This is a project where use the Random Forest Classifier and XGBoost Machine Learning Techniques to held predict what passengers survived the sinking of the Titanic.
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Updated
Apr 7, 2023 - Jupyter Notebook
This is a project where use the Random Forest Classifier and XGBoost Machine Learning Techniques to held predict what passengers survived the sinking of the Titanic.
Data transformation using linear regression and cross validation (MAE)
Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.
Linear regression is also a type of machine-learning algorithm more specifically a supervised machine-learning algorithm that learns from the labeled datasets and maps the data points to the most optimized linear functions.
This repository utilizes time series analysis to predict natural gas prices, aiding informed decisions in the energy market. Through meticulous data preprocessing, visualization, and ARIMA modeling, it provides accurate forecasts. With regression and interpolation techniques, it offers deeper insights for stakeholders, enabling proactive strategies
Different modeling techniques like multiple linear regression and random forest, etc. will be used for predicting the cement compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy.
BenchMetrics Prob: Benchmarking of probabilistic error performance evaluation instruments for binary-classification problems
Machine Learning course of Piero Savastano 1: LinearRegression, mean_absolute_error, train_test_split
Comparing Ridge and LASSO model to find the best accuracy for Home Price Prediction
Build Linear Regression and Mean Absolute Error Models with Python for Machine Learning
TensorFlow deep regression model predicting bicycle rental.
Templates of statistical and DL forecasting on some synthetic data and lastly predicting sunspot using kaggle sunspot dataset.
Using Collaborative Filtering predicting Movie Rating and K-nearest Neighbours & SVM algorithms for Number ClassificationNumber Classification
The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.
A study about Regression algorithms
A data mining project to analyse Airbnb's data of Berlin for the year 2020 using KDD
to observing mean absolute error with decision tree regression for train and test
This is a project where I use the Random Forest Regression and XGBoost Machine Learning Techniques to held predict the Sales Price of Houses..
We are going to use the different classification algorithms to create a model to predict rain in Australia. This project was done as a part of the Honors portion of the IBM Machine Learning Course on Coursera.
Intrusion Detection System for MQTT Enabled IoT.
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