https://github.com/qtle3/multiple-linear-regression

A Python implementation of multiple linear regression to predict the profit of startups based on their spending in R&D, Administration, Marketing, and the state they operate in.
https://github.com/qtle3/multiple-linear-regression

data-preprocessing feature-engineering interpretation-of-results model-training-and-evaluation multiple-linear-regression-model prediction-algorithm

Last synced: 1 day ago
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A Python implementation of multiple linear regression to predict the profit of startups based on their spending in R&D, Administration, Marketing, and the state they operate in.

• Host: GitHub
• URL: https://github.com/qtle3/multiple-linear-regression
• Owner: qtle3
• Created: 2024-08-15T04:58:23.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-16T04:48:23.000Z (3 months ago)
• Last Synced: 2024-08-16T06:22:03.872Z (3 months ago)
• Topics: data-preprocessing, feature-engineering, interpretation-of-results, model-training-and-evaluation, multiple-linear-regression-model, prediction-algorithm
• Language: Python
• Homepage:
• Size: 11.7 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Multiple Linear Regression for Startup Profit Prediction

This project demonstrates the use of multiple linear regression to predict the profit of startups based on their spending in R&D, Administration, Marketing, and the state where the startup operates. The script processes the dataset, encodes categorical data, and trains a linear regression model to predict profit based on these independent variables.

## Detailed Summary

The dataset used in this project contains data from 50 startups, including information on spending in R&D, Administration, Marketing, the State they are located in, and the resulting Profit. The script performs the following steps:

- Imports the dataset and splits it into independent variables (spending and state) and the dependent variable (profit).

- Encodes the categorical "State" column into numerical values using one-hot encoding.

- Splits the dataset into training and test sets.

- Trains a multiple linear regression model on the training data to establish relationships between spending and profits.

- Predicts the profits for the test data and compares them with the actual profits.

- Allows for a single prediction based on specified input values for R&D, Administration, and Marketing spend for a specific state.

- Outputs the regression coefficients and intercept to give insights into the relationships between the independent variables and the predicted profit.

  

## Key Concepts Covered

- **Multiple Linear Regression:** A statistical technique that models the relationship between multiple independent variables (spending in different departments and the state) and a dependent variable (profit).

- **Data Preprocessing:** Includes splitting the data into features (independent variables) and target (dependent variable), encoding categorical data (state), and dividing the dataset into training and testing sets.

- **Model Training and Evaluation:** The linear regression model is trained on the training set and then used to predict values for the test set, allowing comparison between predicted and actual results.

- **Feature Engineering:** The script demonstrates how to handle categorical variables using one-hot encoding to allow a regression model to interpret non-numeric data like "State".

- **Prediction and Interpretation:** It shows how to make specific predictions using new data inputs and outputs the regression coefficients to interpret the impact of each feature on the predicted result.

-  **Regression Coefficients and Intercept:** The script outputs the coefficients and intercept of the regression model, which describe how each variable influences the profit.