amangelbhullar/Machine-learning-Data_Cleaning_Tools-supervised-learning

Data cleaning tools, handling missing data, categorical data, feature scaling

Machine-learning-supervised-learning - python

Data cleaning tools

Data preprocessing

import liberaries and dataset

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

Dataset = pd.read_csv('Data.csv')

x = Dataset.iloc[:,:-1].values

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

Taking care of missing data

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(missing_values = np.nan, strategy='mean')

imputer.fit(x[:,1:3])

x[:,1:3] = imputer.transform(x[:,1:3])

Encoding categorical data (country & purchase status)

Encoding the Independent Variable (country)

from sklearn.compose import ColumnTransformer

from sklearn.preprocessing import OneHotEncoder

ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[0])], remainder='passthrough')

x = np.array(ct.fit_transform(x))

Encoding the Dependent Variable (purchase)

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()

y = le.fit_transform(y)

print(y)

Splitting 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 = 1)

Feature Scaling (leave dummy variables)

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

x_train[:, 3:] = sc.fit_transform(x_train[:, 3:])

x_test[:, 3:] = sc.transform(x_test[:, 3:])