Applying ML Classification algorithms on the data set and getting inferences from the data. You may use the appropriate ML algorithm packages available in R or Python. But know which algorithm you use and know the concept behind it.
- Collect a dataset from https://archive.ics.uci.edu/ml/datasets.php
- Understand the various features (columns) of the dataset.
- Do preprocessing on the dataset.
- Do preliminary analysis on the dataset as much as needed and get a summarisation of your inferences with proper validation
- You may use ether R or Python for this project
- You may use any appropriate ML classification algorithm (you need to justify why you are using the same)
- If the dataset is huge start doing your analysis on a random sample dataset from the original huge dataset and slowly scale it up.
- Write a complete report as with needed plots, charts, bar graphs, pie charts and so on and submit it. Report should also contain the description about the data set, preprocessing done - why, how and what, analysis done / ML classification done, for analysis what is the purpose of doing, for ML why specific algorithms / packages were used, what is the output, what are you inferences from the output, what are your questions about the data? what are questions addressed by your analysis and so on.
- Report should also contain all the details from where you collected the dataset, how many rows and columns are there, what are the features, why we do such and such preprocessing and after preprocessing what is changed in the dataset, what are your basic questions about your data, how you are going to address those questions through an analysis (give this with substantiation about why you use such analysis).
- Data selected : http://archive.ics.uci.edu/ml/datasets/magic+gamma+telescope
- Wrote a detailed .ipynb which can serve as report too.