Data Science Lifecycle revolves around using various analytical methods to produce insights and followed by applying Machine Learning Techniques, to do predictions from the collected data from various sources, through that we could achieve major and innovative objectives, challenges and value added solutions for certain business problem statements. The entire process involves several steps like data cleaning, preparation, modelling, model evaluation, etc.
We can segregate the Data Science Life Cycle in % wise as below in the pie chart., So that we could understand better way, how each stage is playing important roles to build the model for prediction or classification.
Based on the nature of the Data and its source, there might be a few changes in %, So it is not necessarily to be stick with this pattern. Hope you understand my point.
Once we understand the nature of the feature engineering process of the given dataset. We are able to extract notable information, insights as mentioned earlier. Absolutely that would help us to use the right algorithms to build the perfect model for the given problem statement and to achieve a successful model.
Under Feature Improvements, We are having so many things to discuss, Here I am picking just Scaling since this involves mathematics and statistics.
Before applying Machine Learning algorithms to the dataset, We have to carefully understand the magnitude of all key features, which is applicable for feature selection and finding independent and dependent variables. So we have to, scaling them accordingly to accommodate for the analysis and model preparations, the process of adjusting the magnitude of these features is SCALING or Feature Scaling.
Scaling is an important approach that allows us to limit the wide range of variables in the feature under the certain mathematical approach
StandardScaler: Standardizes a feature by subtracting the mean and then scaling to unit variance. Unit variance means dividing all the values by the standard
deviation. StandardScaler makes the mean of the distribution 0. About 68% of the values will lie between -1 and 1.
MinMaxScaler/Normalization: Will transform each value in the column proportionally within the range [0,1].Use this as the first scaler choice to transform a feature, as it will preserve the shape of the dataset (no distortion).
Scaling Process
Robust Scalar: Robust Scalar is specifically to handle the outliers. Since other scaling methods are not supported effectively. This method removes the median and scales the data according to the QUANTILE RANGE, from defaults to IQR: Interquartile Range. (Range è 25% – 75%)
The IQR is the range – 1st quartile (25th quantile) and the 3rd quartile (75th quantile). We could see the outliers themselves are still present in the transformed data set.
import numpy as np
from sklearn import preprocessing
data1 = np.array([[-100.3],
[27.5],
[0],
[-200.9],
[1000]])
print('Before Scaling\n',data1)
standard_scaler = preprocessing.StandardScaler()
scaled = standard_scaler.fit_transform(data1)
print('\nAfter standard scaler\n',scaled)
Min-Max Scaler import numpy as np from sklearn import preprocessing data1 = np.array([[-100.3], [27.5], [0], [-200.9], [1000]]) print('Before scalingn',data1) minmax_scale = preprocessing.MinMaxScaler(feature_range=(1, 2)) scaled = minmax_scale.fit_transform(data1) print('nAfter Min-Max Scalern',scaled)
Before scaling [[-100.3] [ 27.5] [ 0. ] [-200.9] [1000. ]] After Min-Max Scaler [[1.08377051] [1.19019069] [1.1672912 ] [1. ] [2. ]]
Robust Scaler import numpy as np from sklearn import preprocessing data1 = np.array([[-100.3], [27.5], [0], [-200.9], [1000]]) print('Before Scalingn',data1) robust_scaler = preprocessing.RobustScaler() scaled = robust_scaler.fit_transform(data1) print('n After Robust Scalern',scaled)
Before Scaling [[-100.3] [ 27.5] [ 0. ] [-200.9] [1000. ]] After Robust Scaler [[-0.78482003] [ 0.21517997] [ 0. ] [-1.57198748] [ 7.82472613]]
Feature Engineering itself very vast area, and Feature Improvements, is a subdivision of Feature Engineering and Scaling in a small portion. So try to understand how this topic is very important for Data Scientist and Machine Learning Engineers. Will discuss more in upcoming blogs!
Feature Transformation and Scaling Techniques t...
