Salary Prediction via Machine Learning

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