Challenge 3

Tehtävä

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The last task we have prepared for you is the implementation of feature engineering. You will be working with the 'sales_data.csv' dataset, and your task will be to create new variables and process categorical and numeric data.

Use feature engineering to create new columns such as year, month, and day of the week Date
Encode the 'Region' and 'Product; categorical columns with the ohe-hot encoding method
For numeric data ('Sales'), you will need to scale the data

Ratkaisu

from sklearn.preprocessing import StandardScaler, MinMaxScaler

import numpy as np

import pandas as pd

# Read the dataset

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/sales_data.csv')

# Convert date column to datetime format

df['Date'] = pd.to_datetime(df['Date'])

# Create new columns based on date column

# 'Year'

# 'Month'

# 'Weekend' - if the day is a weekend, then the value is 1, if not a weekend, then 0

# For 'Weekend' column use .map() function

df['Year'] = df['Date'].dt.year

df['Month'] = df['Date'].dt.month

df['Weekend'] = df['Date'].dt.day_name()

df['Weekend'] = df['Weekend'].map({'Monday': 0, 'Tuesday': 0, 'Wednesday': 0, 'Thursday': 0,

'Friday': 0, 'Saturday': 1, 'Sunday': 1,})

# Drop original date column

df = df.drop('Date', axis=1)

# Create MinMaxScaler

scaler = MinMaxScaler()

# Make one-hot encoding with pd.get_dummies()

one_hot = pd.get_dummies(df[['Region', 'Product']])

# Join the encoded columns

df = df.join(one_hot)

# Drop initial columns

df = df.drop(['Region', 'Product'], axis=1)

# Fit and transform numerical data for the scaler

df[['Sales']] = scaler.fit_transform(df[['Sales']])

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Osio 6. Luku 3