abis330/footbalysis

Player Rating System in Soccer using Machine Learning

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fosetorico/pH_level_forecasting

pH Level Forecasting of Well Water Samples in Malawi, Conducted by Leeds Beckett University

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razamehar/Predicting-Bank-Customer-Churn

This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.

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UdbhavSrivastava/Stroke-Predictive-Modeling

This study uses predictive analytics to detect stroke risk factors early, aiming to reduce occurrences. By analyzing risk factors with machine learning, it uncovers patterns and correlations. Models such as Logistic Regression, KNN, Decision Trees, Random Forest, and Neural Network.

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fitria-dwi/Credit-Risk-Prediction

This project aims to identify patterns that indicate if a person is unlikely to repay the loan or labeled as bad risk and build a predictive model to predict loan risk from applicants.

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tomartushar/Credit-Card-Fraud-Detection

An ensemble of machine learning models for detecting fraudulent credit card transactions, utilizing advanced techniques for feature selection, data imbalance handling, and hyperparameter tuning.

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princessEmilyy/ML-project-diabetes-

Project for applied classical ML course at the Weizmann institute

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dardenkyle/Credit-Card-Users-Churn-Prediction

Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it

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ShyrenMore/sem08

A compilation of codes for SMA, DC, ADS

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vrbabu9000/bank_termDeposit_predictor

Given a highly biased banking dataset of 45k entries and 17 variables wherein I was able to filter out good predictors for modelling and further balance the bias in the dataset using SMOTETomek method. The bias removal was approached in two different ways and the pros and cons of both methods are stated. The end result is a logistic regression model with 81% accuracy and striking a sharp balance between precision and recall with the former having a slight upper hand.

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Srijha09/Detection-Of-Fraudulent-Claims-In-Medical-Insurance

Checking whether the claims are fraud or non-fraud based on various attributes using Medicare Dataset

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haniye6776/outlier-detection

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Akshadachavan02/classification-_algo

As the there are couple of classification algorithms in supervised machine learning so some of them as here..

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dsvirenpai/KNN_Forensic_Science_Classifying_GlassType

KNN in Forensic Science: Classifying Glass Evidence for Criminal Investigations

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ajitsingh98/E-commerce-product-buyer-session-prediction

Predict whether customer purchase a product or not in a session

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Theofilusarifin/Diabetes-Disease-Prediction

Diabetes, characterized by high blood sugar levels due to ineffective insulin production or usage, poses serious health risks if not managed. Deep Learning offers promising avenues for diabetes management.

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sumitkutty/Respondent-Group-Classification

6-class classification problem on a large dataset using machine learning.

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ggjay9/Application-Flow-Identification

Classify applications using flow features with Random Forest and K-Nearest Neighbor classifiers. Explore augmentation techniques like oversampling, SMOTE, BorderlineSMOTE, and ADASYN for better handling of underrepresented classes. Measure classifier effectiveness for different sampling techniques using accuracy, precision, recall, and F1-score.

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RampageousRJ/Spam-Email-Detection

Detects if an outgoing mail would be classified as spam or not

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nickjlupu/Credit-Risk

Supervised scikit-learn machine learning models using several sampling techniques.

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garick161/alpha_bank_cup_challenge_final

Разработка алгоритма привлечения новых клиентов банка

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ceenaa/fraud_detection

Credit Card fraud detection

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epicure24/Classifier-for-highly-unbalanced-data

This repo represents all the resampling techniques needed to achieve better results in highly unbalanced or skewed data that has 77 % of data in one class and rest in others.

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MsTao-68/Debt-Churn-Data-Analysis

使用比赛方提供的脱敏数据，进行客户信贷流失预测。

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aryamaansaha/employeeattrition

This repository contains code that was used to predict employee attrition using machine learning methods.

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letstryy/LeadScoring

Predictive Lead Scoring using clickstream data

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ishreya09/Parkinson-Prediction-Model

We train different models and apply techniques gives us better evaluation metrics, and find out the best model which works the best for Parkinson's Prediction System

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desininja/Employee-Attrition-analysis

To know the main reasons for attrition of employees.

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shivamverma26/Credit_Card_Fraud_Detection

This project is dedicated to advanced machine learning techniques for credit card fraud detection, providing a solution to protect financial institutions and their clients by predicting and preventing fraudulent transactions in a highly imbalanced dataset.

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Devanshu0502/Road-Severity-Classification

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aniass/Spam-detection

Spam detection in SMS messages with BERT model and Machine Learning algorithms

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skinan/Improved-Sampling-and-Feature-Selection-to-Support-Extreme-Gradient-Boosting-For-PCOS-Diagnosis

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

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Angienoelhaverly/Credit_Risk_Analysis

Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

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manjugovindarajan/RENEWIND-predictive-maintenance-cost-maintenance-usingML

The aim is to decrease maintenance cost of generators used in wind energy production machinery. This is achieved by building various classification models, accounting for class imbalance, tuning on a user defined cost metric (function of true positives, false positives and false negatives predicted) & productionizing model using pipelines

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SarangGami/Bank-Marketing-Effectiveness-Prediction-supervised-learning

The main objective is to build a predictive model that predicts whether a new client will subscribe to a term deposit or not, based on data from previous marketing campaigns.

