There are 1 repository under model-building topic.
🌍 Python package of VTK-based algorithms to analyze geoscientific data and models
A collaborative list of awesome CryoEM (Cryo Electron Microscopy) resources.
Worked on Real Estate Price data analysis by scraping website from www.99acres.com to help the housing domain as well as estimate the sale value of various houses and open plots.
The Heart Disease Prediction project aims to predict the likelihood of heart disease using machine learning techniques.
This project consists of custom built modelling frameworks for pricing equity assets. Through the project's evolution, the framework evolves from a single case Discounted Cash Flow model to an interactive Probability Weighted Discounted Cash Flow model that includes multiple cases, multiple supporting models and is all built in Excel while utilizing the Visual Basic programming language.
The goal of this project is to build an RL-based algorithm that can help cab drivers maximize their profits by improving their decision-making process on the field. Taking long-term profit as the goal, a method is proposed based on reinforcement learning to optimize taxi driving strategies for profit maximization. This optimization problem is formulated as a Markov Decision Process i.e. MDP.
Assignment-04-Simple-Linear-Regression-2. Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.
A neural network model builder, leveraging a neuro-symbolic interface.
This project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.
Tree-level completions of LNV operators for neutrino-mass model building
💰 I’d be walking us through Loan prediction using some selected Machine Learning Algorithms.
Runtime EntityFramework model builder from metadata tables. Provides a static usage at compile time via proxies classes. Created as CRM/ERP core.
Tool demonstrating building credit risk models
The goal of this project is to build multiple linear regression models for the prediction of car prices.
The goal of this project is to build a neural network that takes an MNIST handwritten digit (0-9) image and a random number (digit 0-9) as inputs and returns the predicted class label (0-9) for the input image and its addition (sum) with the input random number as summed output (range 0-18) label as outputs.
Jupyter Notebooks for visualizing and exploring empirical model building. http://charlesreid1.github.io/empirical-model-building
To create a Decision Tree classifier and visualize it graphically, the purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.
Supervised Binary Classifier For IoT Data Stream
In this project, I predict which customers are more likely to respond positively to a bank marketing call by setting up a regular savings deposit or subscribing the term “made_deposit”. Three classification algorithms have been developed in order to predict the target variable. Logistic Regression, Decision Tree and Multi-Layer Perceptron (MLP). The analysis of the project includes Data Summary, Data Preparation, Modelling, Results and Errors using Evaluation Metrics, Confusion Matrices and ROC Curve.
Assignment-04-Simple-Linear-Regression-1. Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regression.
Multi-Linear-Reg
Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model
Stroke prediciton with EDA, data preprocessing, model building and sampling
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.
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.
This repository is dedicated to the AI Amigos team's participation in the Artificial Intelligence (AI) competition with a focus on Data Science.
A Binary Classification Problem Optimized For AU-ROC Curve,. From Data Cleaning to Model Validation, Classifying whether a blight ticket will be paid in time or not, Trained 3 different Classifier on a Highly imbalanced Data provided by Detroit Open Data Portal with around 160000 Tickets.
The fraud identification models were build using Python Scikit-learn machine-learning module.
Web application for logistic regression made using Streamlit.
GUI-based neuronal network model building
Data: Boston Housing Dataset (HousingData.csv) Programming language(s): R Tool(s): RStudio Business problem: To understand the drivers behind the value of houses in Boston and provide data-driven recommendation to the client on how they can increase the value of housing.The Boston housing dataset consisted of 506 observations and 14 variables. Project challenge(s): MEDV (Median value of homes in Boston) was identified as the dependent variable. While the rest, were the independent variables. The goal was to find out which among the independent variables were statistically significant in driving the house prices (MEDV). The dataset consisted of missing values and outliers. Some of the variables had a skewed distribution. There was multicollinearity among few independent variables. Our Approach: Prior to model building, we tidied up our dataset by eliminating the rows that contained missing values. Replacing the missing values with median and mean of those variables were also done. Considering the three approaches, median imputation(replacing missing values with mean) was found to be the best approach. As the dependent variable "MEDV" (median value of houses) was continuous(numerical) in nature, we implemented the Multiple linear regression to build our model. Additional models were built from Decision trees and Random forest. On further investigation, we discovered that the dependent variable had a skewed distribution. By log transformation of this variable, we were able to get a normal distribution. Post transformation, we found out that the model built from Multiple linear regression with log transformed MEDV was the best in terms of MSE (Mean squared error) value and Adjusted R^2. All the assumptions of linear regression were met.
This GitHub repository contains a comprehensive project demonstrating image classification using TensorFlow and Keras on the CIFAR-10 dataset. The project covers various aspects of the machine learning pipeline, including data preprocessing, model building, training, evaluation, and visualization.
In this project Utilizing advanced time series forecasting models, successfully predicted department-wide sales for each store for the upcoming year and Visualizing the data in streamlit GUI.