Healtpredec

Last Updated on May 3, 2021

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Healtpredec

Is an end-to-end machine learning project which contains 7 machine learning models to make predictions about user's health. the 5 are machine learning models are for (Breast cancer, stroke, heart disease, liver disease). and the other 2 are deep learning models for malaria and pneumonia prediction. I use flask as a backend and HTML/CSS/Bootstrap and javascript as a frontend.

Services

  • Breast Cancer Prediction
  • Diabetes Prediction
  • Pneumonia Prediction
  • StrokePrediction
  • Malaria Prediction
  • Liver disease Prediction
  • Heart disease Prediction

Notebooks



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Image Classification Using Machine Learning

Last Updated on May 3, 2021

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This is a prototype that shows the given specific image will belong to which category. Here any images can be taken to classify the difference. The main theme is to predict that the given image will belong to which category we had considered.

In this prototype I downloaded images of three different dog breeds named Doberman, golden retriever and shihtzu. The first step is to preprocess data which basically means converting the images into an numpy array and this process named as flattening the image. This numpy array should be the input of the image.


After preprocessing the data, the next step is to check the best suitable parameters for the machine learning algorithm. After getting the parameters, I passed them into the machine learning algorithm as arguments and fit the model. From Sklearn import classification report, accuracy score, confusion matrix which helps us to get brief understanding about our model. The model can be loaded into file using pickle library.


Now the last step is to predict the output. For this I took a input field which takes a URL as an input. The URL should be the image of the dog for which the output is predicted. In the same way we have to flatten the image into a numpy array and predict output for that. The output will show the predicted output that is which breed that the dog belongs to and the image we are checking the output for.


The main theme of this project is to train the computer to show the difference between different classes considered.

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Eda On Sample_Superstore

Last Updated on May 3, 2021

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In this project I have performed Exploratory Data Analysis on a Sample_Superstore Dataset and concluded some of the major key insights which helps the store to generate more revenue. I have used python programming language to perform operations on my dataset. Firstly I identify the missing values , null values and the outliers. I used the libraries to make my work more easier, some of them are pandas, numpy, matplotlib and seaborn . It is must to remove outliers from our dataset because if we don't it will effect our result and produce wrong observations. So to remove them I have used IQR i.e. Interquartile range . With the help of matplotlib and seaborn library I visualize some of my observations with the help of graphs and charts. I have used heatmap to define the co-relation between different features present in our dataset which gives us a brief idea about how one feature is related to other features. I have used different different segments from my dataset for performing analysis and I have concluded some of the parameters like which segment is producing highest profit, loss , discount . Not just finding out the problems I have also mentioned some of the solutions by observing my result in order to increase the profit gain and to reduce the losses faced by the the store. I have performed detailed analysis on this dataset and All these observations that I have performed will definitely help the store to overcome with the problems


More Details: EDA ON SAMPLE_SUPERSTORE

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House Price Prediction

Last Updated on May 3, 2021

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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.


Data Fields

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.

More Details: House Price Prediction

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Portfolio Website

Last Updated on May 3, 2021

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DIFFERENT PAGES IN IT:


  1. HOME PAGE : this is the home page and it greets the viewer and tells what this is about. and on the top of this page we can see the clicks for other pages which are discussed below.