LorenzoBottaccioli

LorenzoBottaccioli's Stars

• NeuroDiffGym/neurodiffeq

  A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

  Language:Python687269290

• mathLab/PINA

  Physics-Informed Neural networks for Advanced modeling

  Language:Python3671310864

• anujdutt9/Feature-Selection-for-Machine-Learning

  Methods with examples for Feature Selection during Pre-processing in Machine Learning.

  Language:Jupyter Notebook364300163

• GDAOSU/LOD2BuildingModel

  SAT2LoD2: Automated LoD-2 Model Reconstruction from Satellite-derived DSM and Orthophoto

  Language:Python175101132

• mlech26l/ode-lstms

  Code repository of the paper Learning Long-Term Dependencies in Irregularly-Sampled Time Series

  Language:Python1098627

• sharmaroshan/Insurance-Claim-Prediction

  In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis.

  Language:Jupyter Notebook364041

• henze-research-group/MODRLC

  The Advanced Controls Test Bed (ACTB) is a virtual buildings test bed that interfaces external controllers to high-fidelity Spawn of EnergyPlus models.

  Language:Modelica268199

• paulxshen/neural-ode-cartpole

  Language:Julia23107

• TUM-PIK-ESM/TUM-Dynamics-Lecture

  Lectures, code and material for the Modelling and Machine Learning of Dynamical Systems in Julia lecture at Technical University Munich

  Language:Jupyter Notebook8200

• petnez9/daylight_estimation_tool

  A repository created for all the software development that will be used for the thesis.

  Language:Python31

• darshandv/Pandas-tutorials

  Introduction to pandas a python library that is extensively used in Data Pre-processing.

  Language:Jupyter Notebook2100

• JigyasaSachdeva/Caravan-Insurance-Policy-Buyers

  The dataset is owned and supplied by Dutch data mining company Sentiment Machine Research. The problem statement is to classify customers as potential buyers of the Caravan Insurance Policy based on historical data of existing buyers. The dataset comprises customer’s socio-demographic, product ownership and insurance statistics data.

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• polmoya/rutesWikiloc

  Pràctica 1: Web Scraping (Tipologia i cicle de vida de les dades)

  Language:Python2

• robinjo78/solarBAG

  MSc thesis Geomatics on computing solar potential for large CityJSON datasets

  Language:Jupyter Notebook20

• theshreyansh/Insurance-Data-Analysis

  Exploratory data analysis using python

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• 1998MANIKAR/Insurance-Claim-Fraud-Detection

  Data Trained

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• aamanpandey/Analysis-of-Insurance-Dataset

  Medical Cost Personal Datasets

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• AyushMalyanDS/Insurance-Claims-Case-Study

  DATA EXPLORATORY ANALYSIS AND HYPOTHESIS TESTING FOR INSURANCE CLAIMS DATA

  Language:Jupyter Notebook1

• cheeyim/ds-class-7-linear-regression

  Data Science with Python - Class 7

  Language:Jupyter Notebook137

• hassanteymoori/Insurance-claims-over-cars

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• jimevansv/Florida-Insurance-Project

  Exploratory Data Analysis

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• Michael-Gkotsis/us-medical-insurance-analysis

  Personal analysis on us medical insurance dataset.

  Language:Jupyter Notebook1201

• Mokashaa2000/Medical-Insurance-Cost-Prediction-using-Machine-learning

  Here in this project, I will use regression analysis to predict medical insurance cost for people in different regions, and based on several aspects like : Smoking, Number of children, BMI...etc.

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• praveen130/Insurance_claim_Prediction

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• shubha-jyoti/Medical-cost-prediction

  Predicting individual medical costs billed by health insurance company based on 'age','sex','smoking habit', 'bmi' and 'residing region'

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• stevenrhart/pet-insurance-customer-segmentation

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• thayab136/Insurance-Claim-Fraud-Detection

  Problem Statement: Business case: Insurance fraud is a huge problem in the industry. It's difficult to identify fraud claims. Machine Learning is in a unique position to help the Auto Insurance industry with this problem. In this project, you are provided a dataset which has the details of the insurance policy along with the customer details. It also has the details of the insurance policy along with the customer details. It also has the details of the accident on the basis of which the claims have been made. In this example,you will be working with some auto insurance data to demonstrate hoe you can create a predictive model that predicts if an insurance claim is fraudulen or not.

  Language:Jupyter Notebook1

• vaishnavibhavsar/Data-Analysis-with-Python

  The project is about performing analysis on a given dataset. My dataset was about automobile insurance claim policies. It is completely implemented in python

  Language:Jupyter Notebook1

• vigneshjmurali/US-Adult-and-Caravan-Insurance-Policy-Analysis

  Predicted potential customers of caravan insurance policy with highest probability in purchasing insurance. Also proved best ML models for dataset & Classified if income will exceed $50k/year using ML models & Logistic outperformed

  Language:Jupyter Notebook1

• wingbender/trail_scraper

  Web scraper to extract trails from www.wikiloc.com

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