Instructions:
- The course objectives and course outcomes are identical to that of (Data Pre-processing and Post Processing) as this is the practical component of the corresponding theory paper.
- The practical list shall be notified by the teacher in the first week of the class commencement under intimation to the office of the Head of Department / Institution in which the paper is being offered from the list of practicals below.
List of Programs
- Download, install and explore the features of NumPy, SciPy, Jupyter, Stats models and Pandas packages.
- Working with NumPy arrays
- Working with Pandas data frames
- Reading data from text files, Excel and the web and exploring various commands for doing descriptive analytics on the Iris data set.
- Use the diabetes data set from UCI and Pima Indians Diabetes data set for performing the following:
- Univariate analysis: Frequency, Mean, Median, Mode, Variance, Standard Deviation, Skewness and Kurtosis.
- Bivariate analysis: Linear and logistic regression modelling
- Multiple Regression analysis
- Also compare the results of the above analysis for the two data sets.
- Perform following pre-processing techniques on loan prediction dataset
- Feature Scaling
- Feature Standardization
- Label Encoding
- One Hot Encoding
- Apply and explore various plotting functions on UCI data sets.
- a. Normal curves
- b. Density and contour plots
- c. Correlation and scatter plots
- d. Histograms
- e. Three-dimensional plotting
- Perform following visualizations using matplotlib: Bar Graph, Pie Chart, Box Plot, Histogram, Line Chart and Subplots, Scatter Plot.
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