# does money buy happiness

import pandas as pd
import numpy as np

This is Where I got my data:

https://www.kaggle.com/datasets/jahaidulislam/world-happiness-report-2005-2021

df = pd.read_csv('World Happiness Report 2005-2021.csv')
df.head()

	Country name	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Positive affect	Negative affect	Confidence in national government
0	Afghanistan	2008	3.723590	7.302574	0.450662	50.500000	0.718114	0.173169	0.881686	0.414297	0.258195	0.612072
1	Afghanistan	2009	4.401778	7.472446	0.552308	50.799999	0.678896	0.195469	0.850035	0.481421	0.237092	0.611545
2	Afghanistan	2010	4.758381	7.579183	0.539075	51.099998	0.600127	0.125859	0.706766	0.516907	0.275324	0.299357
3	Afghanistan	2011	3.831719	7.552006	0.521104	51.400002	0.495901	0.167723	0.731109	0.479835	0.267175	0.307386
4	Afghanistan	2012	3.782938	7.637953	0.520637	51.700001	0.530935	0.241247	0.775620	0.613513	0.267919	0.435440

(1%) Describes and motivates a real-world problem where data science may provide helpful insights. Your description should be easily understood by a casual reader and include citations to motivating sources or relevant information (e.g. news articles, further reading links … Wikipedia makes for a poor reference but the links it cites are usually promising).

(1%) Explicitly load and show your dataset. Provide a data dictionary which explains the meaning of each feature present. Demonstrate that this data is sufficient to make progress on your real-world problem described above.

(1%) Write one or two sentences about how the data will be used to solve the problem. Earlier in the semester, we won’t have studied the Machine Learning methods just yet but you should have a general idea of what the ML will set out to do. For example:

“We’ll cluster the movies into sets of movies which are often watched by the same users. Doing so allows us to discover if there is a more natural grouping of movies rather than the traditional genres: horror, comedy, romantic-comedy, etc”.

1. My project proposal:

Does money buy happiness. Is there a direct correlation between money and happiness? Are there other factors that should be considered to determine happiness.

2. Data Dictionary

Year: year which the data was collected

Life Ladder: (happiness) With the best possible life for them being a 10 and the worst possible life being a 0.

Log GFP per capita: The gross domestic product per capita in purchasing power parity (PPP) adjusted dollars

Social support: the perceived social support (ability to count on others) in the country Freedom to make life choices: the perceived

Healthy life exptency at birth: Healthy life expectancy at birth

Freedom to make life choices: Freedom to make life choices

Generosity: The perceived generosity of people in the country

Perceptions of corruption: The perceived level of corruption in the government and business sectors of the country

Positive affect: Positive affect

Negative affect: Negative affect

Confidence in nation government: The percieved level of confidence in the nation's government

3. How data will be used:

The data will first be grouped by the 'country name'. Doing so will allow is to see the progression of happiness throughout the years while also comparing the happiness of each country. We will then analyze and compare the data with the desired datatypes to determine if money is correlated to happiness.