"Don't forget Quantum Computer's and Computing is the fuel of AI.. Speed and accelerated performance of AI will only come from Quantum.. And we are near the era of Quantum Computers, just 3 years more we need to wait.."
Vishal Kapur's Discussions
Feature Selection >> On Labelled Data..
Started this discussion. Last reply by Piyush Agarwal Jan 29. 4 Replies 0 Likes
I am working on a data set that has 10 features, and my label ed output is the 'Weight of humans'.I want to find, out of 10 features, which are he 2 or 3 features due to which the 'Weight' varies..…Continue
Missing Value in DATA - Approaches
Started this discussion. Last reply by Vishal Kapur Nov 4, 2018. 2 Replies 0 Likes
Hello Guys,I am new to ML and I am starting with Exploratory Data Analysis, need help on good material on graphs, statistical methods to use when we first see data, different approach to fill missing…Continue
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"Yes, each country should have a AI Education Roadmap and technical universities should for AI courses.. Like in India we have NPTEL, that gives training on AI and ML.."
"Have you tried looking at a correlation in the variables? Maybe you could use a scatter plot matrix or a correlation matrix to visualize your data and see what variables are impacting your label output.
Check out this article on visualizing your…"
"Thanks the above links are helpful, but here my case is a regression one, but here i don't want to predict anything, i want to know features that are impacting my label ed output."
"Depends on what algorithm you are using. Assuming this is a classification problem and you are using RandomForestClassifier from sklearn, you can simply use it feature_importances_ method to look at the a sorted list of the features and…"
"Hi, I'm a new student in data science, I'm doing the process the following way.
from sklearn.feature_selection import RFE from sklearn.linear_model import LogisticRegression
modelo = LogisticRegression()
rfe = RFE(modelo, 8) fit =…"
Feature Selection >> On Labelled Data..
I am working on a data set that has 10 features, and my label ed output is the 'Weight of humans'.I want to find, out of 10 features, which are he 2 or 3 features due to which the 'Weight' varies.. What are these 2-3 features on which the weight varies...Please let me know, how i can find it out, using any ML technique or algorithm..See More
"I liked it.. Can you please share more such articles and links on missing data and imputation techniques..."
Missing Value in DATA - Approaches
Hello Guys,I am new to ML and I am starting with Exploratory Data Analysis, need help on good material on graphs, statistical methods to use when we first see data, different approach to fill missing value in data.Any articles/video or books on Exploratory Data Analysis, graphs and missing value in data would be helpful.See More
Shravan Kumar Koninti replied to Vishal Kapur's discussion Missing Value in DATA - Approaches"Missing value in dataset can be handled by 4 types:
1) You can delete those records from dataset
2) You can do Mean/ Mode/ Median Imputation.
3) If you know modelling, then you can use prediction model
4) KNN Imputation - The missing values of…"
"Thanks Vincent, Any books or articles on basic and advance graphs we can create for data virtualisation??"
"Hello Denis,
Do you have any good material, books suggestion for Exploratory Data Analysis and how to handle missing values.??"
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