backup

board-game-review-prediction

1. Introduction In this project, a dataset of about 82000 rows and 20 columns was given regarding the reviews of various board games available. A supervised machine learning model as then been trained and evaluated to predict the ratings given to a particular board game based on several features of the datasets. In order to choose the machine learning algorithms best suited to make predictions of the average rating of the board games, number of regression or similar models were considered and the data has been investigated to choose the best possible models: Considering that the problem is to predict a numerical value several options such as follows could be chosen: i. Linear Regression: Can be chosen if the variables show a linear correlation with the label. If the correlation is not linear, the linear regression model would not be accurate. ii. Decision Trees and Random forest regression: A Decision Tree is an intuitive model where by one traverses down the branches of the tree and selects the next branch to go down based on a decision at a node.While building the tree, the goal is to split on the attributes which create the purest child nodes possible, which would keep to a minimum the number of splits that would need to be made in order to classify all instances in our dataset. Purity is measured by the concept of information gain, which relates to how much would need to be known about a previously-unseen instance in order for it to be properly classified. Random Forests are simply an ensemble of decision trees. The input vector is run through multiple decision trees. For regression, the output value of all the trees is averaged; for classification a voting scheme is used to determine the final class. Great at learning complex, highly non-linear relationships.Very easy to interpret and understand. can be prone to major overfitting.Using larger random forest ensembles to achieve higher performance comes with the drawbacks of being slower and requiring more memory. iii. Neural Network regression: A Neural Network consists of an interconnected group of nodes called neurons. The input feature variables from the data are passed to these neurons as a multi-variable linear combination, where the values multiplied by each feature variable are known as weights. A non-linearity is then applied to this linear combination which gives the neural network the ability to model complex non-linear relationships. A neural network can have multiple layers where the output of one layer is passed to the next one in the same way. At the output, there is generally no non-linearity applied. Neural Networks are trained using Stochastic Gradient Descent (SGD) and the backpropagation algorithm. They are very effective for data with complex non-linear relationships with negligible consideration to the structure of the data. However, these models could be difficult to interpret and computationally challenging.

breast-cancer-detection-using-logistic-regression

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

decision-tree

emoji-recognition-through-hand-gestures

hindihandwritingrecognition

lda-latent-discrimant-analysis-vs-pca-principal-component-analysis-

logistic-regression

market-basket-analysis

Introduction Market Basket Analysis is one of the most common methods used by large retailers to discover product relationships. It operates by looking for things that are frequently found together in transactions. To put it another way, it enables businesses to discover connections between the things that customers purchase. Association Rules are commonly used to analyse retail basket or transaction data, and they are designed to find strong rules revealed in transaction data using measures of interest, based on the concept of strong rules. An example of Association Rules -Assume there are 100 customers -10 of them bought milk, 8 bought butter and 6 bought both of them. -bought milk => bought butter -support = P(Milk & Butter) = 6/100 = 0.06 -confidence = support/P(Butter) = 0.06/0.08 = 0.75 -lift = confidence/P(Milk) = 0.75/0.10 = 7.5

multiple-linear-regression

multivariate-regression

my-first-repository

My first Github project by Eckovation

my-ml-backup

naive-bayes-classifier

nlp-with-bert-for-sentiment-analysis

object-detection-in-a-video-using-centroid-approach-and-yolo5

opencv

Open Source Computer Vision Library

pca-principalcomponentananlysis

polynomial-regression

radix-ui

Radix Themes is an open-source component library optimized for fast development, easy maintenance, and accessibility. Maintained by @workos.

railwayreservation

silentspeechtechnology

single-linear-regression

souveekroy

My personal Repository

souveekroy.github.io

stonepaperscissors

support-vector-machines-accuracy-in-identifying-spam-mails-from-mail-dataset-

yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite