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SarveshChandra / Machine-Learning-and-Artificial-Intelligence-Case-Studies

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AIML Projects

statistics computer-vision deep-learning random-forest feature-extraction batch-normalization neural-networks supervised-learning pca recommendation-system ensemble-learning object-detection adaboost decision-trees feature-engineering unsupervised-learning support-vector-machines object-recognition knearest-neighbor-algorithm gradient-boosting
  • Updated May 30, 2024
  • Jupyter Notebook

astonglen / churn-prediction

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To build a classification system to predict whether a customer will churn or not based on the IBM Telecom Data from Kaggle. Technically, it is a binary classifier that divides clients into two groups-those who leave and those who do not. The classifier will be built using bagging algorithms like Random Forest, boosting algorithms & Neural Networks

neural-network random-forest xgboost logistic-regression adaboost knn-classification
  • Updated May 21, 2024
  • Jupyter Notebook

RezaSaadatyar / Machine-Learning-in-Python

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This repository contains functions/codes related to different methods of machine learning for classification and clustering in python.

random-forest clustering naive-bayes machine-learning-algorithms classification logistic-regression support-vector-machine adaboost decision-trees gaussian-mixture-model principal-component-analysis k-nearest-neighbours singular-value-decomposition k-means-clustering multilayer-perceptron independent-component-analysis t-distributed-stochastic feature-selection-methods feature-extraction-methods linear-discrimination-analysis
  • Updated May 19, 2024
  • Jupyter Notebook

Agoons20 / Portfolio-Mgmt-And-Machine-Learning-in-Finance

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Investment Analysis and Asset Mgmt, Time Series Analysis & Forecasting, Machine Learning in Finance & Causal Inference Methods

pca-analysis logistic-regression adaboost decision-trees autocorrelation svm-model kmeans-clustering gradient-boosting dbscan-clustering knn-regression knn-classification textual-analysis efficient-frontier extra-trees granger-causality-analysis vector-autoregression-models randon-forest capital-market-line autocorrelation-moving-average
  • Updated May 9, 2024
  • Jupyter Notebook

amir-rs / Prediction-of-osteoporosis-with-ML-and-NN

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This project aims to detect bone fractures using machine learning and neural networks. It utilizes various machine learning models including AdaBoost, CatBoost, Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, and LightGBM and and neural networks.

machine-learning random-forest svm neural-networks xgboost lightgbm logistic-regression adaboost gradientboosting catboost
  • Updated Apr 29, 2024
  • Jupyter Notebook

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