mario85s / data_analysis

# Simple_Linear_Regression - Insurance Charges Prediction

## Description
This project uses a Linear Regression model to predict insurance charges based on age. The dataset used in this project is insurance.csv,
which contains data about different people’s ages and their corresponding insurance charges.
The project involves data loading, data visualization, model training, prediction, and result visualization.

** Overview of what the code does: **

Data Loading: The code begins by importing necessary libraries and loading the insurance.csv file into a pandas DataFrame.
Data Visualization: A scatter plot is created to visualize the relationship between age and insurance charges.
Model Training: A Linear Regression model is trained using the age as the predictor variable and insurance charges as the target variable.
Prediction: The trained model is then used to make predictions.
Result Visualization: The predictions are plotted against the actual values to visualize the performance of the model.

## Credits
This project was created by Mariusz Sawka

# Pandas_Report

## Description

Pandas_Report is a Python-based project that performs various analyses on a dataset contained in a file named 'balance.txt'.
The analyses include calculating average income by ethnicity, comparing the mean balance of married and single people,
finding the highest and lowest income, summing the recorded cards, and counting the number of females and males in the dataset.
The provided Python code performs various analyses on a dataset contained in a file named 'balance.txt'.
The code uses the pandas library to read the data into a DataFrame and then performs operations such as grouping,
filtering, and aggregating the data to calculate various statistics.
The results of these calculations are then printed to the console.
The code also includes conditionals to compare the mean balances of married and single people and print different messages based on the comparison.
The code ends by counting the number of females and males in the dataset and printing the counts.

## Credits
This project was created by Mariusz Sawka