sohrabrahimi Goto Github PK

a-b-testing

a-neural-network-for-predicting-bike-rentals

In this project I will build a Neural Network from scratch and train it to predict the buke ridership at different times of the year and week.

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

boston-housing-market-analysis

charity-organization

A charity organization is interested in running a more effective marketing campaign and maximizing their profit by identifying and targeting the possible donors. This project investigates the optimum classification and prediction models that can best predict the probability of a household to be donor and the donation amount.

convolutional-neural-networks-for-dog-breed-detection-

In this project, I will build a pipeline to process real-world, user-supplied images. Given an image of a dog, this algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

crime-in-philadelphia

In this project I will discover some dynamics of the crime in Philadelphia

customer-segment-analysis

In this project I use unsupervised learning techniques to identify different segments of costumers with different preferences for optimizing product delivery.

data-visualization-javascript-

In this project I have visualized the Hershey data on candy sales for four states. I have used d3.js and dimple.js to visualize the data.

data-wrangling-open-street-map-

Data Wrangling with OpenStreetMap

deep-learning-building-blocks

eda-on-candy-sales-data-set-

Udacity Project 4

finding-potential-donors

In this project I use classification models to predict potential donors given a set of demographic factors.

flights-networks-in-the-us

In this study I will visualize the network of flights in the past 10 years.

fraud-prediction-the-case-of-enron-emails

Project Overview In 2000, Enron was one of the largest companies in the United States. By 2002, it had collapsed into bankruptcy due to widespread corporate fraud. In the resulting Federal investigation, a significant amount of typically confidential information entered into the public record, including tens of thousands of emails and detailed financial data for top executives. In this project, you will play detective, and put your new skills to use by building a person of interest identifier based on financial and email data made public as a result of the Enron scandal. To assist you in your detective work, we've combined this data with a hand-generated list of persons of interest in the fraud case, which means individuals who were indicted, reached a settlement or plea deal with the government, or testified in exchange for prosecution immunity.

gps-data-cleaning

interview-preperation

This repository includes some example codes of commonly used interview questions with a focus on data structures.

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

mit-deep-learning-book-pdf

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

odsc

🍭 Hold your breath, make a wish / Count to three

tensorflow

Computation using data flow graphs for scalable machine learning

the-u.s.-petitions

In this project I will use Natural Language Processing (NLP) to study the major topics in US petitionsaddressed to the White House.

titanic

In this project I will explore some patterns in the Titanic dataset available at https://www.kaggle.com/c/titanic. The main objective of this piece is to experiment with different functions in python, particularly Numpy and Pandas. In this report, I will present different visualization techniques and in the end, present a classification model.

training-a-self-driving-cab

In this project I will apply reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. I will first investigate the environment the agent operates in by constructing a very basic driving implementation. Once the agent is successful at operating within the environment, I will then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, I will then implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, I will improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results.

who-eats-what-

In this project I will analyse the relationship between demographic factors and preferences for restaurant in different regions.

yelp-contest-romance-in-restaurants

yelp-scraper