# Abhishek Wasnik

## Precision and Recall in Python

Let’s talk about Precision and Recall in today’s article. Whenever we implement a classification problem (i.e decision trees) to classify data points, there are points that are often misclassified. Even though accuracy gives a general idea about how good the model is, we need more robust metrics to evaluate our model. Let’s consider an Example. …

## ROC curves in Machine Learning

The ROC curve stands for Receiver Operating Characteristic curve. ROC curves display the performance of a classification model. ROC tells us how good the model is for distinguishing between the given classes, in terms of the predicted probability. In this article, we will understand ROC curves, what is AUC, and implement a binary classification problem …

## K-Nearest Neighbors from Scratch with Python

In this article, we’ll learn to implement K-Nearest Neighbors from Scratch in Python. KNN is a Supervised algorithm that can be used for both classification and regression tasks. KNN is very simple to implement. In this article, we will implement the KNN algorithm from scratch to perform a classification task. The intuition behind the K-Nearest …

## Linear Regression from Scratch in Python

In this article, we’ll learn to implement Linear regression from scratch using Python. Linear regression is a basic and most commonly used type of predictive analysis. It is used to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable. The …

## K-Means Clustering From Scratch in Python [Algorithm Explained]

K-Means is a very popular clustering technique. The K-means clustering is another class of unsupervised learning algorithms used to find out the clusters of data in a given dataset. In this article, we will implement the K-Means clustering algorithm from scratch using the Numpy module. The 5 Steps in K-means Clustering Algorithm Step 1. Randomly …

## Stemming and Lemmatization in Python

In the field of Natural language processing, Stemming and Lemmatization are the text normalization techniques used to prepare texts, documents for further analysis. Understanding Stemming and Lemmatization While working with language data we need to acknowledge the fact that words like ‘care’ and ‘caring’ have the same meaning but used in different forms of tenses. …

## Logistic Regression From Scratch in Python [Algorithm Explained]

The objective of this tutorial is to implement our own Logistic Regression from scratch. This is going to be different from our previous tutorial on the same topic where we used built-in methods to create the function. Logistic regression is a classic method mainly used for Binary Classification problems. even though it can be used …

## Bias Variance Tradeoff – Understanding the Concepts

To evaluate a model performance it is essential that we know about prediction errors mainly – bias and variance. Bias Variance tradeoff is a very essential concept in Machine Learning. Having a Proper understanding of these errors would help to create a good model while avoiding Underfitting and Overfitting the data while training the algorithm. …

## Creating a TF-IDF Model from Scratch in Python

The TF-IDF model is a method to represent words in numerical values. “Hello there, how have you been?”, you can easily understand what I am trying to ask you but computers are good with numbers and not with words. In order for a computer to make sense of the sentences and words, we represent these …

## Creating Bag of Words Model from Scratch in python

The Bag of Words Model is a very simple way of representing text data for a machine learning algorithm to understand. It has proven to be very effective in NLP problem domains like document classification. In this article we will implement a BOW model using python. Understanding the Bag of Words Model Model Before implementing …