Topic: sklearn-metrics Goto Github
Some thing interesting about sklearn-metrics
Some thing interesting about sklearn-metrics
sklearn-metrics,Machine learning model
User: 2512ayushcad
sklearn-metrics,This is a python project for building a linear regression model that is used to predict used car prices from a given dataset using machine learning.
User: ab-aruneswaran
sklearn-metrics,this repo will include all my work regarding NLP
User: ahmed-ai-01
sklearn-metrics,We used various techniques to train and evaluate a model based on loan risk. We used a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
User: alaa-aleryani
sklearn-metrics,
User: anas436
sklearn-metrics,
User: anas436
sklearn-metrics,Create a model that helps choose the region with the highest profit margin.
User: angelicavelez
sklearn-metrics,Multi Linear Regression Assignment - 5
User: arif-rehman
sklearn-metrics,ExcelR_Assignment---Forecasting---Assignment---18
User: arif-rehman
sklearn-metrics,ExcelR_Assignment---KNN---Assignment---13
User: arif-rehman
sklearn-metrics,ExcelR_Assignment---Naive-Byes---Assignment---12
User: arif-rehman
sklearn-metrics,KnowGenius an AI Chatbot who's a General Knowledge Genius!
User: arko-sengupta
sklearn-metrics,KMeans Clustering of data using Sklearn library, numpy and Pickle data
User: ashirsat96
sklearn-metrics,In this problem i have tried to explain how XGB algorithm works in case of classification. I have also stated the accuracy score at the end for our XGBClassifier model. The confusion matrix has also been shown for the same. I have used the Kaggle Dataset - Titanic Survivors csv file.
User: ashishyadav24092000
Home Page: https://github.com/ashishyadav24092000/XGBClassifier_titanicSurvivors
sklearn-metrics,A collection of Deep Learning And AI projects using Tensorflow and Keras
User: benyanko
sklearn-metrics,Implementation of Support Vector Machine, and Random Forest Model using sklearn
User: chrssz
sklearn-metrics,Data Science: Machine Learning analysis of B2B website Visits and Purchase Patterns
User: dpassos91
sklearn-metrics,Use various techniques to train and evaluate a model based on loan risk.
User: hamim-hussain
sklearn-metrics,Predict if a woman will develop breast cancer_Ensemble Techniques_Stacking
User: hohasby
sklearn-metrics,Machine Learning Project to create an AI capable of predicting property's rental prices.
User: hugo-hattori
sklearn-metrics,📔 This repository delves into Logistic Regression for loan approval prediction at LoanTap. It covers data preprocessing, model development, evaluation metrics, and strategic business recommendations. Explore model optimization techniques such as confusion matrix, precision, recall, Roc curve and F1 score to effectively mitigate default risks.
User: kasimuthuveerappan
Home Page: https://loantap.in/
sklearn-metrics,Segmentação de usuários de E-Commerce com base em seus comportamentos e características usando 3 técnicas de machine learning: Random Forest, XGBoost e Redes Neurais.
User: marlevek
sklearn-metrics,Reduce the time that cars spend on the test bench. Work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Mercedes-Benz’s standards.
User: mezbans
sklearn-metrics,Dummy TSA Forecast dashboard using statsmodel, sklearn and streamlit
User: miguelsiloli
sklearn-metrics,
User: miirshe
sklearn-metrics,Run three different classification algorithms for explaining whether region's economies grew by more than 5% based on the data provided. Standard goodness measures for classification algorithms also included.
User: mohidulhaquetushar
sklearn-metrics,This repository consists of prediction of the football team winners using historical data with the help of machine learning algorithms
User: mounishvatti
sklearn-metrics,The "Gold Price Prediction" project focuses on predicting the prices of gold using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, Random Forest Regressor, and others, this project provides a comprehensive solution for accurate price estimation.
User: myoussef885
sklearn-metrics,A movie recommendation system based on the concept of collaborative filtering
User: ohoadit
sklearn-metrics,[College Course] - Course: BITS F312 Neural Network and Fuzzy Logic
User: prakhr
sklearn-metrics, A classification model to predict those who will likely accept the offer of a new personal loan , by analyzing the previous historical campaign's customer behaviour data.
User: pratiksha2712
sklearn-metrics,The aim to decrease the maintenance cost of generators used in wind energy production machinery. This is achieved by building various classification models, accounting for class imbalance, and tuning on a user defined cost metric (function of true positives, false positives and false negatives predicted) & productionising the model using pipelines.
User: rochitasundar
sklearn-metrics,Data Science - Naive Bayes Work
User: saikrishnabudi
sklearn-metrics,Data Science - Random Forest Work
User: saikrishnabudi
sklearn-metrics,Use decision trees to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.
User: shanuhalli
sklearn-metrics,Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.
User: shanuhalli
sklearn-metrics,Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.
User: shanuhalli
sklearn-metrics,Classify the Size Categorie using SVM - the burned area of the forest (Small, Large) and Prepare a classification model using SVM for salary data.
User: shanuhalli
sklearn-metrics,Anticipation des besoins en consommation électrique de bâtiments (OpenClassrooms | Data Scientist | Projet 4)
User: smellyarmure
sklearn-metrics,The aim of this project is: 1.Perform Text Classification using Multinomial Naive Bayes 2. Implement Naive Bayes from scratch for Text Classification. 3. Compare Results of self implemented code of Naive Bayes with one in Sklearn. dataset used is 20_newsgroups
User: swagatika15
sklearn-metrics,HAM10000 Skin Lesion Classification
User: tmfreiberg
sklearn-metrics,Classifying the person as male or female based on hairs, forehead size, nose shape, lips shapes, ect. using ML models
User: vaidehinaik11
sklearn-metrics,Classification of person as underweight, Normal weight, overweight or obese using different ML Models.
User: vaidehinaik11
sklearn-metrics,walmart stores weekly sales prediction using regression techniques.
User: vaidehinaik11
sklearn-metrics,Recommendation-Engine
User: vaitybharati
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