Hospital ReadmissionLast Updated on May 3, 2021
Hospital readmissions of diabetic patients are expensive as hospitals face penalties if their readmission rate is higher than expected and reflects the inadequacies in health care system. For these reasons, it is important for the hospitals to improve focus on reducing readmission rates.
We have to Identify the key factors that will influence readmission for diabetes and to predict the probability of patient readmission.
A leading hospital in the US is suddenly seeing increase in the patient readmission in less than 30 days. This is serious concern for the hospital as it may indicate insufficient treatment or diagnosis when the patient was admitted first and later released under clean bill of health. Hence it is in Hospital’s interest to support their diagnosis by a better predictive model which we are going to build.
Here the objective is: Classify the patients treated by this hospital into two primary categories:
· Readmitted within 30 days
· Not readmitted
The dataset chosen is that available on the UCI website which contains the patient data for the past 10 years for 130 hospitals. The code has been written in Python using different libraries like scikit-learn, seaborn, matplotlib etc. Different machine learning techniques for classification and regression like Logistic regression, Random forest etc .have been used to achieve the objective.
Python Snake GameLast Updated on May 3, 2021
This game reminds everyone their childhood memories.
In this snake game, the player has to move the snake to the fruit in order to eat it. The score will increase once the fruit is eaten. Also, the length of the snake will increase if the snake eats the fruit. The game will get over if the snake touches itself.
The turtle and random modules are used in this game project. So as to install these libraries, simply type “pip install turtle” and “pip install random” on the command prompt.
Turtle library allows us to create pictures, diagrams in a virtual form whereas random module gives the value between the given range of it.
There are 3 functions defined in this game which is “change”, “inside function”, and “move” function. In change function, the x-axis and y-axis are defined. In inside function, the logic of the game is written and in the move function, movement to the snake is given.
There are 4 keys mentioned in the code “right, left, up, down”.
If the player presses the right key, the snake will move to right direction, If the player presses the left key , the snake will move to left direction, If the player presses the up key , the snake will move to upward direction, If the player presses the down key , the snake will move to downward direction and if the snake touches itself the game will get over.
A Review On Weather Forecasting Techniques Using Machine LearningLast Updated on May 3, 2021
Weather depicts the atmospheric conditions of a particular place at a particular time. The basic weather elements comprise of temperature, wind, pressure, cloudiness and humidity. Every day, the Meteorological Department prepares weather maps for the upcoming day with the help of the data obtained from various weather stations around the world. Weather forecasts help in taking measures in advance in case of the probability of bad weather and in planning your day ahead.
Different instruments are used to measure various weather elements like, a thermometer is used to measure the temperature, whereas, a barometer is used to measure pressure. Similarly, a wind vane is used to find the direction of wind and a rain gauge is used to measure the amount of rainfall. Thus, with the help of the data collected through these instruments we get the weather forecast in the form of weather charts.
In order to decrease so much manual labour, these weather forecasting techniques are now getting replaced with machine learning models that can predict future weather quite accurately with the help of previously collected data. In this report, we are discussing some of the weather forecasting techniques that are most-likely to be used in order to get accurate weather predictions result. Herein we are comparing the results of the various models, just to get the best results.
Keywords: Weather Forecasting, ARIMA, Holt Linear, Holt Winter, Stationarity, Dickey- Fuller
Dice SimulatorLast Updated on May 3, 2021
Python offers various packages to design the GUI, i.e. the Graphical User Interface. Tkinter is the most common, fast, and easy to use Python package used to build Graphical User Interface applications. It provides a powerful Object-Oriented Interface and is easy to use. Also, you develop an application; you can use it on any platform, which reduces the need of amendments required to use an app on Windows, Mac, or Linux.
It’s a simple cube with numbers from 1 to 6 written on its face. The simulation is the making of computer model. Thus, a dice simulator is a simple computer model that can roll a dice for us.
The first step is importing the required module where we import Tkinter which is used to make GUI applications and also the random module to generate random numbers.
The next step is Building a top-level widget to make the main window for our application here we will build the main window of our application, where the buttons, labels, and images will reside. We also give it a title by title() function.
