This project used machine learning concept to predict disease on their symptoms
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Updated
Jul 2, 2018 - Jupyter Notebook
This project used machine learning concept to predict disease on their symptoms
Machine Learning Recipes is a series of videos from Google Developers covering codes (python) and insights about ML.
Predicting the incidents raised by the customer
This repo contains the Minor Project 1 named Fasal Fusion: An Algorithmic Approach to Transform Crop Recommendations
Predict the ratings of players based on various attributes, like crossing, free_kick_accuracy, ball_control.
This model predicts the risk of your credit account based on the set of values enteres
Contains FP Tree and FP Growth, decision tree implementation and small programs developed using Tensorflow
A machine leaning based loan classifier using many classification techniques then, uses them trying to find the best parameters for each one of them hence, compares between them according to various metrics
A machine learning application, deployed using Flask, is designed to identify the presence of heart disease in patients by analyzing various medical features.
Data Science final project
A parser for scikit-learn exported text models to execute in the Java runtime.
Football Player Transfer Prediction Using Different Classifiers
Predicting Tags for Stack Overflow
Scala Library for extracting useful information from trained Spark Model (DecisionTreeClassificationModel)
Loan Pay off Prediction Using selected Machine Learning Algorithms
Function Transformer is part of feature engineering it converts probability density function to normal distribution
This is a small and simple Machine Learning project. The front-end is Java (PersonalityTest) and the back-end is Python (PersonalityServer). The user uses the JavaFX Application (client) to connect to the back-end using Python Flask where a Machine Learning model is trained and it is used to predict the user's personality (out of 16 different pe…
Contribution to Titanic Project
Create a model to predict if a customer will leave the bank.
This is my own code. The data set is taken from Kaggle competitions. Here is the link: https://www.kaggle.com/competitions/titanic
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