https://github.com/ebadshabbir/company_profit-onehotencoding-

This project uses multiple linear regression to predict startup profits based on spending and location data from the **50 Startups** dataset. It includes data preprocessing, model training, and performance evaluation using Scikit-Learn.
https://github.com/ebadshabbir/company_profit-onehotencoding-

jupyter-notebook machine-learning matplotlib multiple-linear-regression onehot-encoding pandas pyhton regression sklearn

Last synced: 17 days ago
JSON representation

This project uses multiple linear regression to predict startup profits based on spending and location data from the **50 Startups** dataset. It includes data preprocessing, model training, and performance evaluation using Scikit-Learn.

• Host: GitHub
• URL: https://github.com/ebadshabbir/company_profit-onehotencoding-
• Owner: EbadShabbir
• Created: 2024-10-19T17:26:39.000Z (29 days ago)
• Default Branch: main
• Last Pushed: 2024-10-20T16:41:33.000Z (28 days ago)
• Last Synced: 2024-10-31T11:07:17.283Z (17 days ago)
• Topics: jupyter-notebook, machine-learning, matplotlib, multiple-linear-regression, onehot-encoding, pandas, pyhton, regression, sklearn
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/ebadshabbir/company-profit
• Size: 8.79 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Multiple Linear Regression on 50 Startups Dataset

This project demonstrates how to perform multiple linear regression using Python and Scikit-Learn on the **50 Startups** dataset. The dataset contains data on 50 startups with information on R&D Spend, Administration, Marketing Spend, and State, along with the corresponding profit.

## Project Overview

In this project, we:

1. **Preprocess the data**: Use OneHotEncoding to handle categorical variables (State).

2. **Avoid the dummy variable trap**: Remove one of the one-hot encoded columns.

3. **Split the data**: Divide the dataset into training and test sets.

4. **Fit the multiple linear regression model**: Train the model on the training set.

5. **Evaluate the model**: Measure the accuracy of the model using training and test scores.

## Libraries and Dependencies

- `numpy`: For array operations

- `pandas`: For data handling

- `matplotlib`: For plotting (not used in this case, but imported)

- `scikit-learn`: For machine learning tasks such as encoding, splitting, and regression

    ```bash

    pip install numpy pandas matplotlib scikit-learn

Cloning and Running the Project

Clone this repository to your local machine:

    ```bash    

    git clone https://github.com/EbadShabbir/50-startups-regression.git

Navigate to the project directory:

    ```bash

    

    cd 50-startups-regression

Ensure that you have the 50_Startups.csv dataset in the same directory as the script, or adjust the dataset path accordingly in the code.

Run the Python script:

```bash

python regression_model.py