS>
Step 3: Install the requirements
pip install -r requirements.txt
Now you have successfuly installed all the requirements needed on this project! For your convenience, don't forget to link your new environment to jupyter-notebook by installing the kernel:
pip install ipykernel
python -m ipykernel install --user --name=<ENV_NAME>
The data set is taken from fraud-detection-insurance for autoinsurance fraud detection.
"The autoinsurance data contains information about a fictional auto insurance company that provided insurance for car incident, this data have 1000 sample data that an insurance claim which labeled as fraud claim or non fraud claim.
- 'months_as_customer' : months as policy holder
- 'age' : umur pemegang polis
- 'policy_number' : nomor polis
- 'policy_bind_date' : date when policy made
- 'policy_state' : state where the policy made
- 'policy_deductable': Policy deductable
- 'policy_annual_premium' : Yearly policy premium
- 'umbrella_limit: umbrella limit of the premium
- 'insured_zip': zip code of insured
- 'insured_sex' : insured sex
- 'insured_education_level' : insured education level
- 'insured_occupation' : insured occupation
- 'insured_hobbies' : insured hobbies
- 'insured_relationship' : insured relationship status
- 'incident_date' : reported incident date
- 'incident_type' : reported incident type
- 'collision_type' : reported collision type
- 'incident_severity': reported incident severity
- 'authorities_contacted' : contacted autorities
- 'incident_state' : reported incident state
- 'incident_city' : reported incident city
- 'incident_location' : reported incident location
- 'incident_hour_of_the_day' : reported hour of incident
- 'number_of_vehicles_involved' : number of vehicles involved
- 'property_damage' : number of property damage reported
- 'bodily_injuries' : number of bodily injuries reported
- 'witnesses' : number of witnesses
- 'police_report_available' : police report avaiability
- 'total_claim_amount' : total claim amount
- 'injury_claim' : total injury claim
- 'property_claim' : total property claim
- 'vehicle_claim' : total vehicle claim
- 'auto_make' : vehicle brand
- 'auto_model' : vehicle model
- 'auto_year' : vehicle year made
- 'fraud_reported' : fraud label