A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

A New, Interactive Approach to Learning Python

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.

Bank customers churn dashboard with predictions from several machine learning models.

Karma of Humans is AI

Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification

This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.

Hyperparameters-Optimization

Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets

Obtaining meaningful results from the data set using the model trained with machine learning methods.

I have built a Model using Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California.

Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.

An anytime implementation of scikit-learn GridSearchCV

iThome 13th-ironman (2021) - Data Science Learning Roadmap about Python

Build a classifier to classify transport using sift and svm

Fake News Detection System for detecting whether news is fake or not. The model is trained using "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. Link for dataset: https://arxiv.org/abs/1705.00648.

Classify pictures by architectural style and recognize objects with CNNs and YOLO

In this project, I employ several supervised algorithms to accurately predict an individual income using data collected from the 1994 U.S. Census. We implement various testing procecures to choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data.

Predicting the severity of accident