Case Study (Salary & Position) :

SKLearrn (Polynomial Linear Regression)

Overview

- Importing the Relevant Libraries

- Loading the Data

- Declaring the Dependent and the Independent variables

- Splitting the dataset into the Training set and Test set

- Simple Linear Regression 

    - Creating a Linear Regression 
    - Fitting The Model
    - Predicting the Results
    - Making a Single Observation Prediction


- Visualising the Simple Linear Regression

- Result

Importing the Relevant Libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Loading the Data

url = "https://DataScienceSchools.github.io/Machine_Learning/Sklearn/Regression/Position_Salaries.csv"

dataset = pd.read_csv(url)

dataset

	Position	Level	Salary
0	Business Analyst	1	45000
1	Junior Consultant	2	50000
2	Senior Consultant	3	60000
3	Manager	4	80000
4	Country Manager	5	110000
5	Region Manager	6	150000
6	Partner	7	200000
7	Senior Partner	8	300000
8	C-level	9	500000
9	CEO	10	1000000

Declaring the Dependent and the Independent variables

- Exclude Position (Index 0)
- Position & Level are the same

X = dataset.iloc[:, 1:-1].values

y = dataset.iloc[:, -1].values

Splitting the Dataset into the Training Set and Test Set

- Dataset is small, so we will not split it into training set & test set

Simple Linear Regression

Creating a Linear Regression

- LinearRegression Class from linear_model Module of sklearn Library

- model -> Object of LinearRegression Class

from sklearn.linear_model import LinearRegression

model = LinearRegression()

Fitting The Model

- fit method -> training the model

model.fit(X, y)

LinearRegression()

Predicting the Results

- y_pred -> the predicted salaries

y_pred = model.predict(X)

Making a Single Observation Prediction

- level: 6.5 

model.predict([[6.5]])

array([330378.78787879])

Visualising the Simple Linear Regression

plt.scatter(X, y, color = 'red')

plt.plot(X, model.predict(X), color = 'blue')

plt.title('Salary vs Position Level (Simple Linear Regression)')
plt.xlabel('Position Level')
plt.ylabel('Salary')

plt.show()

Result:

- Considering the scatter plot:

    - A Simple Linear Regression doesn't fit quite well to the data 

    - Let's try Polynomial Linear Regression