Salary Prediction using Decision Trees

This repository contains a Jupyter Notebook that demonstrates how to predict whether an employee earns more than $100K based on company, job role, and degree. The classification is performed using Decision Trees from the scikit-learn library.

Dataset

The dataset salaries.csv contains the following columns:

• company - Name of the company
• job - Job role
• degree - Type of degree held
• salary_more_than_100k - Target variable (1 = Yes, 0 = No)

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Installation

To run this project, install the required dependencies using:

pip install pandas scikit-learn

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Usage

Clone the repository and open the Jupyter Notebook to explore the code and experiment with different inputs.

git clone <your-repository-url>
cd <your-repository-folder>
jupyter notebook

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Code Overview

Importing Necessary Libraries

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn import tree

Loading the Dataset

df = pd.read_csv('salaries.csv')
df.head()

Preprocessing the Data

• Dropping the target column (salary_more_than_100k) from input features
• Encoding categorical variables (company, job, degree) into numerical format

inputs = df.drop('salary_more_then_100k', axis='columns')
target = df['salary_more_then_100k']

le_company = LabelEncoder()
le_job = LabelEncoder()
le_degree = LabelEncoder()

inputs['company_n'] = le_company.fit_transform(inputs['company'])
inputs['job_n'] = le_job.fit_transform(inputs['job'])
inputs['degree_n'] = le_degree.fit_transform(inputs['degree'])

inputs_n = inputs.drop(['company', 'job', 'degree'], axis='columns')

Training the Decision Tree Model

model = tree.DecisionTreeClassifier()
model.fit(inputs_n, target)

Making Predictions

Predicting whether an employee with specific attributes earns more than $100K:

# Example: Employee from company 2, job role 2, and degree 1
prediction = model.predict([[2, 2, 1]])
print("Prediction:", prediction)

# Example: Employee from company 2, job role 0, and degree 1
model.predict([[2, 0, 1]])

# Example: Employee from company 2, job role 0, and degree 0
model.predict([[2, 0, 0]])

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Results

• The model predicts whether a person earns more than $100K based on company, job, and degree.
• Categorical data is encoded into numerical format for processing.
• The Decision Tree model is used for classification.

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Contributing

Feel free to fork this repository, create a feature branch, and submit a pull request! 🚀

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License

This project is open-source and available under the MIT License.