Linear Regression Using Python

 

Experiment 1: Linear Regression Using Python

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🎯 Aim

To implement the Linear Regression algorithm using Python and predict the value of a dependent variable based on an independent variable.

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📖 Theory

Linear Regression is one of the simplest Supervised Machine Learning algorithms. It is used to predict continuous numerical values by finding a best-fit straight line between the input (independent variable) and the output (dependent variable).

It assumes a linear relationship between the variables.

Mathematical Equation

$$Y = mX + c$$

Where:

• Y = Predicted Output (Dependent Variable)
• X = Input (Independent Variable)
• m = Slope of the Line
• c = Intercept

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🌍 Real-Life Example

A company wants to predict an employee's salary based on their years of experience.

Experience (Years)	Salary (₹)
1	25,000
2	30,000
3	35,000
4	45,000
5	50,000
6	60,000
7	65,000
8	70,000

Now, we want to predict the salary of an employee with 9 years of experience.

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🪜 Step-by-Step Algorithm

Step 1️⃣ Import Required Libraries

Import the necessary libraries.

import pandas as pd
from sklearn.linear_model import LinearRegression

Explanation

• pandas → Used to create and manage datasets.
• LinearRegression → Imports the Linear Regression model from scikit-learn.

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Step 2️⃣ Create the Dataset

data = {
    "Experience": [1,2,3,4,5,6,7,8],
    "Salary": [25000,30000,35000,45000,50000,60000,65000,70000]
}

df = pd.DataFrame(data)

Explanation

We create a simple dataset using a Python dictionary.

The dataset has two columns:

• Experience → Independent Variable (X)
• Salary → Dependent Variable (Y)

The data is converted into a DataFrame for easy processing.

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Step 3️⃣ Display the Dataset

print(df)

Output

   Experience  Salary
0           1   25000
1           2   30000
2           3   35000
3           4   45000
4           5   50000
5           6   60000
6           7   65000
7           8   70000

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Step 4️⃣ Separate Input and Output Variables

X = df[["Experience"]]
y = df["Salary"]

Explanation

Machine Learning models require:

• X → Input Features (Independent Variable)
• y → Target Variable (Dependent Variable)

Here:

X = Experience

y = Salary

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Step 5️⃣ Create the Linear Regression Model

model = LinearRegression()

Explanation

This creates an empty Linear Regression model.

At this stage, the model has not learned from the data.

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Step 6️⃣ Train the Model

model.fit(X, y)

Explanation

The fit() function trains the model.

During training:

• Reads all training data
• Calculates the best-fit line
• Finds the slope (m)
• Finds the intercept (c)

The model is now ready for prediction.

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Step 7️⃣ Predict Salary

experience = [[9]]

prediction = model.predict(experience)

Explanation

We ask the model:

"Predict the salary of an employee with 9 years of experience."

The predict() function uses the learned line to estimate the salary.

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Step 8️⃣ Display the Prediction

print("Predicted Salary =", prediction[0])

Sample Output

Predicted Salary = 78809.52

(The exact value may vary slightly depending on the fitted line.)

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💻 Complete Python Program

# Step 1: Import Libraries
import pandas as pd
from sklearn.linear_model import LinearRegression

# Step 2: Create Dataset
data = {
    "Experience": [1,2,3,4,5,6,7,8],
    "Salary": [25000,30000,35000,45000,50000,60000,65000,70000]
}

df = pd.DataFrame(data)

# Step 3: Display Dataset
print("Dataset:")
print(df)

# Step 4: Separate Input and Output
X = df[["Experience"]]
y = df["Salary"]

# Step 5: Create Model
model = LinearRegression()

# Step 6: Train Model
model.fit(X, y)

# Step 7: Predict Salary
experience = [[9]]
prediction = model.predict(experience)

# Step 8: Display Result
print("\nPredicted Salary for 9 years experience = ₹", round(prediction[0],2))

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🔄 Workflow

Start
   │
   ▼
Import Libraries
   │
   ▼
Create Dataset
   │
   ▼
Display Dataset
   │
   ▼
Separate X and y
   │
   ▼
Create Linear Regression Model
   │
   ▼
Train Model using fit()
   │
   ▼
Predict using predict()
   │
   ▼
Display Prediction
   │
   ▼
End

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📌 Explanation of Important Functions

Function	Purpose
pd.DataFrame()	Creates a table from data
LinearRegression()	Creates the regression model
fit(X, y)	Trains the model using the dataset
predict()	Predicts the output for new input

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✅ Advantages

• Simple and easy to implement
• Fast training and prediction
• Easy to interpret results
• Works well for linear relationships

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❌ Limitations

• Only models linear relationships
• Sensitive to outliers
• Performance decreases if data is non-linear

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🌍 Applications

• Salary Prediction
• House Price Prediction
• Sales Forecasting
• Stock Trend Analysis
• Weather Forecasting
• Business Revenue Prediction

⭐ Memory Trick

Import Libraries
        ↓
Create Dataset
        ↓
Separate X and y
        ↓
Create Model
        ↓
Train using fit()
        ↓
Predict using predict()
        ↓
Display Result

Easy Formula to Remember:
Import → Data → X & y → Model → Fit → Predict → Output

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🎓 Viva Questions

1. What is Linear Regression?
2. Why is it called a supervised learning algorithm?
3. What are the independent and dependent variables?
4. What is the purpose of fit()?
5. What is the purpose of predict()?
6. What is the equation of a regression line?
7. What is the role of X and y?
8. Give two real-life applications of Linear Regression.