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Ishan-Kotian / BIL_Project-Life_Expectancy_Prediction

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Life expectancy, an estimate of the number of remaining years of life a person has, is an important consideration for making clinical decisions in primary care. Predicting Life Expectancy helps analyze the average lifespan of the countrymen which helps in making crucial health decisions.

python life countries cross-validation data-visualization gdp data-cleaning alcohol factors kaggle-dataset bil immunization life-expectancy random-forest-regression multiple-linear-regression expectancy regression-model r2score mean-squared-error alcohol-intake
  • Updated May 16, 2021
  • Jupyter Notebook

Saurav0129 / Steel-Industry-Energy-Consumption-Prediction

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The dataset used for this project is taken from the official UCI Machine Learning Repository.

python data-science machine-learning data-mining linear-regression scikit-learn sklearn data-visualization data-analysis ridge-regression support-vector-machines lasso-regression pairplot r2score svr-regression-prediction elasticnet-regression steel-industry-energy-consumption
  • Updated Jun 13, 2023
  • Jupyter Notebook

KasiMuthuveerappan / Jamboree-LinearRegression

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📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

machine-learning linear-regression seaborn mse ridge-regression statsmodels scikitlearn-machine-learning lasso-regression mae scaling-methods train-test-using-sklearn matplotlib-pyplot r2score variance-inflation-factor elasticnet-regression scipy-stats numpy-pandas
  • Updated Jul 9, 2024
  • Jupyter Notebook

syedsharin / Credit-Card-Default-Prediction

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This project provides a performance evaluation of credit card default prediction. Thus different models are used to test the variable in predicting the credit default and we found Random Forest Classifier performs the best with a recall of 0.95 on the test set.

python lime classification-algorithm gradient-boosting random-forest-classifier shap xgboost-classifier r2score model-explainability
  • Updated Mar 21, 2022
  • Jupyter Notebook

sai-manas / Diamond_Price_Prediction

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Diamond Price Predictor - Web Application: Predict diamond prices using various regression models: Linear Regression, Lasso, Ridge, ElasticNet, Decision Tree Regressor, Random Forest Regressor, and KNeighbors Regressor. The chosen Random Forest Regressor, with a remarkable accuracy of 97%, is deployed in a user-friendly Flask app

flask data-science linear-regression python3 ridge-regression lasso-regression random-forest-regressor decision-tree-regression r2score elasticnet-regression kneighborsregressor
  • Updated Jan 18, 2024
  • Jupyter Notebook

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