How to deal with outliers in Python?

How to deal with outliers in Python?

How to deal with outliers in Python?

This recipe helps you deal with outliers in Python

Recipe Objective

In many dataset we find that there are some values in features which are outliers that means they are very large or small as compared to rest of the data. Some values are also out of the range of the feature, so they are also considered as outliers. Outliers effects our model's efficiency because it influences the model very much.

This data science python source code does the following:
1. Imports pandas and numpy libraries.
2. Creates your own dataframe using pandas.
3.Outliers handling by dropping them.
4. Outliers handling using boolean marking.
5. Outliers handling using Rescalinf of features.

So this is the recipe on how we can deal with outliers in Python

Step 1 - Import the library

import numpy as np import pandas as pd

We have imported numpy and pandas. These two modules will be required.

Step 2 - Creating DataFrame

We have first created an empty dataframe named farm then added features and values to it. We can clearly see that in feature Rooms the value 100 is an outlier. farm = pd.DataFrame() farm['Price'] = [632541, 425618, 356471, 7412512] farm['Rooms'] = [2, 5, 3, 100] farm['Square_Feet'] = [1600, 2850, 1780, 90000] print(farm)

Method 1 - Droping the outliers

There are various ways to deal with outliers and one of them is to droping the outliers by appling some conditions on features. h = farm[farm['Rooms'] < 20] print(h) Here we have applied the condition on feature room that to select only the values which are less than 20.

Method 2 - Marking the Outliers

We can also mark the outliers and will not use that outliers in training the model. Here we are using bool to mark the outlier based on some condition. farm['Outlier'] = np.where(farm['Rooms'] < 20, 0, 1) print(farm)

Method 3 - Rescaling the data

We can not use upper two methods when we have less data points in that case we can not afford to drop or mark the outliers. Here we can rescale the data so that the outliers can be used. farm['Log_Of_Square_Feet'] = [np.log(x) for x in farm['Square_Feet']] print(farm) So the final output of all the methods are

     Price  Rooms  Square_Feet
0   632541      2         1600
1   425618      5         2850
2   356471      3         1780
3  7412512    100        90000

    Price  Rooms  Square_Feet
0  632541      2         1600
1  425618      5         2850
2  356471      3         1780

     Price  Rooms  Square_Feet  Outlier
0   632541      2         1600        0
1   425618      5         2850        0
2   356471      3         1780        0
3  7412512    100        90000        1

     Price  Rooms  Square_Feet  Outlier  Log_Of_Square_Feet
0   632541      2         1600        0            7.377759
1   425618      5         2850        0            7.955074
2   356471      3         1780        0            7.484369
3  7412512    100        90000        1           11.407565

Download Materials

Relevant Projects

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Data Science Project on Wine Quality Prediction in R
In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.