prathameshtari /
Predicting-Football-Match-Outcome-using-Machine-Learning
Football Match prediction using machine learning algorithms in jupyter notebook
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yalarcon99 / repository
Python algorithm used to solve Travel Salesman problem based on the Artificial Intelligence course taught by prof. L. Castellanos. Coded and written by Yithzak Alarcón based on Artificial Intelligence course. In partnership with C. Sierra. NOTE: Algorithms are in pure Python format created for Jupyter Notebook.
Python algorithm used to solve Travel Salesman problem based on the Artificial Intelligence course taught by prof. L. Castellanos. Coded and written by Yithzak Alarcón based on Artificial Intelligence course. In partnership with C. Sierra. NOTE: Algorithms are in pure Python format created for Jupyter Notebook.
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prathameshtari /
Football Match prediction using machine learning algorithms in jupyter notebook
anubhavanand12qw /
The coding has been done on Python 3.65 using Jupyter Notebook. This program fetches LIVE data from TWITTER using Tweepy. Then we clean our data or tweets ( like removing special characters ). After that we perform sentiment analysis on the twitter data and plot it for better visualization. The we fetch the STOCK PRICE from yahoo.finance and add it to the data-set to perform prediction. We apply many machine learning algorithms like (random forest, MLPClassifier, logistic regression) and train our data-set. Then we perform prediction on untrained data and plot it with the real data and see the accuracy.
dr-mushtaq /
A complete A-Z guide to Machine Learning and Data Science using Python. Includes implementation of ML algorithms, statistical methods, and feature selection techniques in Jupyter Notebooks. Follow Coursesteach for tutorials and updates.
shahkaran76 /
This repository shows how to deploy YOLO object detection algorithm in tensorflow using python version 3, specifically in Jupyter Notebook.
rajchandran006-ops /
RFD Classification Machine Learning project developed using Python and Jupyter Notebook. This project includes data preprocessing, exploratory data analysis, feature engineering, and implementation of multiple classification algorithms such as Logistic Regression, Random Forest, SVM, KNN, and Naive Bayes for prediction and accuracy evaluation.
PyMLVizard /
Interactive machine learning algorithm visualisation using Python Jupyter notebooks