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Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.
I used this notebook to discuss different supervised learning approaches. In the notebook you can find evaluations of a logistic regression, a K-Nearest-Neighboor, a Support Vector Machine, a Decision Tree and the ensemble methods Random Forest, AdaBoost and XGBoost Classifyer.
I have developed a GitHub project on ex-showroom car price prediction. The project includes data cleaning, data modeling, and data selection for accurate predictions. It also involves feature selection, model evaluation, testing, and comparison to determine the most effective model.
In this project, I use several different classification algorithms to predict whether a patient has breast cancer or not. This project uses K-fold cross validation, logistic regression, LDA, QDA, SVM, and model tuning techniques to achieve a 96% accuracy rate. This project was completed via R Markdown and LaTex.
This is the historical data that covers sales of a supermarket, Walmart. In this work, I tried to explore the dataset and create a simple model to predict the sales (Weekly_Sales)
Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.
The Office of Foreign Labor Certification is facing a dramatic increase in work visa applications, but is hampered by a sluggish review system. It needs to improve the process by developing a way to quickly, accurately identify applications likely to be accepted or rejected so their processing may be prioritized.
In this project we're going to explore a workflow to easily compete in the Kaggle Titanic competition, using a pipeline of functions to reduce the number of dimensions you need to focus on.
The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.