5 Most important Data Pre-Processing Techniques – Feature Scaling – Part IV
what, why and how to do Feature Scaling, Normalization Vs Standardization, common mistakes a beginner may do on Feature Scaling
Tag #MachineLearning
5 Most important Data Pre-Processing Techniques – Encode Categorical Values – Part III
5 Part series of most important Data Pre-Processing Techniques of Machine Learning: Encode Categorical Values, One-Hot Encoding, Ordinal Encoding, Categorical Data types explained with examples
5 Most important Data Pre-Processing Techniques – Impute missing data – Part II
Major imputation techniques in Machine learning, US census data, Listwise Deletion, Impute NaN with mean, Trimmed Mean, median, mode, Drop columns if >60% of data is missing
5 Most important Data Pre-Processing Techniques for Machine Learning – Part I
This is a 5 Part series of most important Data Pre-Processing Techniques of Machine Learning. Part 1 - Verify data types of the variables/features.
How to Impute missing values using SimpleImputer and ColumnTransformer?
How to impute missing values using SimpleImputer and ColumnTransformer, Disadvantages of SimpleImputer, Advantage of ColumnTransformer
How Random Forest works? – Why we need Random Forest?
How Random Forest works? How to overcome Overfitting? What is Ensemble Learning? Why we need Random Forest? What is Bagging/Bootstrap Aggregation?
Decision Trees
In this article we will learn about what is a Decision Tree? How it works? Why Decision Tree Overfitts & How can we resolve it?
Want to Save and Reuse a model later?
In machine learning, training a model and testing it is definitely not an end. Should we run this source code of training, tuning everything again to do predictions in future? No Need!!! We can save and reuse the same model…
Logistic Regression — Part III — Titanic Disaster Survival Prediction
In this article we will be researching on the Titanic Dataset with Logistic Regression and Classification Metrics. Lets see how to do logistic regression with Python — LogisticRegression() from sklearn. I have taken the Titanic data set from Kaggle. Here…
Logistic Regression Part II— Cost Function & Error Metrics
Logistic Regression - Cost Function, Error Metrics, Precision, Recall, Specificity, ROC Curve, F-Score, Observations from ROC Curve