https://github.com/grayoj/comex
Linear regression to perform predictive analysis on individual's income.
https://github.com/grayoj/comex
linear-algebra linear-regression numpy pandas sklearn
Last synced: 10 months ago
JSON representation
Linear regression to perform predictive analysis on individual's income.
- Host: GitHub
- URL: https://github.com/grayoj/comex
- Owner: grayoj
- License: mit
- Created: 2022-04-13T13:40:35.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-01-29T22:16:10.000Z (almost 3 years ago)
- Last Synced: 2025-04-09T23:10:37.861Z (10 months ago)
- Topics: linear-algebra, linear-regression, numpy, pandas, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 117 KB
- Stars: 12
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Linear Regression Model
***
In this project (jupyter notebook) we are using a SickitLearn model, to perform linear regression to make income predictions with the dataset made available in the repository.
Linear regression, is a supervised machine learning model which implements a linear relationship between an independent and
dependent variable. It is a statistical method of making predictive analysis. We can illustrate linear regression by the below formula:
$y=a$$0$$+a$$1$$x+ ε$
Where
- $Y$ represents = The Dependent Variable (Target Variable)
- $X$ represents = The Independent Variable (Predictor Variable)
- $a0$ represents = The intercept of the line (Gives an additional degree of freedom)
- $a1$ represents = The Linear regression coefficient (Scale factor for each input value)
- $ε$ represents = The Random error
## Preparing Data For Linear Regression
***
1. Linear Assumption.
2. Eliminate Noise (Carry out data cleaning)
3. Eliminate Collinearity.
4. Rescale the provided inputs.
## Requirements
These are the tools, environment variables and libraries used in the project.
1. Jupyter Notebook.
2. Python 3+
3. SickitLearn.
4. Pandas.
5. Numpy.
6. Data Set (Already Provided)
## Installation
To use this project, you need to clone the repository.
``> git clone https://github.com/grayoj/income-prediction.git``
After you finish cloning, install these modules using Pip
Install Sklearn
``> pip install sklearn``
Install Pandas to read datasets (CSV)
``> pip install pandas``
Install NumPy
``> pip install numpy``
Ensure you have Jupyter Notebook installed, which you could easily set up if you use VsCode
### Try out the notebook!
Any suggestions, feedback and help, as well as improvements are all welcome and will be appreciated.
Contact!
Mail me, here.
- MIT License.