Test/benchmark regression and comparison system with dashboard

Data science investigations into the mechanics of the world's greatest role playing game

Basic statistical modelling examples.

Perform statistics on Mixpanel API data

Conditional Associative Logic Memory

Implementation of a Partial Least Squares Regressor

Football player rating analysis and position recommendation

Java·Applied·Geodesy·3D - Least-Squares Adjustment Software for Geodetic Sciences

Julia's HPC command for automatic feature/model selection using all-subset-regression approaches

ALGORITHM TRADING AND STOCK PREDICTION USING MACHINE LEARNING

The main aim of this project is to tell that the certain project will be successful or it will fail by applying machine learning algorithm. In this , LOGISTIC REGRESSION is used to determine the success of the project by splitting the data into training and testing models and predicting a successful one.

Predicting Prices for the products to be sold on Black Friday in US using Regression Analysis, Feature Engineering, Feature Selection, Feature Extraction and Data analysis - Data Visualizations.

Predicting the stock market opening values using Deep learning's Model Recurrent Neural Networks which is a very powerful model.

A Linear Regression model to predict the car prices for the U.S market to help a new entrant understand important pricing variables in the U.S automobile industry. A highly comprehensive analysis with detailed explanation of all steps; data cleaning, exploration, visualization, feature selection, model building, evaluation & MLR assumptions validity.

It is from a kaggle competition where we have to predict the future sales using Machine Learning or Deep Learning. It is a Advanced Regression Problem where Statistics and time series analysis is also required. This problem can be very well done by Deep Learning's Model Recurrent Neural Networks.

Winning solution of the Kaggle KDD BR 2018 machine learning competition

It is a Competition for Regression Challenge held by Kaggle, It is based on a Avito Dataset whose size is 123GB which can be accessed from Kaggle, I have done Data Pre-processing, feature engineering, feature extraction, data visualization, machine learning, stacking and boosting

Fast estimation of generalized linear models with high dimensional categorical variables in Julia

Performing the Financial Analysis on Historic Stock Market Data such as calculating various risks, returns,etc.

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.

🚗 Solving the problem of predicting the price of a used car using Sklearn's supervised machine learning techniques integrated with Spark-Sklearn library.

Linear Regression Analysis on Wine data - Pre-processing data, Exploratory Data Analysis, Building a model, Check assumptions, Goodness of fit and Compare with different methods.

Predicting Algae's age using different attributes and Machine Learning Algorithms for Regression Analysis.

Using Machine Learning Algorithms for Regression Analysis to predict the sales pattern and Using Data Analysis and Data Visualizations to Support it.

Regression with Python & R.

This repository contains only projects using regression analysis techniques. Examples include a comprehensive analysis of retail store expansion strategies using Lasso and Ridge regressions.

Predict the shirt size by images

High Accuracy of NBA game outcome & team performance prediction and outstanding player detection.

A Flask web application to scrape and analysis Amazon Reviews

Sensor data of a renowned power plant has given by a reliable source to forecast some feature. Initially the work has done with KNIME software. Now the goal is to do the prediction/forecasting with machine learning. The idea is to check the result of forecast with univariate and multivariate time series data. Regression method, Statistical method.