Salary Prediction via Machine Learning

About the project

Salary Prediction Using Regression Project Overview This project focuses on predicting salary based on experience using Machine Learning regression techniques. It walks through the complete ML pipeline — from data collection to model evaluation — using Python. The goal is to understand how different regression models perform and how well they can predict salaries from real-world data. Concepts Covered Data Collection Data Exploration Exploratory Data Analysis (EDA) Data Cleaning Linear Regression Polynomial Regression Model Training Model Evaluation Dataset Dataset Name: salary_prediction_data.csv Features: Experience (Independent Variable) Salary (Dependent Variable) Technologies & Libraries Used Python Pandas – data manipulation NumPy – numerical operations Matplotlib & Seaborn – data visualization Scikit-learn – model building & evaluation Project Workflow Data Collection The dataset is loaded using Pandas and basic inspection is done. Data Exploration Checking dataset shape Viewing statistical summary Understanding feature relationships Exploratory Data Analysis (EDA) Data visualization Relationship between experience and salary Detecting patterns and trends Data Cleaning Handling missing values Preparing data for modeling Regression Models Linear Regression Polynomial Regression Model Evaluation Performance comparison Accuracy and prediction analysis Results Linear Regression provides a simple baseline model Polynomial Regression captures non-linear relationships better Model performance is evaluated and compared visually and numerically

Skills required