shubhanshu1995/Advanced-Regression-using-Ridge-and-Lasso

We are required to build a regression model using regularization in order to predict the actual value of the prospective properties and decide whether to invest in them or not.

Language: Jupyter Notebook - Size: 1.44 MB - Last synced: 23 days ago - Pushed: over 2 years ago - Stars: 0 - Forks: 2

shubhanshu1995/Bike-Sharing-Multiple-Linear-Regression-Assignment

This assignment is a programming assignment wherein we have to build a multiple linear regression model for the prediction of demand for shared bikes.

Language: Jupyter Notebook - Size: 1.7 MB - Last synced: 23 days ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

jeroenvansweeveldt/predicting_depression-machine_learning_exercise

Data and code for a machine learning exercise in which I predict the development of depression.

Language: Jupyter Notebook - Size: 1.24 MB - Last synced: about 1 month ago - Pushed: about 1 month ago - Stars: 0 - Forks: 0

AutoViML/featurewiz

Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

Language: Python - Size: 10.9 MB - Last synced: about 1 month ago - Pushed: about 1 month ago - Stars: 544 - Forks: 85

Ashutosh27ind/logisticRegressionTelecomChurnCaseStudy

Data Science Project: To predict which factors leads to churn and find customers who are likely to churn.

Language: Jupyter Notebook - Size: 1.98 MB - Last synced: about 1 month ago - Pushed: almost 4 years ago - Stars: 0 - Forks: 0

Ashutosh27ind/creditAmountPredictionHackathon

Skillenza Upgrad DataScience Hackathon : Rank #18 in Leaderboard

Language: Jupyter Notebook - Size: 2.06 MB - Last synced: about 1 month ago - Pushed: almost 4 years ago - Stars: 0 - Forks: 0

shromana98/Diabetes-Prediction

The primary aim of this project is to accurately identify individuals at risk of diabetes based on different features.

Language: Jupyter Notebook - Size: 2.39 MB - Last synced: about 2 months ago - Pushed: 2 months ago - Stars: 0 - Forks: 0

DataScienceVishal/Lead_Scoring

Lead_Scoring Case Study using Logistic Regression

Language: Jupyter Notebook - Size: 808 KB - Last synced: 3 months ago - Pushed: 3 months ago - Stars: 0 - Forks: 0

DataScienceVishal/Bank_Customer_Behaviour

Bank Customer Behaviour Prediction

Language: Jupyter Notebook - Size: 355 KB - Last synced: 3 months ago - Pushed: 3 months ago - Stars: 0 - Forks: 0

RimTouny/Dynamic-DNS-Traffic-Analysis-for-Data-Exfiltration-Detection-with-Kafka

Crafting static and dynamic models for data exfiltration detection via DNS traffic analysis. Static model trained on batch data, while dynamic model simulates a continuous stream. Rigorous analysis, feature engineering, and model training conducted. Implementation part of AI for Cyber Security Master's assignment at the University of Ottawa, 2023.

Language: Jupyter Notebook - Size: 4.73 MB - Last synced: 5 months ago - Pushed: 5 months ago - Stars: 1 - Forks: 0

Dipti028/BikeSharingAssignment

To build a multiple linear regression model for the prediction of demand for shared bikes.

Language: Jupyter Notebook - Size: 1.87 MB - Last synced: 4 months ago - Pushed: 6 months ago - Stars: 0 - Forks: 0

domingosdeeulariadumba/IMDb250_stars

IMDb 250 analysis (Stars vs Gross ROI/Rating)

Language: Jupyter Notebook - Size: 7.03 MB - Last synced: 5 months ago - Pushed: 6 months ago - Stars: 0 - Forks: 0

LisaLi525/Feature-Analysis-for-Classification

This framework is a versatile toolkit for data analysis across domains, offering robust data processing, feature selection, predictive modeling, and visualization tools adaptable to various datasets.

