Tune Hyperparameters of Decision Tree with Grid Search
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Updated
Apr 19, 2018 - Jupyter Notebook
Tune Hyperparameters of Decision Tree with Grid Search
Tuning NN parameters from keras library with sklearn's grid search
This repository includes all the udacity deep learning project that i have worked on while i was taking this course
Hyperparameter Tuning web app made with Streamlit
Neural Networks, Deep Learning, Computer Vision, Natural Language Processing, Python
Project for Deep Learning Nanodegree, unit 4 (Recurrent Neural Networks).
Analyze the performance of 7 optimizers by varying their learning rates
A ML model to classify mobile price range.
Applied several models (decision tree, logistic regression, random forest) to solve a classification problem on demographic data. Explored and compared model performance with hyperparameter tuning.
Used machine learning to create a model that predicts which passengers survived the Titanic shipwreck
In this problem statement, a sequence of genetic mutations and clinical evidences, i.e. descriptive texts as recorded by domain experts are used to classify the mutations to conclusive categories, to be used for diagnosis of the patient.
Improving Machine Learning Performance by: feature engineering, oversampling and hyperparameter tuning
The project aims to build a Species Distribution Model for the frog species - "Litoria Fallax" across Australia using TerraClimate variables.
Different types of supervised learning models used for classification problem. Included cross validation for finding hyperparameters whenever necessaruy.
Machine learning model using sklearn on pandas dataframe
Successfully established a supervised machine learning model that can accurately predict whether the travel insurance claim of a particular customer should be approved or not by a travel insurance agency.
Examining the effect of hyperparameters and Exploring the relationship between hyperparameters through experimentation: Building, Training, and Tuning an Image Classification Model with TensorFlow and PyTorch – A CIFAR-10 dataset Use case.
Designed CNN based Melanoma classification
A CNN model to identify images of plant seedlings.
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