Capstoneproject_house_prices_prediction
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Ml Course | 159 | 2 years ago | 1 | R | ||||||
| Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language | ||||||||||
| Skpr | 89 | 2 months ago | 13 | September 19, 2019 | 3 | gpl-3.0 | R | |||
| Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user. | ||||||||||
| Highcharts_trendline | 77 | 8 years ago | 1 | mit | JavaScript | |||||
| HighCharts demo of scatter plot, including a trend line | ||||||||||
| Machine Learning Matlab | 46 | 9 years ago | mit | Matlab | ||||||
| Matlab implementation of Machine Learning algorithms | ||||||||||
| Regression Wasm | 32 | 2 years ago | Go | |||||||
| Testing doing basic regression with web assembly | ||||||||||
| The Elements Of Statistical Learning | 25 | 5 years ago | gpl-3.0 | R | ||||||
| This repository contains R code for exercices and plots in the famous book. | ||||||||||
| Automobile Dataset Analysis | 20 | 2 years ago | Jupyter Notebook | |||||||
| This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price | ||||||||||
| Mlr | 11 | 1 | 4 years ago | 1 | August 02, 2019 | gpl-3.0 | Python | |||
| Multiple linear regression with statistical inference, residual analysis, direct CSV loading, and other features | ||||||||||
| Peroxide_gallery | 9 | 11 days ago | Rust | |||||||
| Examples of Peroxide (Rust numeric library) | ||||||||||
| Realestate | 9 | a year ago | Python | |||||||
| Contains a set of Python scripts for real estate data analysis, visualization, and fitting. | ||||||||||
This project is about predicting the final sale price of a house. The data is collected from Kaggle. The data set consists of 1460 observations with 81 variables. All the predictors explain the various features of the house, the data frame consists of one output variable 'Sale Price'. Data cleaning steps such as introducing new classes to missing categorical data, filling mean values for missing numerical data (Imputation) are used. Various plots such as scatter plots, violin plots, box plots, bar graphs etc. are plotted to explore the relationships between the output variable 'Sale Price' and predictors. ML algorithms such as Linear Regression, Ridge Regression, Lasso Regression are used to explore the positive and negative coefficients that influence the final Sale Price. The concept of Cross Validation is used to extract the best RMSE (Root mean squared error) score to analyse the best algorithm of all the algorithms applied. Regression plot and Residual plots are plotted to get the visualizations of the performance of the model on test data.