Language: Python - Size: 6.84 KB - Last synced: 6 months ago - Pushed: 6 months ago - Stars: 0 - Forks: 0

MihoRosenberg/Bangladesh-Flood-Guard

Predict precipitation to mitigate flood damage in Bangladesh

Language: Jupyter Notebook - Size: 14.9 MB - Last synced: 4 months ago - Pushed: 6 months ago - Stars: 0 - Forks: 1

letandrade/alavancagem_vendas

O objetivo deste case é trazer insights sobre o que um time de uma empresa do ramo de varejo poderia fazer para alavancar as vendas via análise de dados e machine learning.

Language: Jupyter Notebook - Size: 5.42 MB - Last synced: 6 months ago - Pushed: about 1 year ago - Stars: 0 - Forks: 0

akashkriplani/bike-sharing-assigment

This project tackles BoomBikes' post-Covid revenue decline by predicting shared bike demand after the lockdown. A consulting company identifies key variables impacting demand in the American market. The goal is to model demand, aiding BoomBikes in adapting its strategy to meet customer expectations and navigate market dynamics.

Language: Jupyter Notebook - Size: 4.08 MB - Last synced: 5 months ago - Pushed: 7 months ago - Stars: 0 - Forks: 0

rakibhhridoy/MachineLearning-FeatureSelection

Before training a model or feed a model, first priority is on data,not in model. The more data is preprocessed and engineered the more model will learn. Feature selectio one of the methods processing data before feeding the model. Various feature selection techniques is shown here.

Language: Python - Size: 1000 Bytes - Last synced: 8 months ago - Pushed: almost 4 years ago - Stars: 1 - Forks: 1

brunomontezano/predicting-functional-impairment

Análise de dados relacionada a predição de prejuízo funcional em sujeitos com transtornos de humor.

Language: R - Size: 646 KB - Last synced: 8 months ago - Pushed: almost 2 years ago - Stars: 0 - Forks: 0

Palak-15/Housing-Case-Study-with-RFE

Consider a real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc. Essentially, the company wants — To identify the variables affecting house prices, e.g. area, number of rooms, bathrooms, etc. To create a linear model that quantitatively relates house prices with variables such as number of rooms, area, number of bathrooms, etc. To know the accuracy of the model, i.e. how well these variables can predict house prices.

Language: Jupyter Notebook - Size: 40 KB - Last synced: 8 months ago - Pushed: almost 4 years ago - Stars: 1 - Forks: 0

DavidPanduro/financial_fraud_detection

Previsão de Fraude Financeiro

Language: Jupyter Notebook - Size: 3.56 MB - Last synced: 8 months ago - Pushed: 8 months ago - Stars: 1 - Forks: 0

domingosdeeulariadumba/Penguins_Classification

Multiclass classification model of penguins species.

Language: Python - Size: 380 KB - Last synced: 9 months ago - Pushed: 9 months ago - Stars: 1 - Forks: 0

vasatodorovic/ToxicityOfMolecules

Project on course "Data Mining 2"

Language: Jupyter Notebook - Size: 4.75 MB - Last synced: 9 months ago - Pushed: 9 months ago - Stars: 0 - Forks: 0

ashomah/Bike-Sharing-in-Washington-DC

Bike Sharing in Washington D.C.

Language: Python - Size: 8.34 MB - Last synced: 5 months ago - Pushed: about 5 years ago - Stars: 1 - Forks: 1

domingosdeeulariadumba/MarketingCampaignPrediction

A telemarketing model to predict campaign subscriptions in a portuguese bank institution.

Language: Python - Size: 940 KB - Last synced: 9 months ago - Pushed: 9 months ago - Stars: 1 - Forks: 0

mayur200/BoomBikeAssignment

The Bike-Sharing Demand Prediction Project aims to develop a predictive model to estimate the demand for shared bikes in the American market for BoomBikes, a bike-sharing provider looking to accelerate revenue post the Covid-19 pandemic. The project involves thorough data exploration and preprocessing.

