Practical_Exam_Work

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

Solution :

First convert csv_result-Autism-Adult-Data.arff file to csv_result-Autism-Adult-Data.csv file using online tool.

Converting .arff file to .csv file format
Loading .csv file and changing role of id from feature to meta
Setting Class/ASD as target
Setting pre-processing as above for Encoder, Normalization and missing value handling
Setting pre-processing as above for feature selection
Dataflow
Evaluation result of dataflow before preprocessing(Test and Score)
Evaluation result of dataflow after preprocessing(Test and Score)
Confusion matrix of Random Forest Classification before pre-processing
Confusion matrix of Logistic Regression before pre-processing
Confusion matrix of Logistic Regression after pre-processing
Confusion matrix of Random Forest Classification after pre-processing
Saving data table after pre-processing in .xlsx format

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.

Getting data for PowerBI as Excel Workbook format
Loading data by selecting sheet1 and clicking on Load data
Fields of data table after loading data
Stacked Column chart between Class/ASD and id
Clustered Column chart between Class/ASD and id
Pie chart of id and result
Doughnut chart of id and result

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