Skip to content

Latest commit

 

History

History
42 lines (33 loc) · 1.98 KB

README.md

File metadata and controls

42 lines (33 loc) · 1.98 KB
Raw

Advance-Project--1-2---Prediction-with-Regression

Prediction with Regression

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

Project Details

Datasets

  1. Delivery Time:
    • Objective: Predict Delivery time using Sorting time.
  2. 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 demonstrate a meticulous approach to the entire data science process, from initial exploration to model deployment. Utilizing libraries such as pandas, numpy, seaborn, and matplotlib.pyplot, along with Jupyter notebooks for Python programming, these projects offer valuable insights into regression analysis.