Language: Jupyter Notebook - Size: 918 KB - Last synced: 5 months ago - Pushed: 10 months ago - Stars: 0 - Forks: 0

sharmashubh08/CaseStudy-TelecomChurn

Objective: To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

Language: Jupyter Notebook - Size: 175 KB - Last synced: 11 months ago - Pushed: 11 months ago - Stars: 0 - Forks: 0

deepakrameshgowda/CREDIT-CARD-FRAUD-DETECTION

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.

Language: Jupyter Notebook - Size: 7.71 MB - Last synced: 11 months ago - Pushed: 11 months ago - Stars: 0 - Forks: 0

SajalSinha/Bike_sharing_demand

Deployed a predictive model using decision tree regressor

Language: Jupyter Notebook - Size: 16.8 MB - Last synced: 11 months ago - Pushed: about 2 years ago - Stars: 0 - Forks: 0

mohd-faizy/feature-engineering-hacks

This repository contains a collection of hacks and tips for feature engineering. It is a great resource for anyone who wants to learn how to improve the performance of their machine learning models.

Language: Jupyter Notebook - Size: 6.77 MB - Last synced: 11 months ago - Pushed: 11 months ago - Stars: 1 - Forks: 0

Vayuputra2401/Heart_Arrythmia_Prediction

A comprehensive ML framework to detect heart disease using the Cleveland dataset

Language: Jupyter Notebook - Size: 2.33 MB - Last synced: 11 months ago - Pushed: 11 months ago - Stars: 0 - Forks: 0

xyrusgallito/student_grade_project

Student grade prediction using different machine learning models

Language: R - Size: 1.47 MB - Last synced: 5 months ago - Pushed: 12 months ago - Stars: 0 - Forks: 0

abhikgupt/Bike-Rental-Prediction-based-on-weather-and-season

Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.

Language: Jupyter Notebook - Size: 1.23 MB - Last synced: about 1 year ago - Pushed: about 1 year ago - Stars: 0 - Forks: 0

bioinfoUQAM/CASTOR_KRFE

Alignment-free method to identify and analyse discriminant genomic subsequences within pathogen sequences

Language: Python - Size: 2.89 MB - Last synced: 10 months ago - Pushed: over 1 year ago - Stars: 9 - Forks: 2

GvHemanth/Bike_Sharing_Linear_Reg

Predicting the variables that effects the revenue of the bike sharing company after a serious drop-fall during the covid-19 pandemic.

Language: Jupyter Notebook - Size: 1.12 MB - Last synced: about 1 year ago - Pushed: about 1 year ago - Stars: 1 - Forks: 0

renatokano/cn-feature-selection-and-dimensionality-reduction

[Codenation] Feature Selection w/ Recursive Feature Elimination (aka RFE) and Dimensionality Reduction using Principal Component Analysis (aka PCA)

Language: Jupyter Notebook - Size: 2.06 MB - Last synced: 10 months ago - Pushed: almost 2 years ago - Stars: 1 - Forks: 0

stxupengyu/SVM-RFE

SVM classification, RFE feature selection

Language: Python - Size: 1.95 KB - Last synced: over 1 year ago - Pushed: over 4 years ago - Stars: 3 - Forks: 2

Xamweis/digi-impact-eco-socio

project for the practice of webscraping, APIs, machine learning, feature selection

Language: Jupyter Notebook - Size: 1.31 MB - Last synced: 5 months ago - Pushed: over 1 year ago - Stars: 2 - Forks: 0

Hameem1/Step-Detection-using-Machine-Learning

Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

Language: Python - Size: 1.16 MB - Last synced: over 1 year ago - Pushed: over 1 year ago - Stars: 8 - Forks: 0

Danfoa/parkinson-progression-prediction-with-speech-tests

Computer Intelligence subject final project at UPC.

