Machine Learning Electricity Price Prediction.

The price of electricity depends on many factors. Predicting the price of electricity helps many businesses understand how much electricity they have to pay each year. The Electricity Price Prediction task is based on a case study where you need to predict the daily price of electricity based on the daily consumption of heavy machinery used by businesses.

Now, we need to see the code step by step:

First, as always we need to import the necessary libraries which are needed to import.

We need to read the dataset by using the pandas library.

Getting the top five of the dataset.

Getting the bottom five of the dataset.

Getting more information about the dataset through the describe() method.

Now, we need more information about the data through the info() method.

we need to convert the dataset into the numeric through the pandas library.

Finding out the null values in the dataset.

There are few null values in the dataset which we need to remove it we can remove it through the dropna method.

Now, plotting the graph we can do that through the correlation and giving it to the heatmap in that we are giving the cmap, annot and correlations.

Dividing the dataset into the X and y for preparing the model.

We need to divide the dataset into the train_test_split by giving it through the x, y test_size and random_state.

Finally, we are building the model through the RandomForestRegressor which is one of the algorithms of the ensemble method.

For that model we are building and then fitting the model through the X_train and y_train.

Finally, we are predicting the new features of the dataset and using the method model. predict to get the results.

Thanks for reading,

Mohammed Muqafamuddin.