Feature Scaling: Engineering, Normalization, an...
Feature Scaling Techniques in Python – A...
Introduction to Feature Engineering – Eve...
Step by Step process of Feature Engineering for...
Feature Engineering Techniques to follow in Mac...
Want to Ace Data Science Hackathons? This Featu...
Feature Engineering: How to transform variables...
Getting Started with Feature Engineering
A Comprehensive Guide on Feature Engineering
We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.
Show details
This site uses cookies to ensure that you get the best experience possible. To learn more about how we use cookies, please refer to our Privacy Policy & Cookies Policy.
It is needed for personalizing the website.
Expiry: Session
Type: HTTP
This cookie is used to prevent Cross-site request forgery (often abbreviated as CSRF) attacks of the website
Expiry: Session
Type: HTTPS
Preserves the login/logout state of users across the whole site.
Expiry: Session
Type: HTTPS
Preserves users' states across page requests.
Expiry: Session
Type: HTTPS
Google One-Tap login adds this g_state cookie to set the user status on how they interact with the One-Tap modal.
Expiry: 365 days
Type: HTTP
Used by Microsoft Clarity, to store and track visits across websites.
Expiry: 1 Year
Type: HTTP
Used by Microsoft Clarity, Persists the Clarity User ID and preferences, unique to that site, on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.
Expiry: 1 Year
Type: HTTP
Used by Microsoft Clarity, Connects multiple page views by a user into a single Clarity session recording.
Expiry: 1 Day
Type: HTTP
Collects user data is specifically adapted to the user or device. The user can also be followed outside of the loaded website, creating a picture of the visitor's behavior.
Expiry: 2 Years
Type: HTTP
Use to measure the use of the website for internal analytics
Expiry: 1 Years
Type: HTTP
The cookie is set by embedded Microsoft Clarity scripts. The purpose of this cookie is for heatmap and session recording.
Expiry: 1 Year
Type: HTTP
Collected user data is specifically adapted to the user or device. The user can also be followed outside of the loaded website, creating a picture of the visitor's behavior.
Expiry: 2 Months
Type: HTTP
This cookie is installed by Google Analytics. The cookie is used to store information of how visitors use a website and helps in creating an analytics report of how the website is doing. The data collected includes the number of visitors, the source where they have come from, and the pages visited in an anonymous form.
Expiry: 399 Days
Type: HTTP
Used by Google Analytics, to store and count pageviews.
Expiry: 399 Days
Type: HTTP
Used by Google Analytics to collect data on the number of times a user has visited the website as well as dates for the first and most recent visit.
Expiry: 1 Day
Type: HTTP
Used to send data to Google Analytics about the visitor's device and behavior. Tracks the visitor across devices and marketing channels.
Expiry: Session
Type: PIXEL
cookies ensure that requests within a browsing session are made by the user, and not by other sites.
Expiry: 6 Months
Type: HTTP
use the cookie when customers want to make a referral from their gmail contacts; it helps auth the gmail account.
Expiry: 2 Years
Type: HTTP
This cookie is set by DoubleClick (which is owned by Google) to determine if the website visitor's browser supports cookies.
Expiry: 1 Year
Type: HTTP
this is used to send push notification using webengage.
Expiry: 1 Year
Type: HTTP
used by webenage to track auth of webenagage.
Expiry: Session
Type: HTTP
Linkedin sets this cookie to registers statistical data on users' behavior on the website for internal analytics.
Expiry: 1 Day
Type: HTTP
Use to maintain an anonymous user session by the server.
Expiry: 1 Year
Type: HTTP
Used as part of the LinkedIn Remember Me feature and is set when a user clicks Remember Me on the device to make it easier for him or her to sign in to that device.
Expiry: 1 Year
Type: HTTP
Used to store information about the time a sync with the lms_analytics cookie took place for users in the Designated Countries.
Expiry: 6 Months
Type: HTTP
Used to store information about the time a sync with the AnalyticsSyncHistory cookie took place for users in the Designated Countries.
