Shraddha Kale

Salary Analysis and Prediction using Machine Learning

Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook

• Collected, cleaned, and preprocessed salary dataset by handling missing values, encoding categorical variables, and preparing features for analysis.
• Performed Exploratory Data Analysis (EDA) to identify salary trends and understand the impact of factors such as education, experience, job role, and location.
• Built and evaluated machine learning regression models to predict employee salaries based on relevant features.
• Compared model performance using evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² Score.
• Visualized insights using Matplotlib and Seaborn to communicate patterns and support data-driven decision-making.
• Demonstrated the end-to-end data science workflow, including data preprocessing, feature engineering, model training, evaluation, and prediction.