claudiamartinez14/Loan-Default-Prediction-Machine-Learning-Project

The "Potential Customers Prediction" project uses machine learning to predict which customers are likely to make a purchase. It involves data cleaning, exploratory analysis, and building models to identify potential buyers based on demographics and behavior. The model's performance is evaluated using metrics like accuracy and precision.

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Davityak03/Ridge-and-Lasso-Regression

Implented ridge and lasso regression by understanding the use of parameters

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Seghelicious/Cars45

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Lefteris-Souflas/Modern-Slavery-Analysis

Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.

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GunturWibawa/SpotifyPopularityProbe

In the digital music era, understanding artist popularity on Spotify is vital. This project taps into Spotify's data, analyzing key factors driving artist prominence. Through our insights, we illuminate what sets successful artists apart in this dynamic platform.

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shahriar-rahman/Netflix-Customer-Retention-using-GPR

Forecasting Netflix Customer Retention based on Gaussian Process Regression

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shanuhalli/Assignment-KNN

Prepare a model for glass classification using KNN and Implement a KNN model to classify the animals in to categorie.

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shanuhalli/Assignment-Decision-Trees

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.

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shanuhalli/Assignment-Random-Forest

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.

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uzunb/house-prices-prediction-LGBM

This repo has been developed for the Istanbul Data Science Bootcamp, organized in cooperation with İBB and Kodluyoruz. Prediction for house prices was developed using the Kaggle House Prices - Advanced Regression Techniques competition dataset.

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Akash1070/Machine-Learning-

Learning Machine Learning Through Data

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amit-timalsina/California-Housing-Price-Prediction

I have built a Model using the Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California.

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PriyeshDave/Site-Energy-Intensity-Predictor

In this project, a regression-based performance prediction model was developed to estimate building energy consumption based on simplified façade attribute information and weather conditions.

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PriyeshDave/Accident-Severity-Predictor

RTA severity predictor is an application which predicts the severity of road traffic accident, so as to pave the way for improving the safety level of road traffic.

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murtadapy/iris-flower-classification

Analyze and Build a machine learning (ML) model on the Iris Flower dataset

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murtadapy/disaster-response-pipelines

Disaster response project that implements data engineering tactics to classify messages sent during a real-world disaster. This project uses ETL and ML pipelines and uses the Flask library to deploy the final result on a website.

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prithvimurjani/ML-Utility-Repo

A repo packed with common and important machine learning techniques and algorithm implementations using sklearn.

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existentialplantperson/Week_14

Week 14 - Multiple Linear Regression and Logistic Regression

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Ankit-Kumar-Saini/Machine-Learning-Lab-Plaksha

Programming assignments covering fundamentals of machine learning and deep learning. These were completed as part of the Plaksha Tech Leaders Fellowship program.

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ofir-frd/Prediction-of-Music-Genre

Classify music into genres by classical machine learning models

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copev313/Predicting-Credit-Card-Approvals

A machine learning model built to predict if a credit card application will get approved.

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Faisal-AlDhuwayhi/Disaster-Response-Pipeline

Building Machine Learning and ETL Pipelines to categorize emergency messages based on the needs communicated by the sender

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Zauverer/Support_Vector_Machine

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niklas-joh/Machine-Learning-Project-Supervised-Learning

Identify the most efficient machine learning model to identify potential donors. Project covers Linear Regression, Perceptron Algorithm, Decision Trees, Naive Bayes, Support Vector Machines and Ensemble Methods.

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