Given developer's data, predicts the salary using Boston House Price dataset with python & jupyter-notebook IDE, a web based Machine Leaning App.

Regression model to predict flight fares

This is a Python-based news classifier that uses machine learning to classify news articles into different categories such as business, sports, crime, and science. The model is built using the natural language processing (NLP) library, NLTK, and the scikit-learn machine learning library.

This deals with EDA and building various ML models using sklearn: KNeighborsRegressor DecisionTreeRegressor RandomForestRegressor,AdaBoostRegressor LinearRegression, Ridge,Lasso, etc and HP using randomCV and deploy the same in localhost via flask

This end-to-end machine learning project is focused on predicting medical insurance price using regression.

Loan Default Detector App built with XGBoost, FastApi, Docker and Streamlit

Developed a machine learning model using the Cleveland Heart Disease dataset to accurately predict heart disease presence in individuals based on 14 medical attributes. Conducted comprehensive data exploration, visualization, model selection, training, hyperparameter tuning, and evaluation. Identified crucial features to aid diagnosis and treatment

This machine learning project aims to recommend the most suitable crop to grow based on various soil and environmental factors. The model takes into account the following input data:

Regression model to predict IPL scores

Welcome to this repository! This project uses data science and machine learning to predict retail product sales prices. It includes a robust data preprocessing pipeline, handles outliers, and features an ensemble model. With real-time predictions through a user-friendly Flask app and API, it's a game-changer for businesses seeking accurate sales.

Comparing the Logistic Regression Model and Random Forest Classifier

Collection of code covering various topics in Machine Learning

This project is a machine learning model for predicting customer churn in the telecom sector. It's designed to identify customers at risk of leaving a telecom service, enabling proactive retention efforts.

Tensorflow Lab

The "Python Machine Learning (2nd edition)" book code repository and info resource

Website to locate Patrick

Predicting prices of next day stock using LSTM

Mangrove Classification from Aerial Imagery

Investigated factors that affect the likelihood of charity donations being made based on real census data. Trained and tested several models, and selected the best one based on F-score and efficiency.

A number of notebooks showing practical examples of data science