Machine learning model implementation using scikit-learn
In this tutorial, you are going to learn 1. How to import LightGBM libraries? 2. How to download the dataset? 4. How to split the dataset into training and testing? 5. How to set parameters for the LightGBM model? 6. How to hyper-tune the parameters? 7. How to train a lightGBM model? 8. How […]
In this tutorial, you are going to learn 1. What is import the LightGBM libraries? 2. How to download the dataset? 3. How to explore the dataset? 4. How to split the dataset into training and testing? 5. How to set parameters for the LightGBM model? 6. How to create a dataset for the […]
In this tutorial, you are going to learn 1. What is Feature Engineering? 2. How to download the dataset? 3. How to explore the dataset? 4. How to process data to feed into the LightGBM model? 5. How to normalize the dataset? 6. How to split the dataset into training, validation, and testing? 7. […]
In this tutorial, you are going to learn 1. What is Feature Engineering? 2. How to download the dataset? 3. How to explore the dataset? 4. How to check for null values for effective feature engineering? 5. How to handle numerical columns with null values? 6. How to handle categorical columns with null values? […]
In this tutorial, you are going to learn 1. How to import the XGboost library? 2. How to Import the dataset? 3. How to process the dataset for the machine learning model? 4. How to convert categorical data into numerical data? 5. How to split the data into testing and training dataset? 6. How […]
In this tutorial, you are going to learn 1. How to import the necessary libraries? 2. How to Import the dataset? 3. How to explore the dataset? 4. How to convert Categorical Columns to Numerical Columns? 5. How to select the best Categorical Features? 6. How to plot a bar graph? 1. Import the […]
1. corr( ) : To calculate the correlation between every feature in the dataset.
2. heatmap( ) : To build and visualize the results of the corr ( ) method in the form of a heatmap.