Language: Python - Size: 37.7 MB - Last synced: over 1 year ago - Pushed: over 4 years ago - Stars: 5 - Forks: 1

ashomah/HR-Analytics

HR Analytics Dataset

Language: Python - Size: 17 MB - Last synced: about 1 year ago - Pushed: about 5 years ago - Stars: 6 - Forks: 2

labrijisaad/Car-Price-Prediction

Car Price Prediction

Language: Jupyter Notebook - Size: 1.29 MB - Last synced: over 1 year ago - Pushed: over 1 year ago - Stars: 6 - Forks: 1

darshil2848/House-Price-Prediction

House Price Analysis and Sales Price Prediction

Language: Jupyter Notebook - Size: 7.32 MB - Last synced: over 1 year ago - Pushed: over 1 year ago - Stars: 0 - Forks: 0

maremita/CASTOR_KRFE_v1.0

[Features extraction method] You can find the new version of CASTOR_KRFE at https://github.com/bioinfoUQAM/CASTOR_KRFE

Language: Python - Size: 1.94 MB - Last synced: over 1 year ago - Pushed: almost 4 years ago - Stars: 0 - Forks: 0

darshil2848/Bike-Shraing-Demand-Prediction

Booming Bike Sharing Analysis and Demand Prediction

Language: Jupyter Notebook - Size: 6.1 MB - Last synced: 5 months ago - Pushed: almost 2 years ago - Stars: 0 - Forks: 0

iici-psiddineni/ML_Telecom_Churn

Machine Learning Telecom Churn Model

Language: Jupyter Notebook - Size: 31.6 MB - Last synced: about 1 month ago - Pushed: over 5 years ago - Stars: 4 - Forks: 9

nafisa-samia/Automobile-Price-Prediction-using-Linear-Regression

Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.

Language: Jupyter Notebook - Size: 15.7 MB - Last synced: over 1 year ago - Pushed: over 2 years ago - Stars: 3 - Forks: 3

aman9801/multiple-linear-regression-on-housing-dataset

Multiple Linear Regression and with RFE on Housing Dataset

Language: Jupyter Notebook - Size: 372 KB - Last synced: about 1 year ago - Pushed: about 2 years ago - Stars: 0 - Forks: 0

ChaitanyaC22/Telecom-Churn-Prediction

In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. The business objective is to predict the churn in the last i.e. fourth month using the data from the first three months.

Language: Jupyter Notebook - Size: 27.7 MB - Last synced: about 1 year ago - Pushed: almost 3 years ago - Stars: 1 - Forks: 0

ChaitanyaC22/House-Price-Prediction-Project-for-a-US-based-housing-company

The goal of this project is to garner data insights using data analytics to purchase houses at a price below their actual value and flip them on at a higher price. This project aims at building an effective regression model using regularization (i.e. advanced linear regression: Ridge and Lasso regression) in order to predict the actual values of prospective housing properties and decide whether to invest in them or not.

Language: Jupyter Notebook - Size: 3.97 MB - Last synced: about 1 year ago - Pushed: almost 3 years ago - Stars: 2 - Forks: 1

carzynsky/Analytics-and-Data-mining

Language: Jupyter Notebook - Size: 3.92 MB - Last synced: over 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

aditya-mishra25/Linear_Regression_Boom_Bikes

This is a Linear Regression Project, we have created multiple models using different feature selection techniques to predict the future demands for a bike company.

Language: Jupyter Notebook - Size: 1.61 MB - Last synced: over 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

Sneha-Santhosh/Bike-theft-prediction

Data warehouse and analytics project to predict bike theft prediction from TPS data

Language: Python - Size: 4.88 KB - Last synced: over 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

kuntala-c/Automobile-Price-Prediction-using-Linear-Regression Fork of nafisa-samia/Automobile-Price-Prediction-using-Linear-Regression

Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.

Language: Jupyter Notebook - Size: 17.3 MB - Last synced: over 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

anikch/Telecom-churn-analysis-and-prediction

Analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn (usage-based churn) and identify the main indicators of churn.

Language: Jupyter Notebook - Size: 1.69 MB - Last synced: over 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

ashomah/King-County-House-Sales

King County House Sales

Language: R - Size: 819 MB - Last synced: about 1 year ago - Pushed: almost 5 years ago - Stars: 2 - Forks: 0

anikch/Bike-rental-prediction-based-on-env-season

Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.