The third step is designing the buttons:
Here, we use pack() to arrange our widgets in row and column form. The ‘BlankLine’ label is to skip a line, whereas we use ‘HeadingLabel’ label to give a heading.
The ‘rolling_dice’ function is a function that is executed every time a button is clicked. This is attained through the ‘command=rolling_dice’ parameter while defining a button.
Then ‘root.mainloop()’ is used to open the main window. It acts as the main function of our program.
We have successfully developed a cool application – Dice Rolling Simulator in Python. Now, you can just click on a button and get your next number.
House Price PredictionLast Updated on May 3, 2021
Machine learning Regression model on House price data set
- Kaggle competition dataset on the house price prediction
- Apply Exploratory data analysis on the dataset.
- Create a Machine learning regression model on the dataset.
- Machine learning algorithms used are Decision Tree Regression, Random Forest Regression, Support vector regression, and XGboost Regression.
Here's a brief version of what you'll find in the data description file.
SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict. MSSubClass: The building class MSZoning: The general zoning classification LotFrontage: Linear feet of street-connected to property LotArea: Lot size in square feet Street: Type of road access Alley: Type of alley access LotShape: General shape of property LandContour: Flatness of the property Utilities: Type of utilities available LotConfig: Lot configuration LandSlope: Slope of property Neighborhood: Physical locations within Ames city limits Condition1: Proximity to the main road or railroad Condition2: Proximity to the main road or railroad (if a second is present) BldgType: Type of dwelling
HouseStyle: Style of dwelling OverallQual: Overall material and finish quality OverallCond: Overall condition rating YearBuilt: Original construction date YearRemodAdd: Remodel date RoofStyle: Type of roof RoofMatl: Roof material Exterior1st: Exterior covering on house Exterior2nd: Exterior covering on house (if more than one material) MasVnrType: Masonry veneer type MasVnrArea: Masonry veneer area in square feet ExterQual: Exterior material quality ExterCond: Present condition of the material on the exterior Foundation: Type of foundation BsmtQual: Height of the basement BsmtCond: General condition of the basement BsmtExposure: Walkout or garden level basement walls BsmtFinType1: Quality of basement finished area BsmtFinSF1: Type 1 finished square feet BsmtFinType2: Quality of second finished area (if present) BsmtFinSF2: Type 2 finished square feet BsmtUnfSF: Unfinished square feet of basement area TotalBsmtSF: Total square feet of basement area Heating: Type of heating HeatingQC: Heating quality and condition CentralAir: Central air conditioning Electrical: Electrical system 1stFlrSF: First Floor square feet 2ndFlrSF: Second floor square feet LowQualFinSF: Low quality finished square feet (all floors) GrLivArea: Above grade (ground) living area square feet BsmtFullBath: Basement full bathrooms BsmtHalfBath: Basement half bathrooms FullBath: Full bathrooms above grade HalfBath: Half baths above grade Bedroom: Number of bedrooms above basement level Kitchen: Number of kitchens KitchenQual: Kitchen quality TotRmsAbvGrd: Total rooms above grade (does not include bathrooms) Functional: Home functionality rating Fireplaces: Number of fireplaces FireplaceQu: Fireplace quality GarageType: Garage location GarageYrBlt: Year garage was built GarageFinish: Interior finish of the garage GarageCars: Size of garage in car capacity GarageArea: Size of garage in square feet GarageQual: Garage quality GarageCond: Garage condition PavedDrive: Paved driveway WoodDeckSF: Wood deck area in square feet OpenPorchSF: Open porch area in square feet EnclosedPorch: Enclosed porch area in square feet 3SsnPorch: Three season porch area in square feet ScreenPorch: Screen porch area in square feet PoolArea: Pool area in square feet PoolQC: Pool quality Fence: Fence quality MiscFeature: Miscellaneous feature not covered in other categories MiscVal: $Value of miscellaneous feature MoSold: Month Sold YrSold: Year Sold SaleType: Type of sale SaleCondition: Condition of sale.