Expiry: 6 Months
Type: HTTP
Cookie used for Sign-in with Linkedin and/or to allow for the Linkedin follow feature.
Expiry: 6 Months
Type: HTTP
allow for the Linkedin follow feature.
Expiry: 1 Year
Type: HTTP
often used to identify you, including your name, interests, and previous activity.
Expiry: 2 Months
Type: HTTP
Tracks the time that the previous page took to load
Expiry: Session
Type: HTTP
Used to remember a user's language setting to ensure LinkedIn.com displays in the language selected by the user in their settings
Expiry: Session
Type: HTTP
Tracks percent of page viewed
Expiry: Session
Type: HTTP
Indicates the start of a session for Adobe Experience Cloud
Expiry: Session
Type: HTTP
Provides page name value (URL) for use by Adobe Analytics
Expiry: Session
Type: HTTP
Used to retain and fetch time since last visit in Adobe Analytics
Expiry: 6 Months
Type: HTTP
Remembers a user's display preference/theme setting
Expiry: 6 Months
Type: HTTP
Remembers which users have updated their display / theme preferences
Expiry: 6 Months
Type: HTTP
Used by Google Adsense, to store and track conversions.
Expiry: 3 Months
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
Save certain preferences, for example the number of search results per page or activation of the SafeSearch Filter. Adjusts the ads that appear in Google Search.
Expiry: 2 Years
Type: HTTP
These cookies are used for the purpose of targeted advertising.
Expiry: 6 Hours
Type: HTTP
These cookies are used for the purpose of targeted advertising.
Expiry: 1 Month
Type: HTTP
These cookies are used to gather website statistics, and track conversion rates.
Expiry: 1 Month
Type: HTTP
Aggregate analysis of website visitors
Expiry: 6 Months
Type: HTTP
This cookie is set by Facebook to deliver advertisements when they are on Facebook or a digital platform powered by Facebook advertising after visiting this website.
Expiry: 4 Months
Type: HTTP
Contains a unique browser and user ID, used for targeted advertising.
Expiry: 2 Months
Type: HTTP
Used by LinkedIn to track the use of embedded services.
Expiry: 1 Year
Type: HTTP
Used by LinkedIn for tracking the use of embedded services.
Expiry: 1 Day
Type: HTTP
Used by LinkedIn to track the use of embedded services.
Expiry: 6 Months
Type: HTTP
Use these cookies to assign a unique ID when users visit a website.
Expiry: 6 Months
Type: HTTP
These cookies are set by LinkedIn for advertising purposes, including: tracking visitors so that more relevant ads can be presented, allowing users to use the 'Apply with LinkedIn' or the 'Sign-in with LinkedIn' functions, collecting information about how visitors use the site, etc.
Expiry: 6 Months
Type: HTTP
Used to make a probabilistic match of a user's identity outside the Designated Countries
Expiry: 90 Days
Type: HTTP
Used to collect information for analytics purposes.
Expiry: 1 year
Type: HTTP
Used to store session ID for a users session to ensure that clicks from adverts on the Bing search engine are verified for reporting purposes and for personalisation
Expiry: 1 Day
Type: HTTP
Cookie declaration last updated on 24/03/2023 by Analytics Vidhya.
Cookies are small text files that can be used by websites to make a user's experience more efficient. The law states that we can store cookies on your device if they are strictly necessary for the operation of this site. For all other types of cookies, we need your permission. This site uses different types of cookies. Some cookies are placed by third-party services that appear on our pages. Learn more about who we are, how you can contact us, and how we process personal data in our Privacy Policy.
Edit
Resend OTP
Resend OTP in 45s
Superb .. great effort
Well presented,easy to understand,overall very good material. Worth reading.
Machine Learning enables the user to customize the products and make the life easier.
Excellent information, good
Good Information and Nice Presentation Shanthababu !!!
Hi Shanthababu, Your blog is Informative, good images, and a nice presentation. Keep it up !!!