Welcome to the Python Implementations for Predictive Modeling and Analytics repository! This repository contains Python implementations of various statistical tests and techniques commonly used in predictive modeling and analytics.
Predictive modeling and analytics involve using statistical methods and machine learning algorithms to analyze data and make predictions. This repository provides Python implementations for a variety of techniques, including:
- F-test: A statistical test used to compare the variances of two populations.
- T-test: A statistical test used to determine if there is a significant difference between the means of two groups.
- Analysis of Variance (ANOVA): A statistical technique used to compare means across multiple groups.
- Linear Square Estimation: A method used to estimate the parameters of a linear regression model.
- Autocorrelation Analysis: A technique used to analyze the correlation between observations in time series data.
- Least Squares Method: A method used to find the best-fitting line through a set of points by minimizing the sum of the squares of the vertical deviations.
Each implementation is accompanied by detailed explanations, code examples, and usage instructions to help you understand and apply these techniques in your own projects.
The repository includes Python scripts and excel sheets demonstrating the implementation of each technique. You'll find code examples illustrating how to perform F-tests, t-tests, ANOVA, linear square estimation, autocorrelation analysis, and least squares method using Python libraries such as NumPy, SciPy, and pandas etc.
To get started, for the technique you're interested in, open the corresponding jupyter notebook or excel sheet. Each file contains code examples along with comments explaining the implementation details and usage instructions. You can run the code in your local Python environment or in a Jupyter notebook to experiment with the techniques and understand how they work.
Contributions to this repository are welcome! If you have practical exercises, tutorials, or additional resources related to predictive modeling and analytics that you would like to share, please consider contributing them to the repository. Follow these steps to contribute:
- Fork the repository to your GitHub account.
- Add your practical exercises or tutorials to the appropriate directory, including any necessary code, instructions, and datasets.
- Update the README.md file if needed to include information about the newly added content.
- Submit a pull request to have your changes reviewed and merged into the main repository.
If you have any questions, feedback, or suggestions regarding this repository, please don't hesitate to contact us.
Happy learning and happy modeling!