Ml programs
By: Anonymous1/23/202613 views Public Note
ML Program 1
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
X=np.array([1,2,3,4,5]).reshape(-1,1)
Y=np.array([2,4,5,4,5])
model=LinearRegression()
model.fit(X,Y)
slope=model.coef_[0]
intercept=model.intercept_
print(f"Slope:{slope:2f}")
print(f"intercept:{intercept:2f}")
y_pred=model.predict([[6]])
print(f"Prediction for x=6:{y_pred[0]:.2f}")
plt.scatter(X,Y,color="blue",label="Data")
plt.plot(X,model.predict(X),color="red",label="Regression Line")
plt.legend()
plt.show()
2.ml program
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LinearRegression
dataset_path = "employee_salary_dataset.csv"
data = pd.read_csv(dataset_path)
x = data["Experience_Years"].values.reshape(-1, 1)
y = data["Monthly_Salary"].values
model = LinearRegression()
model.fit(x, y)
slope = model.coef_[0]
intercept = model.intercept_
print(f"slope: {slope:.2f}")
print(f"intercept: {intercept:.2f}")
y_pred = model.predict([[8]])
print(f"prediction for x=8 (Experience_Years): {y_pred[0]:.2f}")
plt.scatter(x, y, color="blue", label="Data")
plt.plot(x, model.predict(x), color="red", label="Regression line")
plt.legend()
plt.xlabel("Experience_Years")
plt.ylabel("Monthly_Salary")
plt.show()