This repository contains a sinple linear regression analysis on tips given in a restaurant business and a multiple linear regression on the percentage of female employment in the labour force. I will briefly summarise the Simple Linear Regression, then then the multiple linear regression.
#Simple linear regression analysis on tips
The dataset was collectd from https://www.kaggle.com/datasets/tasneemabdulrahim/tips-dataset
I used the following libraries for the analysis pandas for data wrangling seaborn for visualizing the relationship OLS and qqplot from Statsmodels was used for model extraction and prediction
The dataset contained 24 rows and 12 columns
from scatter plot it shows there is a positive relationship between the tip and total bill of a customer.
The fitted model shows that an increment by 1 on the amount of tip given will result to an expected increase on the total bill by 4.347714.
#On Multiple Linear Regression Analysis Women inclusion in the workforce has always been a problem in every country whether developed, developing or underdeveloped country. The percentage of female worker is insignificant compared to the male counterparts. Major reasons are such economic factors such as insufficient access to education, religious superstitions and lack of adequate infrastructure are responsible for this gap. With the application of multiple Linear Regression, I discovered the relationship between the predictor(percentage of female workers ) and the response variables (Agriculture, Industry, Services, Wage&Salaried).
The data for this analysis was collected from https://www.kaggle.com/datasets/mdmuhtasimbillah/female-employment-vs-socioeconimic-factors libraries applied pandas seaborn sklearn to implement the Linear regression model
Steps of the Analysis Data exploration and wrangling. Set up the dependent and independent variables. Divide the data into train & test sets Train the algorithm Compare the predicted value to the actual value Evaluate the algorithm
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