Habitat, a company based in Chicago, has recently purchased Seasons Villas in Woodbury, Minnesota. This multifamily community consists of 214 townhome-style rental units and is located at 8630 Summer Wind Alcove.
The acquisition was made from Boston Capital Group and the property was previously managed by Willow Bridge Property Company. Habitat’s strategic acquisitions team oversaw the purchase process with support from the Middle-Income Affordable Preservation (MAP) Fund. This fund is a joint venture between Enterprise Community Partners and Banc of America Community Development Company worth $150 million.
The seller in this transaction was represented by Minneapolis CBRE team. Seasons Villas offers a variety of floor plans ranging from 960 to 1,160 square feet with a total of 47 single-story units and 167 two-story units.
Zack Zalar, vice president of investments for Habitat stated that this acquisition is an important long-term investment for their company in the Twin Cities region. They are committed to providing exceptional value and service to all residents at Seasons Villas.
x = int(input(“Enter first number: “))
y = int(input(“Enter second number: “))
# addition
print(‘x + y = ‘, x+y)
# subtraction
print(‘x – y = ‘, x-y)
# multiplication
print(‘x * y = ‘, x*y)
# division
if(y !=0):
print (‘X / Y= ‘ , X/Y)
else:
print (“Division by zero not possible”)
# modulus
if(y!=0):
print (‘X % Y= ‘ , X%Y)
else:
print (“Modulus operation not possible”)
# exponentiation
if(x>=0 or (y>int)):
Print(‘X**Y’)= ‘’, pow(X,Y))
else :
Print (“exponent should be positive integer”)_This repository contains code for the paper “A Multi-Task Learning Approach to Predicting Stock Price Movements”_
## Requirements
The code was tested with Python 3.7 and PyTorch 1.8.
To install all required packages, run:
“`
pip install -r requirements.txt
“`
## Data
The data used in this project is from [Kaggle](https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs). It contains daily stock prices for various companies on the NYSE and NASDAQ exchanges between January 2000 and August 2016.
Download the data from Kaggle, unzip it, then move `prices-split-adjusted.csv` into a directory called `data` at the root of this repository.
You can also download our preprocessed dataset by running:
“`bash
wget https://github.com/samuelmchu/multi-task-stock-prediction/releases/download/v1/data.zip
unzip data.zip && rm -f data.zip
“`
This will create a new directory called `data`, which contains three CSV files:
* **train.csv** (80% of original dataset)
* **val.csv** (10% of original dataset)
* **test.csv** (10% of original dataset)
Each file has columns corresponding to different features such as open price, close price etc., along with labels indicating whether or not there was an increase in stock price on that day.
## Training
To train models using default hyperparameters run:
“`bash
python main.py –model
# e.g.
python main.py –model mlp # trains MLP model
python main.py –model lstm # trains LSTM model
python main.py –model gru # trains GRU model
“`
For more information about available arguments use:
“`bash
python train_multi_task_model/main_mtl.py –help
“`
## Results
The following table shows the results of our models on the test set.
| Model | Accuracy |
| — | — |
| MLP (single-task) | 53.6% |
| LSTM (multi-task) | 54.4% |
| GRU (multi-task) | **55.1%** |
Note that these values are slightly different from those reported in the paper, as we have since made some minor changes to our code.
## Citation
If you use this code or find it useful for your research, please consider citing:
“`bibtex
@article{chu2020multitask,
title={A Multi-Task Learning Approach to Predicting Stock Price Movements},
author={Samuel Chu and Yuhao Zhang and David Rosenberg},
year={2020}
}
“`
# 
Airbnb Clone
Airbnb is a vacation rental online marketplace company which offers arrangement for lodging, primarily homestays, or tourism experiences.
This project aims at creating an Airbnb clone using React Native with Expo CLI.
### Features:
* User registration / login
* Search listings by location
* Filter search results based on price range & number of guests
* View property details including description ,amenities ,price per night etc.
* Book a listing
* Add dates when booking will start & end
* Total cost calculation based on duration of stay
* Option to pay via credit card / PayPal
### Screenshots:
### Dependencies:
* React Native
* Expo CLI
* Firebase Realtime Database & Authentication
* Stripe API for payment processing
### How to run the app:
Clone this repository and install dependencies using `npm install` or `yarn`.
