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
Machine Learning project based on UCI mushroom dataset
Check my projects related to ML feature engineering and modeling.
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.
Multiple Disease Prediction System using Machine Learning.
Machine Learning Projects learning and Practicing
This project uses supervised machine learning techniques with multiple regression models to predict CO2 emissions in Canada, it includes data cleaning, encoding, analyzing and visualization to identify patterns, resulting in a model that can make accurate predictions.
Predicting whether or not a cell has cancer.
End-to-end projects: customer churning prediction using the Random Forest Classifier Algorithm with 97% accuracy; performing pre-processing steps; EDA and Visulization fitting data into the algorithm; and hyper-parameter tuning to reduce TN and FN values to perform our model with new data. Finally, deploy the model using the Streamlit web app.
This Project is about to identify if a person's back pain is abnormal or normal using collected physical spine details/data.
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