react_formik

react_hook

react_js

react_native_expo

react_redux

reducer_hook

residential_real_estate_price_prediction_website

The backend part deals with building a machine learning model to predict housing price based on various features like the number of bedrooms in a house, the house has a balcony, or not. We have used several features to give a clear understanding to the users of what kind of house they want. During model building, we will cover almost all data science concepts such as data load and cleaning, outlier detection and removal, feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tunning, k fold cross-validation, etc. The frontend part deals with showing the predicted price based on the value of the features provided by the user. We have used HTML, CSS, javascript to make the UI/UX. Used flask to connect the bridge between the frontend and backend. The website built allows users to enter the to enter values of different features and it will call the python flask server to retrieve the predicted price based on the value of the features. This project helps to maintain transparency among users and also the comparison can be made easy through this model. If a user finds the price of the house at some given website higher than the price predicted by the model, so he can reject that house.

twitter_sentiment_analysis

widhya_internship

I have done bitcoin and stock price prediction using linear regression.