## Performance Metrics for Classification and Regression Algorithms

We will examine many performance indicators used frequently in machine learning in this post. The performance and efficiency of our machine learning model are measured using performance metrics. In machine learning, the performance of a model is often evaluated using performance metrics. These metrics help assess the accuracy and effectiveness of classification and regression algorithms. […]

## What is Data Imputation and it’s different techniques

Data imputation is an essential technique in data science that involves filling in missing values in a dataset. Missing values can affect the accuracy of predictive models and cause biased results. In this article, we will explore various data imputation techniques to help you choose the best approach for your project. There are several methods […]

## Random Forest Algorithm

Random Forest is a robust machine-learning algorithm that is used for both classification and regression tasks. It is a type of ensemble learning method, which means that it combines multiple decision trees to create a more accurate and stable model. The mathematical intuition behind Random Forest is rooted in the concept of decision trees and […]

## Decision Tree

Decision tree algorithms are a type of supervised learning algorithm used to solve both regression and classification problems. The goal is to create a model that predicts the value of a target variable based on several input variables. Decision trees use a tree-like model of decisions and their possible consequences, including chance event outcomes, resource […]

## Support Vector Machine

Support Vector Machines (SVM) is a supervised machine learning algorithm that can be used for classification or regression tasks. The goal of the SVM algorithm is to find the hyperplane in an N-dimensional space that maximally separates the two classes. Mathematical Intuition Support Vector Machines (SVMs) are a type of supervised machine learning algorithm that […]

## Steps to Create a Tensorflow Model

There are 3 fundamental steps to creating a model Create a Model -> Connect the layers of NN yourself by using Sequential or Functional API or import a previously built model(Transfer Learning) Compile a Model -> Define how a model’s performance should be measured(metrics) and how to improve it by using an optimizer(Adam, SGD, etc.) […]

## How to deal with outliers

In this Notebook, we will describe how to deal with outliers Trimming outliers from the dataset Performing winsorization Winsorizing is different from trimming because the extreme values are not removed, but are instead replaced byother values. Data greater than quantile 90 percent is replaced by value at 90 quantiles similarly less thenquantile 5 percent is […]

## What is data leakage in Machine Learning

When training a machine learning model, we normally prefer selecting a generalized model which is performing well both on training and validation/test data. However, there can be a situation where the model performs well during testing but fails to achieve the same level of performance with real-world (production data) usage. For example, your model is […]

## How to do Feature Encoding and Exploratory Data Analysis

Categorical variables are those values that are selected from a group of categories or labels. For example, the variable Gender with the values of male or female is categorical, and so is the variable marital status with the values of never married, married, divorced, or widowed. In some categorical variables, the labels have an intrinsic […]

## 8 Essential Machine Learning Terms You must Know

Data Wrangling Data Wrangling is the process of gathering, selecting, cleaning, structuring, and enriching raw data into the desired format for better decision-making in less time. If you want to create an efficient ETL pipeline(Extract, transform, and load) or create beautiful data visualizations, you should be prepared to do a lot of data wrangling-springboard. Data […]