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A better framework than Flask. Get production-ready code and API. With automatic interactive documentation. Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.
Blog¶
Handle Outliers - Univariate
Handling outlier is a big task for data scientist. To handle the outliers we have many different methods to handle them i.e. IQR, Z-score, Mean-Median Imputation, Winsorization, etc. We are going to discuss only univariate methods to handle outliers.
I have written this page as notes very time ago; so if there is any mistake please let me know I'll fix it. Thanks 🤗
Regularization in ML
Regularization is used to solve the problem of overfitting caused while training a ML model. In regularization, the model is penalized for overfitting on train data means whenever model tries to predict on training data it add some penalty to the loss function in term of coefficients of the model.
Regression Interview Questions
Assumptions of Linear Regression?
There are about 5 main assumption while training a Linear Regression Model which are:
1. Linear Relationship Between Input And Output Data
Relationship of every input feature must be linear with output feature.
2. Multi-Collinearity
Starship Prompt
