- Linear Regression: In this notebook, we train a Linear Regression model on the California Housing dataset.
- Logistic Regression: In this notebook, we train a Logistic Regression model on the Iris dataset.
- Lasso & Ridge Regression: In this notebook, we train two models - Lasso & Ridge Regression on the House Price dataset.
- Polynomial Regression: In this notebook, we train a Polynomial Regression model on the Fuel Emissions dataset.
- Naive Bayes: In this notebook, we train a Naive Bayes model on the E-Commerce Dataset.
- Suppoer Vector Machine: In this notebook, we train a Support Vector Machine model on the Breast Cancer dataset.
- Decision Trees: In this notebook, we train a Decision Trees model on the German Credit dataset.
- Multi Layer Perceptron: In this notebook, we train a Multi Layer Perceptron on the Wine dataset.
- Variance Inflation Factor: In this notebook, we analyze the Variance Inflation Factor to remove Multicollinearity and improve the accuracy of the Machine Learning model.
In all the above notebooks, we perform some preprocessing including handling missing values, scaling numeric data, encoding categorical data, and preprocessing as per the dataset given.