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30 Jan

Hierarchical Clustering Algorithm

Thanmai Chandaka0 CommentsBlogclustering, DataScience, Hierarchical Clustering, Machine learning, Machine learning models, Programming, Python, Unsupervised Learning, What is Hierarchical Clustering?

Hierarchical Clustering is a type of unsupervised machine learning algorithm used to group similar data points together. The goal of this algorithm is to create a hierarchy of clusters, where each cluster is a subset of the previous one. The algorithm starts by treating each data point as its own cluster. It then repeatedly merges the two closest clusters, until all points are in the same cluster or a stopping criterion is met. The result is a tree-like structure called a dendrogram, which shows the hierarchy of the clusters. There are two main types of Hierarchical Clustering: Agglomerative and Divisive.…

26 Jan

K-Means Clustering Algorithm

Thanmai Chandaka0 CommentsAlgorithmsCoding, DataScience, K-Means, K-Means Clustring, Machine learning, Mathematical Intuition, Programming, Python

K-Means is a widely used clustering algorithm that partitions a set of data points into K clusters, where each cluster is defined by its centroid. The goal of the algorithm is to minimize the sum of squared distances between each data point and its closest centroid. The algorithm starts by randomly selecting K initial centroids and assigning each data point to the closest centroid. Then, it iteratively updates the position of the centroids and reassigns each data point to the closest centroid until the assignments no longer change. The algorithm terminates when the centroids reach a stable position. Mathematical Intuition…

20 Jan

How to connect OpenAI api with python code

geekycodesco0 CommentsBlog, PythonAPI, ChatGPT, GPT3, OpenAI, Programming, Python

To connect OpenAI API with Python code, you will need to use the OpenAI Python library, which can be installed using pip: pip install openai You will also need to have an API key for the OpenAI service you want to use. You can get an API key by creating an account on the OpenAI website. Once you have the OpenAI library and an API key, you can use the following code as an example on how to connect the OpenAI API with Python: import openai # Set the API key openai.api_key = "YOUR_API_KEY" # Define the prompt prompt =…

18 Jan

Random Forest Algorithm

Thanmai Chandaka0 CommentsData Science, Decision Tree, Guest Blogs, Machine Learning, Python, Random ForestBagging Technique, Ensemble learning, how to build machine learning models, Machine learning, Machine learning models, Python, Random Forest

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 bagging. A decision tree is a tree-like structure in which the internal nodes represent the feature(s) of the data, the branches represent the decision based on those features, and the leaves represent the output or class label. Each internal node in a decision tree represents…

09 Jan

Decision Tree

Thanmai Chandaka0 CommentsData Science, Decision Tree, Guest Blogs, Machine LearningDataScience, Decision Tree, Machine learning, Programming, Python

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 costs, and utility. The model is based on a decision tree that can be used to map out all possible outcomes of a decision. A decision tree algorithm works by breaking down a dataset into smaller and smaller subsets while at the same time, an…

09 Jan

Support Vector Machine

Thanmai Chandaka0 CommentsData Science, Machine LearningCoding, DataScience, Machine learning, Programming, Python, Support Vector Machine, Support Vector Machine Mathemeatical Intution, SVM

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 can be used for classification or regression tasks. The goal of an SVM is to find the hyperplane in a high-dimensional space that maximally separates the different classes. Imagine we have two classes of data points, represented by circles and rectangles The SVM algorithm will…

20 Oct

Five Courses that can be finished in one week to advance Pandas skills

geekycodesco0 CommentsBlogMachine learning, Pandas, Programming, Python

𝟏. 𝐖𝐫𝐢𝐭𝐢𝐧𝐠 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐂𝐨𝐝𝐞 𝐰𝐢𝐭𝐡 𝐩𝐚𝐧𝐝𝐚𝐬: This course will build on your knowledge of Python and the panda's library and introduce you to efficient built-in pandas functions to perform tasks faster. Link:- Get the course here 𝟐. 𝐉𝐨𝐢𝐧𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐩𝐚𝐧𝐝𝐚𝐬: In this course, you will learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. Get this course here 𝟑. 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐞𝐝 𝐃𝐚𝐭𝐚 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐩𝐚𝐧𝐝𝐚𝐬: This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas to get data from a variety of sources, from spreadsheets of…

20 Oct

Find out the Longest Path in a matrix

geekycodesco0 CommentsInterview, Interview Questions, PythonAlgorithms, Coding, Programming, Python

Given an m-by-n matrix with positive integers, determine the length of the longest path of increasing within the matrix. For example, consider the input matrix:[1 2 34 5 67 8 9] The answer should be 5 since the longest path would be 1-2-5-6-9 def isValid(mat, i, j): return 0 <= i < len(mat) and 0 <= j < len(mat) def findLongestPath(mat, i, j): if not isValid(mat, i, j): return [] path = [] if i > 0 and mat[i - 1][j] - mat[i][j] == 1: path = findLongestPath(mat, i - 1, j) if j + 1 < len(mat) and mat[i][j…

04 Oct

How to do Feature Encoding and Exploratory Data Analysis

geekycodesco0 CommentsData Science, Machine Learning, Pandas, PythonDataframe, DataScience, Datasets, Machine learning, Programming, Python

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 order, for example, in the variable Student's grade, the values of A, B, C, or Fail are ordered, A being the highest grade and Fail the lowest. These are called ordinal categorical variables. Variables in which the categories do not have an intrinsic order are…

20 Jun

Which one to use – RandomForest vs SVM vs KNN?

geekycodesco0 CommentsData Science, Machine Learning, Random ForestDataScience, KNN, Machine learning, Python, Random Forest, SVM

The basic steps to deciding which algorithm to use will depend on a number of factors. A few factors which one can look for are listed below: The number of examples in the training set.Dimensions of featured space.Do we have correlated features?Is overfitting a problem? These are just a few factors on which the selection of the algorithm may depend. Once you have the answers to all these questions, you can move ahead to decide the algorithm. SVM The main reason to use an SVM instead is that the problem might not be linearly separable. In that case, we will…