🌍 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.

Stochastic gradient descent with model building

A targeted resource for mastering Scikit-Learn, featuring practice problems, code examples, and interview-focused machine learning concepts in Python. Covers model building, evaluation, and preprocessing techniques to excel in data science interviews.

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.

PyR@TE 3

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.

The Heart Disease Prediction project aims to predict the likelihood of heart disease using machine learning techniques.

No description provided.

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.

The goal of this project is to build multiple linear regression models for the prediction of car prices.

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 project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.

A neural network model builder, leveraging a neuro-symbolic interface.

Tree-level completions of LNV operators for neutrino-mass model building

Analyzing and predicting Google's stock prices through detailed data exploration and advanced LSTM models. This project involves data preprocessing, creating time-series sequences, constructing and training LSTM networks, and evaluating their performance to forecast future stock prices utilizing Python and Machine Learning libraries.

💰 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.

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.

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.

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.

Supervised Binary Classifier For IoT Data Stream

NLP: HMMs and Viterbi algorithm for POS tagging

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.

Tool demonstrating building credit risk models

Build an RL (Reinforcement Learning) agent that learns to play Numerical Tic-Tac-Toe. The agent learns the game by Q-Learning.

The objective of this project is to recognize hand gestures using state-of-the-art neural networks.

Stroke prediciton with EDA, data preprocessing, model building and sampling

Applying knowledge of image processing and deep learning to create a convolutional neural network (CNN) for facial keypoints (eyes, mouth, nose, etc.) detection.