mursaleenshiraj Goto Github PK
Type: User
Location: Washington, USA
mursaleenshiraj's Projects
birthday-problem-2
This is a demonstration of how simulations can solve an otherwise difficult to solve problem. I showed this to my class once to show them the power of simulation. I posted a procedural code earlier, and posting this as I find this a intuitive way to learn about OOP in Python.
birthday_probelm
This is a sample code to calculate the probability of at least 2 people sharing the same birthday in a group. I wrote this code to demonstrate the power of simulations to a class I taught in Fall 2018. This is an interesting and intuitive way to show how results that require complicated or long calculations can be obtained through simulations.
hirano-imbens-2004
This is a demonstration of how we can implement Hirano-Imbens (2004) model for estimating Average Dose Response Function under Normally distributed continuous treatment.
keras-to-predict-host-rating
Using Keras to predict the ratings of airbnb hosts. This is a simple demonstration of using keras.
linear-regression-with-stochastic-gradient-descent
This is an implementation of estimating linear regression parameters through stochastic gradient descent method. The code is to learn about this method. This is not the best way to estimate linear regression parameters for the data used in the example. As I said, the purpose of the code is to show what gradient descent does, in practice such code may not be used as there are existing packages that are far more efficient in optimizing these functions. Having said that, this is a nice exercise to lear about stochastic gradient descent and practice writing own codes..
logistic-regression-from-scratch
This is a demonstration of the logistic regression. In this code, I show how the likelihood of the logistic regression in constructed and use gradient descent to optimize the objective function. Final output is very close to the result from scikit learn package.
machine-learning-prediciting-energy-efficient-appliance-purchase
The objective here is to predict the purchase of energy efficient home appliances using various methods like Neural Network, Logistic regression, SVM, Random Forest etc. and then compare the prediction performance in the test data. For estimation, the library sklearn has been used here. This is part of a class project for EconS 514 in Spring 2019 in collaboration with Afrin Islam, Josh Olsen and Jake Wagner.
machine_learning_methods_for_demand_estimation
This is a presentation I gave to the Econometrics IV class. I described a paper by Patrick Bajari and coauthors. I also described some of the methods the applied in simple terms.
monte-carlo-integration
This is a sample code to show how Monte-Carlo integration work. The function used in this demonstration is from Cameron and Trivedi's Econometrics book. This is written as an OOP style.
regression-with-gradient-descent
This is a demonstration of linear regression with gradient descent. In addition to gradient descent, some other methods of estimating the parameters of a linear regression is also shown.
simulation_demonstrating_bias_in_linear_regression
This code is to demonstrate the bias in a linear regression model through bootstrapping/simulations
unsupervised_clustering
clustering a group of vegetables based on price growth rates
vector-auto-regression
This code is a demonstration of how to implement a VAR model. I estimate the VAR coefficients and then compare those with the results from a statsmodels package. The results are identical. This is a nice way to understand the steps behind estimating a VAR. This would also help to connect the concepts VAR and SUR.
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