Advance-Project--Prediction-with-Regression

Prediction with Regression

Overview

This repository contains the implementation of two advanced projects focusing on building simple linear regression models using Python. The primary goals of these projects are to predict Delivery Time based on Sorting Time and to create a prediction model for Salary Hike.

Project Details

Datasets

Delivery Time: + Objective: Predict Delivery time using Sorting time.

Salary Hike: + Objective: Build a prediction model for Salary Hike.

Methodology

1. Machine Learning Life Cycle:

   • Followed industry-standard Machine Learning Life Cycle steps.

2. EDA and Transformations:

   • Conducted thorough Exploratory Data Analysis (EDA) on both datasets.
   • Implemented necessary transformations to enhance model performance.

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. Code and Model Export:

   • Saved Python code in .ipynb format as "Delivery_prj1" and "salary_prj2."
   • Exported models to Excel following class examples.

6. GitHub Repository:

   • Named the repository "Prediction with Regression."
   • Uploaded zip folders containing datasets, graphs, .ipynb files

7. Readme File:

   • Used pandas , numpy, seaborn , matplotlib.pyplot
   • Jupyter notebook used to write Python a program

Conclusion

This repository serves as a comprehensive resource for anyone interested in regression modeling. The projects showcase a meticulous approach to the entire data science process, from initial exploration to model deployment.