Topic: one-hot-encoding Goto Github
Some thing interesting about one-hot-encoding
Some thing interesting about one-hot-encoding
one-hot-encoding,The aim of this project is to classify the faces. Olivetti Faces dataset has been used. In this dataset there are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). The βtargetβ for this database is an integer from 0 to 39 indicating the identity of the person pictured. Each of the sample images needs to be classified in the classes ranging from 0 to 39. PCA has been applied to reduce the dimensionality. Then various classification and regression techniques are used with and without using PCA and the accuracy and time taken by the algorithms are recorded. Algorithms used: SVM, KNN, logistic regression, neural networks, linear regression and random forests.
User: 0gaurav
one-hot-encoding,Natural Language Processing In Actions
User: abdelrahmanrezk
one-hot-encoding,Using Regression algorithms for predict houses prices for a dataset
User: abdulrahmankhaled11
one-hot-encoding,Using a dataset provided by Airbnb, analysis and predictions will be made to understand what effects the total price of an Airbnb
User: ahing
one-hot-encoding,Create a machine learning model using logistic regression that can predict credit card approvals from the described dataset.
User: aiprototype
one-hot-encoding,Welcome to the FIFA Dataset Data Cleaning and Transformation project! This initiative focuses on refining and enhancing the FIFA dataset to ensure it is well-prepared for in-depth analysis. The project involves a comprehensive data cleaning process and transformation of key features to improve data quality and usability.
User: arbaazkhaan
one-hot-encoding,π A heart disease classifier using 4 SVM kernels and decision trees, with PCA, ROC, pruning, grid search cv, confusion matrix, and more
User: avestura
one-hot-encoding,This Program is for Prediction of Crop Recommendation based on Rainfall,Humidity,Amount of Potassium and Amount of Nitrogen
User: bharatkulmani
one-hot-encoding,Project is about predicting Class Of Beans using Supervised Learning Models
User: bharatkulmani
one-hot-encoding,This is a project where I use the Random Forest Regression and XGBoost Machine Learning Techniques to held predict the Sales Price of Houses..
User: bradyfisher
one-hot-encoding,This is a project where use the Random Forest Classifier and XGBoost Machine Learning Techniques to held predict what passengers survived the sinking of the Titanic.
User: bradyfisher
one-hot-encoding,Keras implementation of multi-label classification of movie genres from IMDB posters
User: d-misra
one-hot-encoding,
User: entron
one-hot-encoding,Classification - Term Deposit Opening Decision
User: gogundur
one-hot-encoding,Predictive analysis, with feature engineering, and machine learning (ML) algorithms, such as linear regression, applied to predict the final sale price of homes in Ames, IA from 2006-2010.
User: griffinbran
one-hot-encoding,NLP tutorials and guidelines to learn efficiently
User: iamkankan
one-hot-encoding,This project involves training of Machine Learning models to predict the Heart Failure for Heart Disease event. In this KNN gives a high Accuracy of 89%.
User: jayachandru001
one-hot-encoding,We tried in this notebook to predict student admissions to graduate school at UCLA based on three pieces of data.
User: labrijisaad
one-hot-encoding,The goal of this project is to use Natural Language Processing (NLP) techniques to analyze hotel reviews and gain insights into customer opinions and experiences to classify hotel reviews.
User: mahyarsab
one-hot-encoding,Feature Engineering
User: mdnuruzzamankallol
one-hot-encoding,Feature Engineering
User: mdnuruzzamankallol
one-hot-encoding,Analysis of Terry Stops in Seattle
User: melodygr
one-hot-encoding,In this project, I worked with a small corpus consisting of simple sentences. I tokenized the words using n-grams from the NLTK library and performed word-level and character-level one-hot encoding. Additionally, I utilized the Keras Tokenizer to tokenize the sentences and implemented word embedding using the Embedding layer. For sentiment analysis
User: muhammadakmal137
one-hot-encoding,An Introduction to Natural Language Processing (NLP)
User: parsa-abbasi
Home Page: https://parsa-abbasi.github.io/slides/nlp/
one-hot-encoding,Utilizing Principal Component Analysis (PCA) for insightful feature reduction and predictive modeling, this GitHub repository offers a comprehensive approach to forecasting heart disease risks. Explore detailed data analysis, PCA implementation, and machine learning algorithms to predict and understand factors contributing to heart health.
User: praveenhurakadli
one-hot-encoding,Exercises for the "Data Analytics" course, University of Bologna (2021/2022)
User: prushh
one-hot-encoding,Unofficial but extremely useful Label and One Hot encoders.
User: razhoshia
one-hot-encoding,π― The project aims to predict suitable yoga asanas based upon the user defined constraints including benefits, age, contraindication and level, datasets from suggested books were collected and preprocessed
User: richa1711
one-hot-encoding,dataframe library for machine learning
User: rom1mouret
one-hot-encoding,Provides insights on relocating to 2020s hotspot: Texas. Link to project report below.
User: s-vatsal
one-hot-encoding,Credito - Credit Risk Analysis using XGBoost Classifier with RandomizedSearchCV for loan approval decisions.
User: sannketnikam
Home Page: https://credito.pythonanywhere.com
one-hot-encoding,This repository covers my code using regression models to predict if a customer would be exiting a bank or not. It also capture the use classification models to classify if a customer has left the bank or not (binary classification).
User: sayo2rule
one-hot-encoding,Neural Network using NumPy, V1: Built from scratch. V2: Optimised with hyperparameter search.
User: sef007
one-hot-encoding,Digit Recognition Neural Network: Built from scratch using only NumPy. Optimised version includes HOG feature extraction. Third version utilises prebuilt ML libraries.
User: sef007
one-hot-encoding,Revolutionize sales forecasting for Rossmann stores with our high-accuracy XGBoost model, leveraging data analysis, feature engineering, and machine learning to predict sales up to six weeks in advance.
User: shahrukh2016
Home Page: https://linktr.ee/shahrukh2016
one-hot-encoding,A model to classify loan status using ML Classification algorithms.
User: shazam-19
one-hot-encoding,Processing of data gaps, coding of categorical features, data scaling.
User: shimolina-polina
one-hot-encoding,Liver Tumor Detection using Multiclass Semantic Segmentation with U-Net Model Architecture. CT-Scan images processed with Window Leveling and Window Blending Method, also CT-Scan Mask processed with One Hot Semantic Segmentation (OHESS)
User: skygers
one-hot-encoding,Here, you'll find my solution to the Dynamic Gridworld Sales Prediction challenge
User: tikhon-radkevich
one-hot-encoding,Assignment-06-Logistic-Regression. Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no") Attribute information For bank dataset Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no") 8. Missing Attribute Values: None
User: vaitybharati
one-hot-encoding,Exploratory Data Analysis Part-2
User: vaitybharati
one-hot-encoding,Korean OCR Model Design(νκΈ OCR λͺ¨λΈ μ€κ³)
User: wongi-choi1014
one-hot-encoding,Case-based Reasoning (CBR) System
User: yammadev
Home Page: https://github.com/yammadev/cbrs/blob/master/cbrs.ipynb
one-hot-encoding,Character Level Text Generation with RNN, LSTM and GRU. Implemented using PyTorch and trained with Marvel Cinematic Universe(MCU) Dialogue Corpus.
User: z1311
one-hot-encoding,Built various machine learning models for banks to develop effective credit rating
User: zhousrhhh
one-hot-encoding,When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time
User: zouguojian
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