Language: Jupyter Notebook - Size: 977 KB - Last synced: over 1 year ago - Pushed: over 2 years ago - Stars: 1 - Forks: 0

derekngoh/HDB-Resale-Flat-Valuation

HDB flats resale price prediction. Neural network in Python. Machine learning models in R. Data pre-processing, feature engineering and feature selection mainly in R.

Language: Jupyter Notebook - Size: 5.45 MB - Last synced: about 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

chiranjeevbitm/boombike-Bike-sharing-business-problem

BoomBikes wants to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19.

Language: Jupyter Notebook - Size: 2.92 MB - Last synced: over 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

kbjornson/sentiment-analysis

Sentiment analysis of iPhone and Samsung Galaxy.

Language: R - Size: 542 KB - Last synced: about 1 year ago - Pushed: almost 3 years ago - Stars: 0 - Forks: 0

SmartNamDevoloper/Logistic_Regression

This is a project demonstrating Logistic Regression method using Python. An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.

Language: Jupyter Notebook - Size: 888 KB - Last synced: about 1 year ago - Pushed: over 3 years ago - Stars: 0 - Forks: 0

SmartNamDevoloper/Telecom_Customer_churn_Classification

This project demonstrates building a classification model for imbalanced data. Feature engineering, feature selection and extensive EDA. Comparing of logistic regression, random forest and ADA Boost models are done before finalizing the best model.

Language: Jupyter Notebook - Size: 5.23 MB - Last synced: about 1 year ago - Pushed: about 3 years ago - Stars: 0 - Forks: 0

SmartNamDevoloper/Linear_Regression

A linear regression model to predict demand for a bike based on the different conditions like weather conditions, whether a day is a holiday, weekend, or workday and other conditions.

Language: Jupyter Notebook - Size: 1.26 MB - Last synced: about 1 year ago - Pushed: about 3 years ago - Stars: 0 - Forks: 0

kk-deng/Exoplanets-Classifier-ML

Create machine learning models capable of classifying candidate exoplanets from the raw dataset

Language: Jupyter Notebook - Size: 2.84 MB - Last synced: 5 months ago - Pushed: about 3 years ago - Stars: 0 - Forks: 0

isra-st/London_Bike_Sharing

The goal of this project is to perform an Explorartory Data Analysis with visualization and use a liner regression model to predict the number of bikes rented.

Language: Jupyter Notebook - Size: 84.3 MB - Last synced: 5 months ago - Pushed: about 3 years ago - Stars: 0 - Forks: 0

Upendra-Allagadda/Diabetes-prediction-using-machine-learning

Hospitals contain large databases. We can use that data to discover new useful and potentially life saving knowledge. Here we use datamining especially to predict type 2 diabetes mellitus.Predicting the percentage of chance of occurrence of Diabetes mellitus type 2 with less time complexity and high accuracy.

Language: Jupyter Notebook - Size: 663 KB - Last synced: 12 months ago - Pushed: over 3 years ago - Stars: 0 - Forks: 1

charan89/Car-Pricing-Prediction

Given features of car, training the model to predict the car price.

Language: Jupyter Notebook - Size: 1.55 MB - Last synced: over 1 year ago - Pushed: almost 4 years ago - Stars: 0 - Forks: 0

sakusuma/SalesLeadScoring

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses. The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone.

Language: Jupyter Notebook - Size: 1.33 MB - Last synced: about 1 year ago - Pushed: about 4 years ago - Stars: 0 - Forks: 0

sakusuma/CarPricePrediction

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know: Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

Language: Jupyter Notebook - Size: 1.14 MB - Last synced: about 1 year ago - Pushed: about 4 years ago - Stars: 0 - Forks: 0

mccurcio/anti-cancer-human-modeling

CART, K-Means, Apriori, Adaboost, RFE; models using Anti-cancer peptides vs Human proteins

Language: HTML - Size: 61 MB - Last synced: over 1 year ago - Pushed: over 4 years ago - Stars: 0 - Forks: 0