Create a new project on [Firebase](http//console.firebase.google.cm) and add your firebase config in **firebase.js** file.
Enable Email / Password authentication from Firebase console.
Add your Stripe publishable key in **payment.js** file.
Run the app using command `expo start` .
# Welcome to my personal website!
This is where I share my thoughts, projects, experiences and anything else that comes to mind.
## About me
I am an AI enthusiast with a passion for data science, machine learning (ML), natural language processing (NLP), computer vision (CV) and deep learning.
My interests include but are not limited to:
– Data Science
– Machine Learning
– Natural Language Processing
– Computer Vision
– Deep Learning
I have a strong background in mathematics, statistics and programming. I am proficient in Python, R and SQL.
I have experience working with various machine learning algorithms such as:
– Linear Regression
– Logistic Regression
– Decision Trees
– Random Forests
– Gradient Boosting Machines (GBMs)
– XGBoost
– LightGBM
I also have experience working with deep learning frameworks such as TensorFlow and Keras for computer vision tasks like image classification, object detection etc.
## Projects
Here are some of the projects that I’ve worked on recently:
### [Predicting Credit Card Fraud](https://github.com/akshay-madar/capstone-project)
This project is about predicting credit card fraud using machine learning algorithms. The dataset used for this project can be found on Kaggle ([link](https://www.kaggle.com/mlg-ulb/creditcardfraud)). The goal of this project was to build a model that could accurately detect fraudulent transactions from legitimate ones based on features like transaction amount, time elapsed since previous transaction etc.
The following models were trained:
1) Logistic regression classifier
2) Random forest classifier
The random forest classifier performed better than the logistic regression model achieving an AUC score of ~0.95 whereas the logistic regression model achieved an AUC score of ~0.91.
### [Image Classification using Convolutional Neural Networks (CNNs)](https://github.com/akshay-madar/image-classification-cnn)
In this notebook we’ll use CNNs to classify images from CIFAR10 dataset into one out of ten categories: airplane ���️ , automobile ��� , bird ��� , cat �� , deer���️️��♀️����️��������� dog ��� ship ��� truck . We’ll use PyTorch to build, train and evaluate our model. We’ll also use the trained model to make predictions on unseen images.
The following steps were followed:
1) Load and preprocess the dataset
2) Define a CNN architecture
3) Train the network on training data
4) Test trained model on test data
### [Image Classification using Transfer Learning](https://github.com/akshay-madar/image-classification-transfer-learning)
In this notebook we’ll use transfer learning for image classification. We’ll fine-tune a pre-trained ResNet18 neural network (trained on ImageNet dataset). The goal is to classify images from CIFAR10 dataset into one out of ten categories: airplane ���️ , automobile ��� , bird ��� , cat �� , deer���️️��♀️����️��������� dog ��� ship ��� truck .
The following steps were followed:
1) Load and preprocess the dataset
2) Fine-tune ResNet18 neural network by replacing its last fully connected layer with another one that has 10 output features instead of 1000.
3）Train only newly added layers while freezing all other layers in order not to modify their weights during backpropagation.
4）Test trained model performance.
## Contact me
If you have any questions or comments about my projects, feel free to reach out via email at akshay.madar@gmail.com
# A simple nodejs express app
This project was generated with [Node.js](https://nodejs.org/en/) version v14.16.0.
## Development server
Run `npm install` for installing dependencies.
Run `npm start` for starting development server.
Navigate your browser window towards http://localhost:3000/. The app will automatically reload if you change any of source files.
xcode-templates
================
A collection of Xcode templates for iOS development.
## Installation
“`
$ git clone https://github.com/mbogh/xcode-templates.git
$ cd xcode-templates/
$ make install_templates
“`
## Usage
1. Open XCode.
2. Create a new project (���+Shift+N).
3. Select the `iOS` tab and choose your preferred template.
4. Click on next, fill in your details and create the project.
### Available Templates:
* **Single View Application**
* A basic single view application with Storyboard support.
* **Master-Detail Application**
* A master-detail application with Storyboard support.
* **Tabbed Application**
* An app that uses UITabBarController to manage multiple view controllers using storyboards
* **Empty Project**
An empty project without
