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Ridge-Regression
Reference - Introduction to ML with Python- O'ReilyIris-Outliers
Detecting the anomalies of outliers using IRIS DatasetHeart-Disease-Prediction
Predicting whether a person has heart disease or not.SVM-Kernels
Classifying default datasets using SVM KernelsTask-1-TSF-Internship
KNN-Regression
K-nearest Neighbors Regression done using the Wave Dataset. Reference taken from Introduction to ML with Python-O'ReillyTitanic-Kaggle-Dataset
The Titanic ML competiton on KaggleYOLO-Object-Detection
Sign-Language-Classification
Decision-Tree-Classifier-on-Breast-Cancer-Dataset
Reference taken from Introduction to ML by Python-O'ReillyRandom-Forest-Classifier
Reference taken from Introduction to ML by Python-O'ReillyTSF-Internship-Task-3
TSF-Internship-Task-2
Sales-Prediction
Using Linear Regression to predict sales of various products using linear regressionTSF-Internship-Task-5
shubhankartiwari99.github.io
Wikipedia-Web-Scraping
Scraping data off a table from a web page of Wikipedia using BeautifulSoupFeature-Extracton-on-Facial-Image-Dataset
Reference taken from Intro to ML in python - O'ReillyFacial-Expression-Recognition
Multiclass-SVM
Cancer-Classification
Finding the best classifier for classifying cancer as benign or malignant, using Hyperparameter TuningMovies-Rating
Using recommender systems to show different movie ratings and thus recommend them based on their popularityHeadlines-Text-Generation
Predicting news headlines from a data-set of New York Times HeadlinesDecision-Tree-Regressor
Reference taken from Introduction to ML by Python-O'ReillyNLP-Sentiment-Analysis
Online-Retail-K-Means-Hierarchical-Clustering-Kaggle
Price-Analysis-and-Linear-Regression-Kaggle
This jupyter notebook shows the code for price analysis of Melbourne Housing using Linear RegressionIris-Classfication-using-K-Nearest-Neighbors
Classification of Iris Data using KNN. Reference has been taken from the book Introduction to ML with Python-O.ReillyKNN-Classification
Understanding the basics of K-Nearest Neighbors Classification.Love Open Source and this site? Check out how you can help us