Predictive modeling of insurance claims using regression analysis

Project Report: Analysis of white wine and red wine Datasets   Introduction: The aim of this project is to analyse two datasets containing information about white wine and red wine samples. We will explore various aspects of the datasets, including descriptive statistics, correlation analysis, and hypothesis testing,…

Aim: This project aims to analysis data in detail on vaccinated people and fully vaccinated by year and country. The main objective of this project is to analysis the data on, COVID-19 Vaccinations. Through the Analysis of data, we can find out some important insights. Problem Statement: • We can find out how many people are completely vaccinated. • Total vaccinations by each year and then by country. In this analysis, we can see the number of people vaccinated per hundred.

Predict the accuracy of the regression Model and creating regression Line to predict the charges for insurance using independent variables.

Libraries used * difflib * tkinter

This Project explores the advantages of using power query, macro VBA for reporting, such as improved data accuracy, increased efficiency, and better overview of insights using a Dashboard in Excel. https://docs.google.com/spreadsheets/d/1Pbmb5JLPhl592qjj_87dzWktBs65IPcP?rtpof=true&authuser=prithviraje166@gmail.com&usp=drive_fs

see (literacy rate of tamilnadu)

see dashboard

# 1.) Create new schema as alumniCREATE SCHEMA alumni;#______________________________________________________________________________________________# 2.) Import all .csv files into MySQL -- see screen shot (import .csv in Mysql)#______________________________________________________________________________________________#…

# 3.) Run SQL command to see the structure of table USE ecommerce;DESC users_data;SELECT * FROM ecommerce.users_data;#______________________________________________________________________________________________# 4.) Run SQL command to select first 100 rows of the database SELECT * FROM ecommerce.users_data LIMIT 100;#______________________________________________________________________________________________#…

1. Categorical values: #### GENDER#### VEHICLE AGE#### VEHICLE DAMAGE#### REGIONAL CODE ( UNIQUE)#### Customer_ID ( UNIQUE)#### SALES CHANNEL CODE (UNIQUE)  2. Numerical Values: #### ANNUAL PREMIUM#### VINTAGE#### AGE  3. DUMMIES / Binary values : #### RESPONSE#### previously_insured#### driving_licence_present

please look into the execel sheet from a-j answered as question-1 to question-10