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

  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:

python regression_model.py