Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
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Updated
Jun 20, 2024 - Python
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
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.
Graphical user interface for designing and simulating model predictive control using MATLAB and the Multi-Parametric Toolbox 3
Fake Music
R package for data cleaning, preliminary data analysis and modeling assessing with visualisation.
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 Mo…
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.
Decision-Tree
Gesture Recognition Python Backend
Customer lifetime value predictions
Red wine quality prediction machine learning model.
Cadivascular Disease Prediction
This repo contains a python script which is a fastapi backend server that can be used for model (Image classification) predictions
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.
Used libraries and functions as follows:
This repo contains python scripts that are needed to deploy a machine learning model behind gRPC running using asyncio.
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
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