Simple linear regression.py

#!/usr/bin/env python
# coding: utf-8

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score


# Loading the data

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df = pd.read_csv('C:/Users/3yearb1/Desktop/ML_datasets/Salary_dataset.csv')


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display(df.head())


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df.info()


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df.describe()


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sns.regplot(x=df['YearsExperience'],y=df['Salary'])
plt.xlabel("Years Of Experience")
plt.ylabel("Salary")
plt.title("Salary vs Experience")
plt.show()


# Preparing X and Y

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X=df['YearsExperience'].values.reshape(-1, 1)
y=df['Salary']


# Splitting X and Y into training and testing sets

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X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)


# Model Building

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model = LinearRegression()


# Model Training

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model.fit(X_train, y_train)


# Testing Model

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y_pred=model.predict(X_test)


# Calculating MSE and Accuracy

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mse =mean_squared_error(y_test,y_pred)
r2=r2_score(y_test,y_pred)
print(f"Mean Squared Error:{mse}")
print(f"R-squared score: {r2}")


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plt.scatter(X_test, y_test, color='blue', label='Actual') 
plt.plot(X_test, y_pred, color='red', linewidth=2, label='Predicted')
plt.xlabel("Years of Experience")
plt.ylabel("Salary")
plt.title("Linear Regression: Salary vs Experience")
plt.legend()
plt.show()


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print(f"intercept: {model.intercept_}")
print(f"Cofficient:{model.coef_[0]}" )