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https://github.com/alejandro945/insurance-risk
This project aims to predict the risk of insurance claims using a dataset from Kaggle. The dataset consists of 26 columns and 205 rows, providing various features related to insurance risk. By analyzing this data, we seek to build predictive models that can help insurers assess the risk of claims.
https://github.com/alejandro945/insurance-risk
data-analytics ipython-notebook numpy pandas python
Last synced: 27 days ago
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This project aims to predict the risk of insurance claims using a dataset from Kaggle. The dataset consists of 26 columns and 205 rows, providing various features related to insurance risk. By analyzing this data, we seek to build predictive models that can help insurers assess the risk of claims.
- Host: GitHub
- URL: https://github.com/alejandro945/insurance-risk
- Owner: alejandro945
- Created: 2024-08-25T16:21:24.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-15T04:58:52.000Z (about 2 months ago)
- Last Synced: 2024-09-28T23:22:37.308Z (about 1 month ago)
- Topics: data-analytics, ipython-notebook, numpy, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 8.62 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
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README
# 📣 Car Insurance Risk Prediction
## Introduction
This project aims to predict the risk of insurance claims using a car feature data. The dataset consists of 26 columns and 205 rows, providing various features related to insurance risk. By analyzing this data, we seek to build predictive models that can help insurers assess the risk of claims.
## Dataset
The dataset contains the following columns:
- **symboling**: The auot is risky (+3 more risk and -3 is pretty safe). Rating corresponds to the degree to which the auto is more risky than its price indicates.
- **normalized-losses**: The relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc.), and represents the average loss per car per year.
- **make**: The make of the car.
- **fuel-type**: The fuel type of the car.
- **aspiration**: Aspiration used in a car.
- **num-of-doors**: The number of doors in a car.
- **body-style**: The body style of the car.
- **drive-wheels**: The drive wheels of the car.
- **engine-location**: The location of the engine in the car.
- **wheel-base**: The wheel base of the car.
- **length**: The length of the car.
- **width**: The width of the car.
- **height**: The height of the car.
- **curb-weight**: The curb weight of the car.
- **engine-type**: The engine type of the car.
- **num-of-cylinders**: The number of cylinders in the car.
- **engine-size**: The engine size of the car.
- **fuel-system**: The fuel system of the car.
- **bore**: The bore of the car.
- **stroke**: The stroke of the car.
- **compression-ratio**: The compression ratio of the car.
- **horsepower**: The horsepower of the car.
- **peak-rpm**: The peak rpm of the car.
- **city-mpg**: The city mpg of the car.
- **highway-mpg**: The highway mpg of the car.
- **price**: The price of the car.
## Objective
The primary objective of this project is to build predictive models that can help in assessing the risk of insurance claims based on the given dataset.
## Getting Started
To get started with this project, you can clone the repository using the following command:
```bash
git clone [email protected]:alejandro945/insurance-risk.git
```
## Installation
First make sure you have Python installed on your machine. You can download it from the official website: https://www.python.org/downloads/
Next, set up a virtual environment and install the required packages using the following commands:
```bash
python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.txt
```
Desactivate the virtual environment using the following command:
```bash
deactivate
```