💼 Salary Prediction Using Linear Regression

This project uses a simple machine learning model to predict salaries based on years of experience. It’s ideal for beginners looking to understand supervised learning and regression techniques.

Project Link

📁 View the Salary Prediction Project on Google Colab

📊 Dataset Overview

File Name: Salary_dataset.csv
Columns:

• YearsExperience: Number of years someone has worked.
• Salary: The salary associated with that experience level (in dollars).

This dataset is clean and minimal, making it great for learning linear regression.

🧠 Project Type

• Category: Supervised Machine Learning
• Algorithm: Linear Regression
• Problem Type: Regression (predicting a continuous value)

We’re using the known input feature YearsExperience to predict a continuous output variable Salary.

🔧 Step-by-Step Procedure

1. 🛠️ Importing Required Libraries

We import essential Python libraries like:

• pandas for data handling
• numpy for numerical operations
• matplotlib and seaborn for data visualization
• sklearn for machine learning models and evaluation

2. 📥 Loading the Dataset

We load the Salary_dataset.csv file into a pandas DataFrame using:

df = pd.read_csv('Salary_dataset.csv')

3. 🧼 Data Inspection and Cleaning

• Use df.info() and df.describe() to explore data types and basic statistics.
• Check for missing values using df.isnull().sum().
• Drop unnecessary columns like 'Unnamed: 0' if present.

4. 👀 Exploratory Data Analysis (EDA)

• Use scatter plots to visualize the relationship between YearsExperience and Salary.
• Use box plots to identify outliers in both features.

5. 📤 Feature Selection

Split the dataset into:

• X: Independent variable (YearsExperience)
• Y: Dependent variable (Salary)

6. 🧪 Splitting the Dataset

Split the data into training and testing sets using:

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25, random_state=101)

Jai hanuman