💼 Salary Prediction Classification

A Machine Learning project that predicts whether a person's salary belongs to a particular salary category based on demographic and employment-related features. The project includes data preprocessing, exploratory data analysis (EDA), feature engineering, model training, evaluation, and comparison of multiple classification algorithms.

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📌 Project Overview

The objective of this project is to classify individuals into salary categories using machine learning techniques. The dataset is cleaned, analyzed, and transformed before training different classification models to identify the best-performing model.

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🎯 Objectives

• Perform data cleaning and preprocessing.
• Conduct Exploratory Data Analysis (EDA).
• Encode categorical variables.
• Train multiple classification models.
• Compare model performance using evaluation metrics.
• Select the best model for salary prediction.

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📂 Dataset

The dataset contains demographic and employment-related information such as:

• Age
• Workclass
• Education
• Marital Status
• Occupation
• Relationship
• Race
• Gender
• Capital Gain
• Capital Loss
• Hours per Week
• Native Country
• Salary (Target Variable)

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Google Colab / Jupyter Notebook

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📊 Machine Learning Workflow

1. Data Loading
2. Data Cleaning
3. Exploratory Data Analysis (EDA)
4. Feature Encoding
5. Train-Test Split
6. Feature Scaling (if required)
7. Model Training
8. Model Evaluation
9. Performance Comparison

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🤖 Models Used

• Logistic Regression
• Decision Tree Classifier
• Random Forest Classifier
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)
• Naive Bayes (if implemented)

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📈 Evaluation Metrics

The models are evaluated using:

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix
• Classification Report

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📁 Project Structure

Salary-Prediction-Classification/
│
├── Salary_Prediction_Classification.ipynb
├── README.md
└── dataset.csv

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🚀 How to Run

1. Clone the repository

git clone https://github.com/Ronak-si/Salary-Prediction-Classification.git

2. Install the required libraries

pip install pandas numpy matplotlib seaborn scikit-learn

3. Open the notebook

jupyter notebook Salary_Prediction_Classification.ipynb

or run it directly on Google Colab.

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📌 Results

• Cleaned and preprocessed the dataset.
• Trained and compared multiple classification algorithms.
• Evaluated model performance using standard classification metrics.
• Identified the best-performing model for salary classification.

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🔮 Future Improvements

• Hyperparameter tuning using GridSearchCV.
• Cross-validation for better model reliability.
• Model deployment using Flask or Streamlit.
• Feature importance visualization.
• Interactive prediction dashboard.

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👨‍💻 Author

Ronak Singh

• GitHub: https://github.com/Ronak-si
• LinkedIn: https://www.linkedin.com/in/ronak-singh-55a886320

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