Data Analysis Assinment Help Using Pyspark - Million Song Dataset Analysis

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We have a Million song Dataset which is freely available on UCI machine learning Repository. The MSD contains metadata and audio analysis for a million songs that were legally available to The Echo Nest. The dataset contains 515345 instances and 90 attributes.

As an illustration, we present year prediction as an example application. We show positive results on year prediction, and discuss more generally the future development of the dataset

Attributes Information

Total 90 attributes in the dataset. 12 attributes are timbre average and 78 attributes timbre covariance. The first value is the year (target) which ranges from 1922 to 2011.

We are performed the following task using pyspark

• Read and load data in spark dataframe. Count the number of data points and print the first 40 instances.

• Normalize the feature between 0 and 1.

• create a new DataFrame in which the labels are shifted such that smallest label equals zero

• Split the data into training, validation and testing.

• Create a baseline model where we always provide the same prediction irrespective of our input.

• Calculate the Root mean square error.

• Using the testing data measures the performance of the base model.

• Visualize the scatter plot of Actual data and predicted data.

• Again split data into training, validation and testing set ( 70%, 10%, 20%)

• Train model on training data and evaluate the model based on validation set.

• Visualize the training error and log of training error for 50 iterations.

• Use the model for prediction on test data and calculate the Root Mean Squared Error.

The data is stored in the text file. In this step we are stored in text data as a pyspark dataframe. Each data point is separated by comma-delimited string and each string starts with a label (year) followed by numerical audio features.

Output :

MSD Dataset

There are 90 attributes in the million song dataset.

Output :

Total Columns

Normalize the data between 0 and 1. Normalization helps to converge machine learning algorithms faster. Before Normalization we need to combine all feature column in vector column by applying the VectorAssembler. Then apply the MinMaxScaler on Vector column.

Output :

VectorAssembler and MinMaxScaler

Now here we are create a new DataFrame in which the labels are shifted such that smallest label equals zero. In the machine learning problem it is often natural to shift labels such that they start from zero

Output :

shifted label

Now split the data into training, validation and testing ( 80%, 10%, 10% ).

Output :

Split Data

A very simple natural baseline model is one where we always make the same prediction independent of the given data point, using the average label in the training set as the constant prediction value.

Otuput :

Average Train Year

Now here see how well this baseline performs. We used root mean squared error (RMSE) for evaluation purposes. Calculate the RMSE given dataset of (prediction and label ) Using the Regression Evaluator

Output :

Training, Validation and Test RMSE Of Our Baseline model

We will Visualizing the scatter plot predictions on the validation dataset. The scatter plot show of Actual data and predicted data. we can see in the plot the predicted value exactly equals the actual data.

Output :

Predicted Vs Actual on Validation Dataset

In the following plot uses the baseline predictor for all predicted value.

Output :

Baseline Predictor For All Predicted Value

Now we will split data into training , validation and testing set ( 70%, 10%, 20% )

Output:

Split Data ( Train - 70%, valid - 10%, and Test - 20% )

Now Train a linear regression model on all of our training data and evaluate its accuracy on the validation set.

output :

Visualize the training error and log of training error for 50 iterations.

Log Of Training Error For 50 Iteration

Use the model for prediction on test data and calculate the Root Mean Squared Error.

Output :

Thank you

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