Skip to content

Implemented and compared various machine learning algorithms and visualizations on the World Population 2024 dataset to identify the most efficient predictive model. Additionally, evaluated model accuracy using different methods to ensure prediction reliability and precision.

Notifications You must be signed in to change notification settings

manya-gangoli/World-Population-2024-EDA-and-prediction

Repository files navigation

World Population 2024 using Machine Learning

Techniques Used:

  • Data Cleaning
  • Data Visualization
  • Handling Missing Values
  • Pre-Processing
  • Modeling Training

Techniques Used:

  • Linear Regression
  • Support Vector Regressorli>
  • Random Forest Regressor
  • Decision Tree Regressor

Model Evaluation Method:

  • Mean Squared Error
  • R2 Score

Packages and Tools Required:

  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit Learn
  • Jupyter Notebook

Packages Installation:

  • pip install numpy
  • pip install pandas
  • pip install seaborn
  • pip install scikit-learn
  • pip install matplotlib

About

Implemented and compared various machine learning algorithms and visualizations on the World Population 2024 dataset to identify the most efficient predictive model. Additionally, evaluated model accuracy using different methods to ensure prediction reliability and precision.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published