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Vicky2-code / repository
Predicts whether a person will like a particular movie based on features like genre, age, and viewing habits. Uses machine learning classification algorithms. 📁 Type: Jupyter Notebook 📌 Use Case: Recommender system concept using supervised ML.
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rileypredum /
A machine learning project trying to predict whether or not a Kickstarter campaign succeeds. Final report in PDF as well. Includes original dataset in csv and Jupyter Notebook
32/100 healthonyxfuzzx /
An end-to-end machine learning project that predicts whether a telecom customer will churn. Includes a complete Jupyter Notebook for data analysis and model training, plus a Flask web application with a modern dark-themed UI for real-time predictions.
50/100 healthsushantpattanaik /
Objective: The repository is a learning exercise to: Apply the fundamental concepts of machine learning from an available dataset Evaluate and interpret and justify our results and interpretation based on observed dataset in Jupyter notebook The analysis is divided into four sections, saved in juypter notebooks in this repository Identifying the problem and Data Sources Exploratory Data Analysis Pre-Processing the Data Build model to predict whether breast cell tissue is malignant or Benign Part_1: Identifying the problem and Getting data. Aim:Identify the types of information contained in our data set We'll used Python modules to import external data sets for the purpose of getting to know/familiarize ourself with the data to get a good grasp of the data and think about how to handle the data in different ways. Part_2: Exploratory Data Analysis Aim: Explore the variables to assess how they relate to the response variable In this notebook, we'll get familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) Part_3: Pre-Processing the data Aim: Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model. We'll use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. Part_4: Predictive model using Support Vector Machine (svm) Aim: Construct predictive models to predict the diagnosis of a breast tumor. We'll construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). we'll also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. Part_5: Optimizing the Support Vector Classifier Aim: Construct predictive models to predict the diagnosis of a breast tumor. We'll aim to tune parameters of the SVM Classification model using scikit-learn.
YunchaoYang /
Heart-Disease-Prediction-using-Machine-Learning Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate. The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras' I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not. Machine Learning algorithms used: Logistic Regression (Scikit-learn) Naive Bayes (Scikit-learn) Support Vector Machine (Linear) (Scikit-learn) K-Nearest Neighbours (Scikit-learn) Decision Tree (Scikit-learn) Random Forest (Scikit-learn) XGBoost (Scikit-learn) Artificial Neural Network with 1 Hidden layer (Keras) Accuracy achieved: 95% (Random Forest) Dataset used: https://www.kaggle.com/ronitf/heart-disease-uci
60/100 healthAhmad-Indragiri /
This repository contains a Jupyter notebook titled Credit Card Customer Churn Prediction. The notebook uses a credit card customer dataset with the goal of performing analysis and building a predictive model to determine whether a customer will stop using their credit card (churn) or not.
28/100 healthmax0907github /
I'm building a Loan Approval Prediction Model using Jupyter Notebook. Use machine learning techniques to predict whether a loan application will be approved or not based on various factors such as applicant income, loan amount, credit history, etc.
28/100 health