There are 0 repository under label-encoding topic.
Analytical understanding and applying parameter optimization, regression with gradient descent to predict water quality levels across Indian waters.
INSAID Assignment to create a ML model to detect fraud transactions for a financial company.
This project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats
We build a chatbot by implementing machine learning and natural language processing.
[CIKM 2021] Code and dataset for "Label-informed Graph Structure Learning for Node Classification"
Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.
Project is about predicting Class Of Beans using Supervised Learning Models
WiDS Datathon 2020 on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.
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).
the code uses KNN, Gaussian Naive Bayes & SVM to classify images. It preprocesses, normalizes data, applies PCA , computes accuracy, precision etc. It evaluates k-NN using Euclidean distance & cosine similarity, visualizing results with line plots, 3D scatter plots, & confusion matrices to demonstrate classifier performance.
Repo houses the predictive NN model and its associated .py modules
This is an implementation for a DataCamp project: A Visual History of Nobel Prize Laureates. We try to answer the proposed questions and visualize the results.
Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
This classification task is specifically dependent on a video dataset that includes video clips of kill and death scenes from the first-person shooting game “CS Go”. I have used the ResNet-50 model for image classification and then turn it into a more accurate video classifier by employing the rolling averaging method.
Unofficial but extremely useful Label and One Hot encoders.
Credito - Credit Risk Analysis using XGBoost Classifier with RandomizedSearchCV for loan approval decisions.
Database management and data analytics from a car-sharing dataset. The dataset contains information about the customers' demand rate between January 2017 and August 2018.
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.
This sentiment analysis model utilizes a Transformer architecture to classify text sentiment into positive, negative, or neutral categories with high accuracy. It preprocesses text data, trains the model on the IMDB dataset, and effectively predicts sentiment based on user input.
This project focuses on analyzing patient feedback regarding the treatment provided by home healthcare service agencies.
Built various machine learning models for banks to develop effective credit rating
This repository consists of various projects based on Machine Learning and NLP.
This project provides the data based on classification to check if the patient is covid +ve or -ve.
Building predictive models to detect and prevent the fraudulent transactions happening on cerdit cards and debit cards. Implementation of 2nd factor authentication for safe and secure transactions.
This repository contains Machine Learning Classification algorithms implementation
Diamonds Price prediction using Polars and Ski-learn
📶In this repository, we will do feature engineering with Python.
There are lot of things that need to be done on the given dataset before we feed it to the machine, these things come under data preprocessing. In this repository I have tried to explain those things with some examples.
The primary goal of this project is to convert free users of a financial tracking app into paid members. This conversion will be achieved by building a model that identifies users who are unlikely to enroll in the paid version of the app.
In this project, I use 3 machine learning models (CART, Random Forest and ANN) to predict the claim frequency for a travel insurance firm. I also evaluate which of the three models is most suitable for our dataset.
The document classification solution should significantly reduce the manual human effort in the HRM. It should achieve a higher level of accuracy and automation with minimal human intervention.
Feature engineering or feature extraction or feature discovery is the process of extracting features from raw data.
To implement the internal workings of perceptron and testing the accuracy of in train and test dataset.
Heart Risk Level Predicting Regression Model & Web using Feature Engineering and Data Preprocessing :baby_chick:
Content: Machine Learning, Logistic regression steps, Probability matrix, Confusion matrix, Accuracy score, Recall value, Data preprocessing, Label encoding, Scaling the data, Splitting train test data, Running Logistic Regression, Y prediction on test data, Class imbalance, Type 1 & Type 2 errors.