Prediction with Multiple Regression

Welcome to the Prediction with Multiple Regression repository! This repository showcases two advanced projects focused on building multiple linear regression models using Python.

Project No 3 Details

Dataset

• Dataset Name: 50_startups data
• Columns:
  • R&D Spend: Research and development spend in the past few years.
  • Administration: Spend on administration in the past few years.
  • Marketing Spend: Spend on marketing in the past few years.
  • State: States from which data is collected.
  • Profit: Profit of each state in the past few years.

Methodology

1. Machine Learning Life Cycle:
   • Followed industry-standard Machine Learning Life Cycle steps.
2. EDA and Transformations:
   • Conducted comprehensive EDA on the dataset.
   • Applied necessary transformations to improve profit predictions.
3. Graphs and Interpretation:
   • Utilized Seaborn for EDA graphs.
   • Provided detailed interpretations of each graph.
4. Code and Print Statements:
   • Ensured proper documentation with print statements.
   • Rounded numbers appropriately.
5. Model Evaluation:
   • Prepared a table containing R^2 values for each prepared model.
6. GitHub Repository:
   • Named the repository "Prediction with Multiple Regression."
   • Uploaded zip folders containing datasets, graphs, and the .ipynb file.
7. Readme File:
   • Details about the use case, tools used, and insights gathered.
   • Keywords: Regression, EDA, Multiple Regression, Profit Prediction.

Project No 4 Details

Dataset

• Dataset Name: Toyota Corolla data
• Columns for Prediction Model:
  • ("Price","Age_08_04","KM","HP","cc","Doors","Gears","Quarterly_Tax","Weight")

Methodology

1. Machine Learning Life Cycle:
   • Followed industry-standard Machine Learning Life Cycle steps.
2. EDA and Transformations:
   • Conducted comprehensive EDA on selected columns.
   • Implemented necessary transformations for enhanced predictions.
3. Graphs:
   • Utilized Seaborn for EDA graphs.
4. Code and Print Statements:
   • Ensured proper documentation with print statements.
   • Rounded numbers appropriately.
5. Model Evaluation:
   • Prepared a table containing R^2 values for each prepared model.
6. GitHub Repository:
   • Named the repository "Prediction with Multiple Regression."
   • Uploaded datasets, zip graphs, and the .ipynb file.
7. Readme File:
   • Details about the use case, tools used, and insights gathered.
   • Keywords: Regression, Multiple Regression, Price Prediction, EDA.

Considerations for Prediction Model

The prediction model specifically focuses on predicting the Price of Toyota Corolla cars using the selected columns. The dataset includes various features such as model, manufacturing details, fuel type, and car specifications.

This repository serves as a valuable resource for enthusiasts interested in multiple linear regression modeling, providing a step-by-step guide, visualizations, and insights gained during the process.