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.