If you had more time what do you think can be done further to improve the results?

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Learning Goal: I’m working on a machine learning project and need an explanation and answer to help me learn.Decide on solving a problem and find an appropriate data set.
Write clearly the source of the data. Provide the link at the top.
Apply data cleaning, data preparation, visualization, etc.
Apply several of the machine learning models we have learned in class. Please, if possible, try to apply some models from outside the class.
Conclude with the best model and its predictions. What are your findings? If you had more time what do you think can be done further to improve the results?
Provide some writing and explanations throughout your notebook.
Deliverables: 1. The Python Jupyter notebook file 2. The HTML output of this code notebook – File -> Download as -> HTML Models include: Model Evaluation: Overfitting and Underfitting, Errors, Bias, Variance, Regularization, Performance metricsResampling Methods: Monte Carlo simulation, Bootstrapping,Cross-Validation, Grid SearchEnsemble Methods: Bagging, Boosting, Random forests, Adaboost, Gradient Boosting Machine, Support Vector Machines (SVM)Kernel Methods: Kernel Density Estimation, PCA, Kernel PCA, Kernel RegressionBayesian Data Analysis: Priors, Likelihood, Maximum a Posteriori, Sampling methods, MCMC, Bayesian RegressionProbabilistic graphical models: Bayesian Belief Networks, Variable Elimination Method
Requirements: all the instruction must be followed well | Python

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