First of all, get into an Environment of Anaconda weather Spyder, Jupyter Notebook and for Business Analytics, it can be Orange.
To head-start with this, first:
  1. Import Libraries
These are the main eight Steps to test — train the dataset and get your model Train.
For simplicity, Here is the code for Logistic Regression:
# Simple Linear Regression
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
mydataset = pd.read_csv(‘salary.csv’)
X = mydataset.iloc[:, :-1].values
y = mydataset.iloc[:,:].values
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)
# Fitting Simple Linear Regression to the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
# Predicting the Test set results
y_pred = regressor.predict(X_test)
# Visualising the Training set results
plt.scatter(X_train, y_train, color = ‘red’)
plt.plot(X_train, regressor.predict(X_train), color = ‘blue’)
plt.title(‘Salary vs Experience (Training set)’)
plt.xlabel(‘Years of Experience’)
plt.ylabel(‘Salary’)
plt.show()
# Visualising the Test set results
plt.scatter(X_test, y_test, color = ‘red’)
plt.plot(X_train, regressor.predict(X_train), color = ‘blue’)
plt.title(‘Salary vs Experience (Test set)’)
plt.xlabel(‘Years of Experience’)
plt.ylabel(‘Salary’)
plt.show()
The given code is for Salary Dataset for Simple Linear Regression.





My Introduction

Arpit Bhushan Sharma
B.Tech Electrical Engineering (2016 - 2020)
KIET Group of Institution, Ghaziabad
Dr. APJ Abdul Kalam Azad Technical University, Lucknow

Former CEO,
KIET Makers Fab Lab
Innovate Craft, KIET Group of Institutions

Student Teaching Assistant (2019 - 2020)
KIET Makers Fab Lab
Innovate Craft, KIET Group of Institutions

Student Teaching Assistant (2019 - 2020)
Department of Electrical Engineering (2019 - 2020)
KIET Group of Institution, Ghaziabad
Dr. APJ Abdul Kalam Azad Technical University, Lucknow

Industrial Trainee (5 June 2019 - 5 July 2019)
KIET Makers Fab Lab
Innovate Craft, KIET Group of Institutions

Contact no. +918445726929
Whatsapp: +918445726929
email: bhushansharmaarpit@gmail.com
           arpit3043@gmail.com
           arpitisalwaysdere@gmail.com
           arpit.1621041@kiet.edu
Location: Ghaziabad - Meerut Highway, NH-58, Ghaziabad, Uttar Pradesh 201206, India

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