It is a full stack ml app , compared multiple ml models(KNeighborsClassifier, LogisticRegression, RandomForestClassifier ) , later deploy the best model using flask , and the frontend is created with react.js
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Updated
Sep 2, 2023 - Jupyter Notebook
It is a full stack ml app , compared multiple ml models(KNeighborsClassifier, LogisticRegression, RandomForestClassifier ) , later deploy the best model using flask , and the frontend is created with react.js
In this repository, you can find my work related to Machine Learning topics like Guassian Mixture Model, KNeighbors classification, Naive Bayesian classification etc..
In this project I use different models and hyperparameters to classify fruits types and compare the results of each approach.
peer-to-peer lending, use techniques to train and evaluate Machine Learning models with imbalanced classes to identify the worthy
This GitHub repository hosts a machine learning project focused on predicting customer interest in insurance products. Leveraging various ML algorithms, including decision trees, logistic regression, and ensemble methods, this project analyzes historical customer data to create predictive models.
Analysis on Glass Dataset to find out the category of the class in which it belongs.
KNeighborsClassifier for audio files
Machine Learning for Pattern Recognition
MNIST Handwritten digit classification using KNeighborsClassifier
Classifying the various quality of wine and analyzing the data led to the prediction of wine quality using some Machine Learning Algorithms.
Different classification algorithms to predict the species of Iris flowers
A stroke Prediction Model
A basic classification model using KNeighborsClassifier on Iris Dataset.
To predict who's more likely to buy your product
This Repo contains the projects of Data Science Internship assigned by OASIS INFOBYTE SIP for Duration 15 JUNE 2023 to 15 JULY 2023
Heart attack data analysis with python
A Machine Learning (ML) model that every ML beginner develops. This model is used to predict type of Iris flower based on the input.
This example application predicts the iris flowers classification based on the IRIS data set.
ETL workflow and data analysis. ETL-workflow using prefect and pygrametl (SCD, slow changing dimension). Product classification based on product name.
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