Iris dataset
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Updated
Dec 20, 2023 - Jupyter Notebook
Iris dataset
Project compares three regression models for predicting the amount of gold recovered from gold ore in order to optimize gold production and eliminate unprofitable parameters. Data provided by Zyfra.
In this project, I have developed a Machine Learning model to predict whether users will click on ads. By analyzing various characteristics of users who click on ads, we can gain valuable insights and optimize ad campaigns for better engagement.
Using scikit-learn RandomizedSearchCV and cross_val_score for ML Nested Cross Validation
Calculate the bias of k-fold cross-validation with hyper-parameter configuration
Create a prototype for a machine learning model to predict the amount of gold recovered from gold ore.
Model to predict the amount of gold extracted from gold mineral.
Machine learning model which can predict the strength of a mixture for given composition of ingredients like cement, slag, ash, water, superplastic, coarse aggregates, fine aggregates, age.
Built Random Forest classifier from scratch on top of Scikit Learn decision trees. Using Scikit Learn to create data cleaning pipelines, perform grid searches for hyper parameter tuning, and decision tree modeling
Different types of supervised learning models used for classification problem. Included cross validation for finding hyperparameters whenever necessaruy.
Prediction Model, Bias and Uncertainty
Study Project for Yandex Practicum
GridSearchCV, RandomSearchCV For Model optimization and Saving/Loading the model
GridSearchCV For Model optimization
Overcoming overfitting and underfitting
Using various supervised learning estimators in Sci-Kit Learn to get the best prediction accuracy if possible for the pima indians dataset.
This folder contains project assignments that solve the problem of a case based on a dataset with hypothesis testing, Supervised and Unsupervised material.
pipelines chains together multiple steps so that the output of each step is used as input to the next step
Add a description, image, and links to the cross-validation-score topic page so that developers can more easily learn about it.
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