denjev / car_insurance_sale_prediction

Predicting Sales of a customer

Outline of Project

The objective of this repository is to predict whether there will be a sale (Yes/No) of car insurance using the data set. There are 10 attributes in the dataset including: Veh_Mileage, Credit_Score, Licesnse_Length, Veh_Value, Price, Age, Marital_Status, Tax, Date. There are 50,000 entries.

Description of the file structure

• The code is all contained in the main directory
• main.py is where the main program is executed and calls methods from the DataModeler class.
• Class_Data_Loader.py contains the class DataLoader that loads in the data from a CSV file and stores it as an object
• Class_Data_Exploration.py contains the class DataExploror that inherits DataLoader. It's purpose is to print and plot various properties of the data so that it can be analysed
• Class_Data_Preprocessor contains the class DataPreprocessor that preprocesses the data using various methods such as normalising and one-hot encoding
• Class_Data_Modeler contains Data_Modeler which models the data and performs cross validation
• Data_In contains DS_Assessment.csv which is the data set that is analysed
• Data_Out contains all various plots that the code produces saved as a pdf file

This was done by using inheritance where DataModeler -> DataPreprocessor -> DataExploror -> DataLoader and the arrow is a symbol indicating 'inherited from'.

Packages Required:

Environment set up in python 3.5 and runs with following packages:

• numpy
• pandas
• matplotlib
• seaborn
• sklearn
• scipy

Example of the code

main.py demonstrates how the various methods can be called to be used correctly. It also demonstrates some of my thought processes when analysing and pre-processing the data with comments.

How to run the code

Ensure all the packages are installed, set the working directory to Car_Insurance_Sales_Prediction and run main.py. If there are any problems please email me at: [email protected].