CMPF104: Data Cleaning and Preprocessing: Data science and Data Anaytics: Programming For Foundation In Engineering, Assignment, UNITEN, Malaysia
University | Universiti Tenaga Nasional (UNITEN) |
Subject | CMPF104: Programming For Foundation In Engineering |
Data science and Data Anaytics
Download the dataset from BRIGHTEN. If your student ID ends with an odd number, select Concrete_Data_A dataset, and if your student ID ends with an even number, select Concrete_Data_B dataset. Using the Python attributes, function and libraries to solve the following problems.
a) Data Cleaning and Preprocessing:
- Use Pandas to load the dataset. Name the dataframe as concrete_df_XXX.
- Remove ‘Number’ column using .drop() function and visualize the first ten (10)
rows of the data. - Handle any missing values by dropping or replacing the empty cells. Check for missing values using functions like .info() or .isnull().sum()
- Convert the data frame to array, using to_numpy() function.
- Divide the data into two sets of data with division of 80% and 20% for train and test data, respectively. Name the dataset as train_data_XXX and test_data_XXX
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b) Data Analysis:
- Calculate the correlation between the variables in the dataframe.
- Utilize NumPy and Pandas to calculate summary statistics of the data such as
maximum, minimum, standard deviation, average, median and mode of each
category. - Use Pandas functions like .describe() for an overview of summary statistics and apply NumPy functions for specific calculations.
c) Visualization:
- Use Matplotlib to create visualizations such as line plots for train and test data
across all categories. - Generate histogram plots and box plots for all variables.
- Ensure that the visualizations are clear, informative, and aesthetically pleasing.
- Customize your plots by adding the titles, labels and legends
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