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Data Mining coursework. Required the submission of two seperate py files. Short description. The aim of this coursework assignment is to demonstrate understanding of classification and cluster analysis.
Enantiodromis/Data_Mining_UCI_Machine_Learning
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##################################################################################### ####################### FILE NAME: adult_data.py #################################### ##################################################################################### 1. read_data(data_path, data_cols, excluded_columns = '') Inputs: datapath, list of strings, string Outputs: Either one dataframe or two dataframes Function: Reads in csv and outputs a dataframe. Additionally a df with a specific column dropped can also be created. 2. data_information_gathering(data_frame) Inputs: dataframe Outputs: Prints table Function: Returns information about the data within the dataframe THE INFORMATION BEING: - Number of instances - Number of missing values - Fraction of missing values over all attribute values - Number of instances with missing values - Fraction of instances with missing values over all instances 3. cols_encoding(data_frame, to_print = True) Inputs: dataframe, boolean Outputs: encoded column values Function: Encodes for each attribute and returns the set of encoded values 4. x_y_encoding(data_frame, target_column_index) Inputs: dataframe, integer value Outputs: two lists "encoded_rows" and "target_encoded_row" Function: A function called from create_decision_tree() function and used to encode the data used for training and testing. 5. create_decision_tree(data_frame, target_column, test_data_frame = pd.DataFrame(), ignore_missing = True) Inputs: dataframe, string, dataframe, boolean Outputs: printed line, showing the error rate of a decision tree Function: Function which creates trains and tests a decision tree classifier 6. missing_val_df_split(data_frame) Inputs: dataframe Outputs: dataframe Function: Return df with only missing values 7. add_random_df(data_frame1, data_frame2, amount = 0) Inputs: dataframe, dataframe, integer Outputs: dataframe Function: Randomly gets values from one df and adds to another used for creating D' 8. populate_df_na_val(data_frame, fill_value = "", mode = False) Inputs: dataframe, string, boolean Outputs: printed line, showing the error rate of a decision tree Function: Filling missing values withing a dataframe with a specified value or with the mode value ##################################################################################### ####################### FILE NAME: wholesale_data.py ################################ ##################################################################################### 1. read_data(data_path, data_cols, excluded_columns = '') Inputs: datapath, list of strings, string Outputs: Either one dataframe or two dataframes Function: Reads in csv and outputs a dataframe. Additionally a df with a specific column dropped can also be created. 2. mean_and_ranges_df(data_frame) Inputs: datapath Outputs: prints a table Function: function is used to retrieve the means and ranges for every column of a specified dataframe 3. bc_distance_calc(centroids) Inputs: np.array() Outputs: float Function: Used to calculate the between cluster score.. 4. display_cluster_data(wc, bc, clusters) Inputs: list, list, list or integer Outputs: Prints a line, K number WC value, BC value and BC/WC ratio Function: Prints the within cluster and between cluster distances and scores 5. scatter_plotting(data_frame, labels, centroids, clusters) Inputs: dataframe, np.array, np.array, list Outputs: saves a png image Function: Called by the k_means_pair_run function and used to plot scatterplots when triggered
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Data Mining coursework. Required the submission of two seperate py files. Short description. The aim of this coursework assignment is to demonstrate understanding of classification and cluster analysis.
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