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abhijha3011/Techniques-To-Handle-Imbalanced-Data

Different Techniques to Handle Imbalanced Data Set

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yrnigam/UCI_Credit_Card_Default

This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.

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Safaa-p/Fraudulent-Insurance-Claims-Detection

Different models to detect if a claim is fraudulent or not

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HRX101/Employee-satisfaction

Explainable AI for Predictive Analytics on Employee Satisfaction

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j1bulbul/Term_Deposit_Subscription_Predictor_ML

Supervised Learning, Binary Classification ML problem utilised to determine if an individual would subscribe to a term deposit based on various marketing characteristics

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joanitolopo/eval-sampling-methods

🔍 Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset.

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sharmasapna/credit-card-fraud-detection

Code to detect credit card fraud detecton

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theheisenberg10/Facebook-Post-Sentiment-Analysis-NLP-using-RNNs

Predicting sentiment of Facebook posts (Appreciation, Complaint, or Feedback) using RNNs

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AI-14/lumpy-skin-disease-classification

Binary classification of lumpy skin disease (imbalanced dataset) using ML algorithms in addition to oversampling/undersampling techniques.

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Niranjan-stat/Loan-Default-Prediction

This project aims to provide a robust and accurate solution to the loan defaulting problem, helping financial institutions make more informed lending decisions and reduce their risk exposure.

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nazgol-nikravesh/NSGRT

Source code for "Cross-project Defect Prediction with An Enhanced Transfer Boosting Algorithm"

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lucashomuniz/Project-6

Comparing Machine Learning Algorithms for Credit Risk Analysis in Banking

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krishna-aditi/credit-card-fraud-detection-imbalanced-dataset-problem

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technisekai/sentiment-analysis-of-new-halal-logo

:round_pushpin: Final project of Telkom Institute of Technology Purwokerto

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M-Hashemzadeh/RCSMOTE

RCSMOTE: Range-Controlled Synthetic Minority Over-sampling Technique for handling the class imbalance problem

Size: 6.28 MB - Last synced: 12 months ago - Pushed: 12 months ago - Stars: 3 - Forks: 0

rikhuijzer/Resample.jl

An implementation of SMOTE

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FataiAzeez/stroke_prediction_rf_svm_lr

This repo evaluates Logistic Regression, Random Forest, and Support Vector Machine models for predicting stroke risk. Implemented in Python, the project includes data pre-processing, model training, and performance metric calculations

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Palemravichandra/customer-segmentation

customer segmentation of insurance comapany

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Candida18/ADS_SMA

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yourssincerely/eurovision

This project provides a comprehensive analysis of the Eurovision Song Contest, with insights derived from both traditional statistical methods and machine learning techniques.

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avr2002/credit-card-default-prediction

This a classic Credit Card Default Prediction project where based on customer profile we want to predict whether the borrower is likely to default in the next 2 years or not having a delinquency of more than 3 months.

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urmipandya123/Road_Severity_Classification

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bharatkulmani/Dry-Bean

Project is about predicting Class Of Beans using Supervised Learning Models

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sahidul-shaikh/credit-card-fraud-detection

Machine learning model for Credit Card fraud detection

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AvinandanBose/Credit-Card-Fraud-Detection-Machine-Learning-

Credit Card Fraud Detection using Python and Machine Learning.

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rochitasundar/Predictive-maintenance-cost-minimization-using-ML-ReneWind

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.

Language: Jupyter Notebook - Size: 15.2 MB - Last synced: over 1 year ago - Pushed: over 2 years ago - Stars: 7 - Forks: 2

rupeshsure/Obstructive-Sleep-Apnea-Project

Obstructive Sleep Apnea classification with help of numerical data set which having the physical body characteristics with the help of machine learing

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ankit-kothari/Credit-Risk-Analysis

Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods

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MoinDalvs/Assignment_SVM_Forest_Fire_Prediction

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LokeshSreenathJ/Bankruptcy-Prediction---Analytics

Built XGBoost Classifier using SMOTE technique and Hyper-Parameter Tuning

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AayushSameerShah/SMOTE

This small repository contains the SMOTE implementation from scratch.

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TzeLun/SMOTE

A minority oversampling method for imbalance data set

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rishawsingh/Credit-Card-Fraud-Detection

System to tell apart the transaction was from the real user who owns the credit card or the transaction was from the stolen credit card.

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BibhuPrasadPanda97/Credit-Card-Default-Risk---AmExpert-CodeLab

Competition conducted by American Express on HackerEarth Platform to Predict Credit Card Defaulters by building Machine Learning Models for the given data.

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chinmaysharmacs10/University_Recommender

A model that recommends University based on details of an applicant.

