Over-fitting vs Under-fitting in Machine Learning

A Machine Learning or Deep Learning model must be in balanced state (Generalized) If you ever built a supervised Machine Learning model on some real-time data, it is impossible
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Deep Neural Networks – Activation Functions

“Activation Functions” play a critical role in the architecture of Artificial Neural Networks (ANN). The Deep neural networks are being successfully used in many emerging domains to solve real
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Feature Engineering for Structured Data (numerical and categorical)

Feature Engineering for Structured Data (numerical and categorical) “Best Ingredients make Best Dish “, the same way “Best Features make Best Model” As part of any Machine Learning project,
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Regression – Find relation between Multiple Inputs and Target variable (Residuals Vs Fitted Graph)

Regression – Find relation between Multiple Inputs and Target variable One Input variable : When only one input variable and one output variable, scatter chart is useful in finding
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Step-by-Step Data Science Project (End to End Regression Model)

Step-by-Step Data Science Project (End to End Regression Model) We took “Melbourne housing market dataset from kaggle” and built a model to predict house price. While building the model
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Credit Card Fraud Detection Using SMOTE

Credit Card Fraud Detection Using SMOTE (Classification approach) : This is the 2nd approach I’m sharing for credit card fraud detection. We are going to explore resampling techniques like
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Anomaly Detection using Gaussian (Normal) Distribution

Anomaly Detection using Gaussian (Normal) Distribution For training and evaluating Gaussian distribution algorithms, we are going to split the train, cross validation and test data sets using blow ratios.
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Naive Bayes Algorithm – Introduction to Text Analytics

Conditional Probability¶ Conditional probability as the name suggests, comes into play when the probability of occurrence of a particular event changes when one or more conditions are satisfied (these
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Heterogeneous Ensemble Learning (Hard voting / Soft voting)

Heterogeneous Ensemble Learning (Hard voting / Soft voting) Voting Classifier Suppose you have trained a few classifiers, each one individually achieving about 80% accuracy (Logistic Regression classifier, an SVM
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Credit Card Fraud Detection with Python

Credit Card Fraud Detection, Anomaly Detection Using Python (Complete – Classification & Anomaly Detection) : Let us take a credit card fraud dataset from Kaggle (https://www.kaggle.com/mlg-ulb/creditcardfraud/Data). The datasets contains
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