Part1 Data Pre-Processing

Machine Learning process

Data Pre-Processing

• Import the data
• Clean the data
• Split into training & test sets.
• Feature Scaling

Modelling

• Build the model
• Train the model
• Make predictions

Evaluation

• Calculate perfomance metrics
• Make a verdict

Feature Scalling Basic

Data Pre-Processing Template

Importing the libraries

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

Import the dataset

dataset = pd.read_csv('data.csv')
## Not include last column
X = dataset.iloc[:, :-1]
## only include last column
y = dataset.iloc[:, -1]

Spliting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

Featur Scaling

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)