one-hot-encoding

Wongi-Choi1014/Korean-OCR-Model-Design-based-on-Keras-CNN

Korean OCR Model Design(한글 OCR 모델 설계)

zouguojian/Travel-time-prediction

When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time

d-misra/Multi-label-movie-poster-genre-classification

Keras implementation of multi-label classification of movie genres from IMDB posters

iAmKankan/Natural-Language-Processing-NLP-Tutorial

NLP tutorials and guidelines to learn efficiently

yammadev/cbrs

Case-based Reasoning (CBR) System

labrijisaad/Predicting-Student-Admissions-with-Neural-Networks-using-Python

We tried in this notebook to predict student admissions to graduate school at UCLA based on three pieces of data.

Abdelrahmanrezk/nlp_in_actions

Natural Language Processing In Actions

gogundur/Classification

Classification - Term Deposit Opening Decision

jayachandru001/Heart-Failure-Prediction-

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%.

Hoda233/Arabic-Text-Diacritization

Using Natural Language Processing techniques, to predict diacritics of an Arabic Text.

Shahrukh2016/Retail_Sales_Prediction

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.

avestura/scikit-heart-disease-classifier

💚 A heart disease classifier using 4 SVM kernels and decision trees, with PCA, ROC, pruning, grid search cv, confusion matrix, and more

mdnuruzzamanKALLOL/One-Hot-Encoding-User-Input-LR

Feature Engineering

tanvirnwu/Feature-Engineering--Python

Abdulrahmankhaled11/House-Price-Prediction

Using Regression algorithms for predict houses prices for a dataset

AIprototype/LogisticRegression-CreditApproval-Python

Create a machine learning model using logistic regression that can predict credit card approvals from the described dataset.

bharatkulmani/Dry-Bean

Project is about predicting Class Of Beans using Supervised Learning Models

billy-enrizky/predicting_income_project

Practicion of Machine Learning, One-hot encoding, Random Forest, Hyperparameter Tuning, Grid Search CV, With accuracy of 84.92%!

griffinbran/Real-Estate-Statistical-Modeling

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.

mdnuruzzamanKALLOL/One-Hot-Encoding

Feature Engineering

MUHAMMADAKMAL137/IMDB-Dataset-Classification-using-Pre-trained-Word-Embedding-with-GloVec-6B

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

prushh/data-analytics-exercises

Exercises for the "Data Analytics" course, University of Bologna (2021/2022)

sayo2rule/bank-churning-with-machine-learning

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).

vaitybharati/Assignment-06-Logistic-Regression

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

arbaazkhaan/FIFA-Dataset-Refinement

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.

BradyFisher/Housing-Prices-Machine-Learning-Project

This is a project where I use the Random Forest Regression and XGBoost Machine Learning Techniques to held predict the Sales Price of Houses..

BradyFisher/Machine-Learning-Titanic-Project

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.

mohadeseh-ghafoori/Coursera-Deep-Learning-Specialization

parsa-abbasi/intro-to-nlp

An Introduction to Natural Language Processing (NLP)

PraveenHurakadli/Heart-Disease-Prediction-Using-PCA

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.

sef007/Neural-Network-Email-Classifier-Numpy-Only

Neural Network using NumPy, V1: Built from scratch. V2: Optimised with hyperparameter search.

sef007/NN-Numpy-Only-HOG-Feature-Extraction-and-ML-Library-Integration

Digit Recognition Neural Network: Built from scratch using only NumPy. Optimised version includes HOG feature extraction. Third version utilises prebuilt ML libraries.

shimolina-polina/processing-of-data

Processing of data gaps, coding of categorical features, data scaling.

Skygers/Multiclass-U-Net-for-liver-tumor-segmentation

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)

Tikhon-Radkevich/DynamicGridworld

Here, you'll find my solution to the Dynamic Gridworld Sales Prediction challenge