model-predictions

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

Automated Tool for Optimized Modelling

Visualization and Imputation of Missing Values

Graphical user interface for designing and simulating model predictive control using MATLAB and the Multi-Parametric Toolbox 3

Assignment-04-Simple-Linear-Regression-2. Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

Fake Music

Assignment-04-Simple-Linear-Regression-1. Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regression.

R package for data cleaning, preliminary data analysis and modeling assessing with visualisation.

Supervised-ML---Simple-Linear-Regression---Waist-Circumference-Adipose-Tissue-Data. EDA and data visualization, Correlation Analysis, Model Building, Model Testing, Model Prediction.

This Project analyses the carbon footprint of the U.S. commercial sector using three machine learning models. A combination of energy consumption data and carbon dioxide emission data was used to achieve the carbon footprint variable.

This repo contains python scripts that are needed to deploy a machine learning model behind gRPC running using asyncio.

Decision-Tree

Gesture Recognition Python Backend

Applied clustering algorithm on 29 countries to narrow scope of analysis. Time series forecasting of solar energy potential of a country using fbprophet and neural networks.

Customer lifetime value predictions

A machine learning project to predict red wine quality using the Kaggle dataset. It includes data preprocessing, feature engineering, model training (XGBoost with 95.8% accuracy), and deployment via a Flask app on Render, offering an interactive interface for predictions.

This repo contains a python script which is a fastapi backend server that can be used for model (Image classification) predictions

A streamlit web application serving an XGBoost model trained to predict customer churn.

Credit card fraud detection-prediction model

This repo evaluates Logistic Regression, Random Forest, and Support Vector Machine models for predicting stroke risk. Implemented in Python, the project includes data pre-processing, model training, and performance metric calculations