PRACTICAL_EXAM_18IT073
TASKS:
DATASET USED:
Task-1:
Dataset Description using Orange tool.
What is need to be done to improve the accuracy of classification result of the given dataset? Get the maximum classification accuracy possible by performing following methods.
→Pre-processing
o Encoding
o Normalization
o Missing value handling
o Feature Selection
Compare your accuracy with and without applying pre-processing steps. Perform the Classification and visualize accuracy before and after preprocessing in Orange/Python.
SOLUTION:
Now, first of all we have to preprocess the data using the Orange3 tool. I have created the data follow which does the classification of target variable ‘Age’ in the dataset using 2 methods.
The above figure shows us the path where data is not pre processed. I have used KNN classification here.
The outputs of without pre-processing dataflow is shown below:
Now, here we will see the other path i.e. pre-processing.
Next step is to send data to data sampler which will divide dataset in proportion of 70% as data sample and 30% as remaining data.
After that the next step is to use the test and score widget.
The output is as follows:
Now, we will see the comparison between the simple dataflow and preprocessed data flow, we can observe that preprocessed data are in better place.
TASK2:
Task-2:
Generate the Dashboard of preprocessed dataset from task-1.
Find the Maximum data insights by plotting Bar chart, Boxplot, Pie Plot, Stack Plot using PowerBI dashboard visualization.
Now, we will create a power bi dashboard. To create dashboard first we will create reports in the power bi app.
The report contains: Line chart, ribbon chart, pie chart, clustered column chart.
Now we will publish the report and access the same through app.powerbi.com.
So, here are the tasks of practical exam.
Thank you for reading!
-18IT073 (Dhwani Panjwani)