Source Code Link: https://drive.google.com/drive/folders/1izq7lIUDex8yFgWp5oNKbZX0HTcvNgpa

Dataset Link : https://drive.google.com/file/d/139GGmfrf6QGCXMAwCI6P9Gs0O8Ubh0Mw/view?usp=drive_link

import pandas as pd

dataset=pd.read_csv("<https://raw.githubusercontent.com/RamishaRaniK/dataset/main/Salary_Data.csv>")

dataset

indep=dataset[["YearsExperience"]]

indep

dep=dataset[["Salary"]]

dep

import matplotlib.pyplot as plt

plt.scatter(indep,dep)
plt.xlabel("experience")
plt.ylabel("salary")
plt.show()

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(indep,dep,test_size=0.30,random_state=0)

X_train.shape

len(X_train)

X_train

y_test.shape

from sklearn.linear_model import LinearRegression

regressor=LinearRegression()

regressor.fit(X_train,y_train)

weight=regressor.coef_

weight

bais=regressor.intercept_

print(bais)

y_pred=regressor.predict(X_test)

y_pred

predActual=pd.DataFrame(index=range(0,10))

y_test.index=range(0,9)

y_test

predActual["ActualValue"]=y_test

y_pred_table=pd.DataFrame(y_pred,columns=["Pred"])

y_pred_table

predActual["PredValue"]=y_pred_table

y_pred

predActual

from sklearn.metrics import r2_score
r=r2_score(y_test,y_pred)

r

import pickle
filename="finalModel.sav"
pickle.dump(regressor,open(filename,'wb'))

load_model=pickle.load(open("finalModel.sav",'rb'))

result=load_model.predict([[15]])

result

dir(pd)

# Import libraries

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#data variablecsv valus is stored
data = pd.read_csv('Salary_Data.csv')

data

independent = data[["YearsExperience"]] #Indepedent Variable,years of Experience, datatype float
dependent = data[["Salary"]] # Depedent Variable, Salary, datatype float

plt.scatter(independent,dependent)
plt.xlabel('YearsExperience',fontsize=20)
plt.ylabel('Salary',fontsize=20)
plt.show()

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(independent,dependent, test_size = 1/3, random_state = 0)

y_test

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)#y=W*x1+b0 for this equation we got value for b1 and bo

# Viewing the b1 and bo value
weight=regressor.coef_
print("Weight of the model={}".format(weight))
bais=regressor.intercept_
print("Intercept of the model={}".format(bais))

y_pred=regressor.predict(X_test)

y_pred

from sklearn.metrics import r2_score
r_score=r2_score(y_test,y_pred)

r_score

import pickle
filename = 'finalized_model.sav'
pickle.dump(regressor, open(filename, 'wb'))

loaded_model = pickle.load(open('finalized_model.sav', 'rb'))
result = loaded_model.predict([[15]])
print(result)

prediction_input=int(input("Enter the Prediction input value:"))

Future_Prediction=regressor.predict([[prediction_input]])# change the paramter,play with it.
print("Future_Prediction={}".format(Future_Prediction))

import pickle

load_model=pickle.load(open("finalModel.sav",'rb'))

result=load_model.predict([[0]])

result

Salary Data - Dataset