Basic Procedure to Write a Code of Machine Learning for Training Models

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
2. Importing Dataset
3. Distribute dataset to test data and Train dataset
4. Feature Scaling(If needed.)
5. Import Machine Learning Model like SVM, Linear Regression, etc for the dataset.
6. Predicting the Test set results
7. Visualizing the Training set results
8. Visualizing the Test set results

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.