This is about Treue Technologies Data science Internship tasks.
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Updated
Aug 30, 2023 - Jupyter Notebook
This is about Treue Technologies Data science Internship tasks.
This has been a machine learning quest to classify cancer types using gene expression data, utilizing powerful tools and techniques to preprocess, train and evaluate models. The ultimate goal, to save lives through early diagnosis with high accuracy and precision.
Linear Regression Models on Montesinho Forest Fire
This repository explores and compares different regression models for predicting continuous outcomes. This repository includes implementations and evaluations of five key regression models. The primary goal is to demonstrate how each model works, evaluate their performance using R-squared values, and guide users in selecting the best model.
Predicting compressive strength of concrete using machine learning models with featurization and Hyper parameter tuning
It calculates the accuracy score and confusion matrix for a logistic regression model. The dataset is about coupon used or not in an apparel store known as Simmons .
Integrated robust and reliable ML Pipelines for Research and Production environment
A dredge function to select the best models through an exhaustive combination of parameters.
A web application that employs machine learning models to provide accurate and instant car price estimations based on various features and specifications.
Machine Learning project based on UCI mushroom dataset
Open Machine Learning course at MIPT
Machine Learning Projects learning and Practicing
Amazon employee data to predict approval/ denial
We have been given historical sales data for 45 stores situated in various regions. Each store comprises multiple departments, and our objective is to forecast sales for each department within these stores.
Flight Analysis - Flight Delay
This Project is about to identify if a person's back pain is abnormal or normal using collected physical spine details/data.
A project to predict the average prices of Avocado in USA.
The aim of this project is to predict fraudulent credit card transactions using machine learning models.
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