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gulabpatel/Handle_Imbalance

Language: Jupyter Notebook - Size: 190 KB - Last synced: about 1 year ago - Pushed: over 3 years ago - Stars: 7 - Forks: 0

Wamuza1/Credit_Risk_Analysis

Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

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yandi-farinango/CreditRiskModel

Training XGBoost ML model to detect credit default risk. Used SMOTE technique for handling unbalanced data. Evaluation of model trained on unbalanced dataset vs SMOTE generated dataset

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GianRomani/ML_course_homework

Code and reports of the two homework for the Machine Learning course (Winter 2020)

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Olatohun/campaign-response

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shumph10/Credit_Risk_Analysis

Established a supervised machine learning model trained and tested on credit risk data through a variety of methods to establish credit risk based on a number of factor

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tamannanazmin/Datathon

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sidharth-ds/Credit-Card-Default-prediction

EDA ---> Balancing the Dataset (SMOTE) ---> Feature engineering ---> Modelling with Hyperparameter Tuning

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gaetanoantonicchio/DataMining-2

Repository for "Data Mining - Advanced Topics and Applications" projects exam.

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Toshani/Credit-Card-Fraud-Detector

Mini project repository where we have implemented Credit card fraud detection using encoding, SMOTE-ing and KNN.

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kalyaniasthana/CS273A_project_diabetes

Course Project for CS273A: Machine Learning at UCI

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bkraffa/desafioclassificacao

Desafio de Classificação do curso de Data Science e Machine Learning da Tera. Em um dataset de mais de 6 milhões de operações bancárias tinhamos um objetivo de realizar a previsão de fraudes. Fazendo uso de um processo de feature engineering que acrescentou 20 features ao modelo, combinado com um resampling feito através do método SMOTE. Para o treinamento criamos três modelos: Regressão Logística, Random Forest e XGBoost. Esses dois últimos performaram com precisão e recall superiores a 99%.

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akibzaman/Soil-nutrient-web

Prediction of basic soil nutrients (phosphorus, potassium, boron, calcium, magnesium and manganese) using reflectance from Hyperspectral Satellite Images (HSI).

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alexberndt/machine-learning-sandbox

Collection of machine learning algorithms ...

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rohitdwivedula/enzyme-classification

Predict the enzyme class of a given FASTA sequence using deep learning methods including CNNs, LSTM, BiLSTM, GRU, and attention models along with a host of other ML methods.

Language: Python - Size: 76.8 MB - Last synced: over 1 year ago - Pushed: almost 3 years ago - Stars: 7 - Forks: 1

shaunwang1350/CreditLoans_MachineLearning

Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. I employed Machine Learning techniques to train and evaluate models with unbalanced classes. I used imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. I also evaluated the performance of these models and made a recommendation on whether they should be used to predict credit risk.

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izzypatrick21/cuisines

classification of asian and indian cuisines. A good example for resampling imbalance dataset for a classification project using interpolation. I have also included deploying machine learning model using Onnyx.

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lucacarniato/predicting-customer-churn-kaggle-competition

A solution to the Kaggle competition "Predicting Churning customers" (https://www.kaggle.com/sakshigoyal7/credit-card-customers)

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86lekwenshiung/Classification-Modelling-Projects

Classification Projects for balanced and imbalanced datasets

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jharvey09/Risky_Business_Peer_To_Peer_Lending

In this project, I will use credit risk models to assess the credit risk using peer-to-peer lending. Algorithms such as SMOTE, Naive Random Sampling, etc.

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ritwikkanodia/Privacy-Preserving-Machine-Learning

Maintaining the privacy of local server data in a federated learning framework using differential privacy by TensorFlow Privacy Library.

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WilliamSMendes/multiclass_students_classifier

A model for multiclass calssification, label ech student profile in Saint Paul School for predict the future profiles.

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jesussantana/Sampling

Perform Data Sampling with Python

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NataliaVelasquez18/credit-risk

The purpose of this study is to recommend whether PureLending should use machine learning to predict credit risk. Several machine learning models are built employing different techniques, then they are compared and analyzed to provide the recommendation.

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Rizwan-Hasan/Improved-Sampling-and-Feature-Selection-to-Support-Extreme-Gradient-Boosting-For-PCOS-Diagnosis Fork of skinan/Improved-Sampling-and-Feature-Selection-to-Support-Extreme-Gradient-Boosting-For-PCOS-Diagnosis

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

Size: 386 KB - Last synced: over 1 year ago - Pushed: about 3 years ago - Stars: 0 - Forks: 0

Serfati/covid_bloodtest

❤️ 🩸 Blood test classifier for infected COVID-19 patients using xgb, catboost, rf and lr

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AkashSDas/predict-hr-stay-or-leave

Sampling unbalanced dataset using SMOTE and creating a classifier to classify if a HR will stay or leave.

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jasminedogu/DS4002-Sentiment-Analysis

A sentiment analysis using SPAM/HAM Text Classification data using Support Vector Machines. Utilizes different variations of the Synthetic Minority Oversampling Technique (SMOTE-SVM, SMOTE-KNN).

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rtharungowda/cascade-cup-2020

Cascade Cup Data Science Hackathon, Solve a real-world Data Science Challenge by Trell

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rachellimce/Project-4-West-Niles-Virus

DSI 16 Project 4, Predicting West Niles Virus

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