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train-test-using-sklearn

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This repository contains all the Machine Learning projects I did using different Machine Learning methods. Python being the main software used.

  • Updated Jun 15, 2022
  • Jupyter Notebook

Different modeling techniques like multiple linear regression and random forest, etc. will be used for predicting the cement compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy.

  • Updated Jan 8, 2023
  • Jupyter Notebook

Rusty Bargain is a used car buying and selling company that is developing an app to attract new buyers. My job as data science is to create a model that can determine the market value of a car.

  • Updated Jul 3, 2024
  • Jupyter Notebook

An insurance company called "Sure Tomorrow" wants to solve some problems with the help of machine learning. As a Data Science we're Predict the amount of insurance claims that a new client might receive and Protect clients' personal data without breaking the model with masking

  • Updated Jul 3, 2024
  • Jupyter Notebook

Megaline company wants to develop a model that can analyze consumer behavior and recommend one of Megaline's two new plans: Smart or Ultra. In this classification task, we need to develop a model that is able to choose the right package

  • Updated Jul 1, 2024
  • Jupyter Notebook

📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

  • Updated Jul 9, 2024
  • Jupyter Notebook

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