PYTHON-Final-project-

Project Overview:

In this project, I developed a classification-based machine learning model using Python. I worked with a real dataset containing at least 2000 instances and performed data preprocessing to ensure the dataset's suitability for modeling. Additionally, I conducted exploratory data analysis to identify any patterns or trends in the data. I used several classification models, including naive Bayes, KNN, decision tree, logistic regression, and support vector machines, to classify the data based on specific features. The models were then compared based on their predictive accuracy to determine the best model for the dataset. The project is summarized in a report, including details of the dataset, data preprocessing, EDA, and model development, with relevant screenshots and plots.

Dataset overview

Data Source with Valid URL:

https://data.nashville.gov/General-Government/General-Government-Employees-Titles-and-Base-Annua/2hu7-5kjq

Description About Dataset :

This dataset contains 2543 total rows and 5 columns of information about Metro Nashville's general government employees, including their name, job title, department, employment status, and base annual salary. The dataset includes various job titles, ranging from police officers and sergeants to maintenance leaders, program specialists, and family development workers.

• Name : The name column lists the employees' full names.
• Titles : Title column provides their job titles, indicating the specific role they fulfill within their department.
• Department : The department column identifies the department that the employee belongs to, such as police, parks, public library, or juvenile court.
• Employment Status : The employment status column indicates whether the employee is full-time, part-time, or temporary.
• Annual Salaries : The annual salary column lists the base annual salary of each employee, providing an idea of their compensation for their job.

Overall, This dataset provides valuable information about the employees of Metro Nashville's general government and their respective job titles and salaries. It could be useful for conducting analyses on government salaries, workforce diversity, and other related topics.