This project is developed as one of Algoritma Academy Data Analytics Specialization using capstone Projects. The deliverables of this project is a simple analytic dashboard using Flask framework.
The skeleton for this application is separated into 2 different files: app.py and helper.py, in which the first one handles the routing and within helper you would define function to be called in app.
The Auto Insurance Fraud Analysis.ipynb notebook contains all the tasks and explanation which will guide you to complete the given skeleton.
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