Metadata-Version: 2.1
Name: MissingValues_101703292
Version: 1.0.1
Summary: A Python package to handle missing values in the dataset
Home-page: UNKNOWN
Author: Kriti Pandey
Author-email: kritip105@gmail.com
License: MIT
Description: # Project MISSING VALUES
        
        Name **Kriti Pandey** 
        
        Roll no **101703292**
        
        Group **3COE13**
        
        **DESCRIPTION**
        Data can have missing values for a number of reasons such as observations that were not recorded and data corruption.Handling missing data is important as many machine learning algorithms do not support data with missing values.
        
        Hereâ€™s some typical reasons why data is missing:
        
        1) User forgot to fill in a field.
        
        2) Data was lost while transferring manually from a legacy database.
        
        3) There was a programming error.
        
        4) Users chose not to fill out a field tied to their beliefs about how the results would be used or interpreted.
        
        Specifically, there are 2 steps to handle missing data:
        
        1) mark invalid or corrupt values as missing in your dataset.
        
        2) impute missing values with mean values in your dataset.
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install OUTLIER_101703292.
        
        ```bash
        pip install MissingValues_101703292
        ```
        
        ## Usage
        Enter csv filename followed by .csv extentsion
        
        ```python
        MissingValues_101703292 data.csv 
        ```
        ## Sample dataset
        
        | 0  | 1  | 2     | 3    | 4    | 5     | 6    | 7     | 8  | 9 |
        |----|----|-------|------|------|-------|------|-------|----|---|
        | 0  | 6  | 148.0 | 72.0 | 35.0 | NaN   | 33.6 | 0.627 | 50 | 1 |
        | 1  | 1  | 85.0  | 66.0 | 29.0 | NaN   | 26.6 | 0.351 | 31 | 0 |
        | 2  | 8  | 183.0 | 64.0 | NaN  | NaN   | 23.3 | 0.672 | 32 | 1 |
        | 3  | 1  | 89.0  | 66.0 | 23.0 | 94.0  | 28.1 | 0.167 | 21 | 0 |
        | 4  | 0  | 137.0 | 40.0 | 35.0 | 168.0 | 43.1 | 2.288 | 33 | 1 |
        | 5  | 5  | 116.0 | 74.0 | NaN  | NaN   | 25.6 | 0.201 | 30 | 0 |
        | 6  | 3  | 78.0  | 50.0 | 32.0 | 88.0  | 31.0 | 0.248 | 26 | 1 |
        | 7  | 10 | 115.0 | NaN  | NaN  | NaN   | 35.3 | 0.134 | 29 | 0 |
        | 8  | 2  | 197.0 | 70.0 | 45.0 | 543.0 | 30.5 | 0.158 | 53 | 1 |
        | 9  | 8  | 125.0 | 96.0 | NaN  | NaN   | NaN  | 0.232 | 54 | 1 |
        | 10 | 4  | 110.0 | 92.0 | NaN  | NaN   | 37.6 | 0.191 | 30 | 0 |
        | 11 | 10 | 168.0 | 74.0 | NaN  | NaN   | 38.0 | 0.537 | 34 | 1 |
        | 12 | 10 | 139.0 | 80.0 | NaN  | NaN   | 27.1 | 1.441 | 57 | 0 |
        | 13 | 1  | 189.0 | 60.0 | 23.0 | 846.0 | 30.1 | 0.398 | 59 | 1 |
        | 14 | 5  | 166.0 | 72.0 | 19.0 | 175.0 | 25.8 | 0.587 | 51 | 1 |
        | 15 | 7  | 100.0 | NaN  | NaN  | NaN   | 30.0 | 0.484 | 32 | 1 |
        | 16 | 0  | 118.0 | 84.0 | 47.0 | 230.0 | 45.8 | 0.551 | 31 | 1 |
        | 17 | 7  | 107.0 | 74.0 | NaN  | NaN   | 29.6 | 0.254 | 31 | 1 |
        | 18 | 1  | 103.0 | 30.0 | 38.0 | 83.0  | 43.3 | 0.183 | 33 | 0 |
        | 19 | 1  | 115.0 | 70.0 | 30.0 | 96.0  | 34.6 | 0.529 | 32 | 1 |
        
        ## Input
        
        ```bash
        MissingValues_101703292 Sampledata.csv
        ```
         ## Result
        
         ```bash
           S No.   1    2     3     4       5      6      7   8  9
        
        0       0   6  148  72.0  35.0  116.15  33.60  0.627  50  1
        
        1       1   1   85  66.0  29.0  116.15  26.60  0.351  31  0
        
        2       2   8  183  64.0  17.8  116.15  23.30  0.672  32  1
        
        3       3   1   89  66.0  23.0   94.00  28.10  0.167  21  0
        
        4       4   0  137  40.0  35.0  168.00  43.10  2.288  33  1
        
        5       5   5  116  74.0  17.8  116.15  25.60  0.201  30  0
        
        6       6   3   78  50.0  32.0   88.00  31.00  0.248  26  1
        
        7       7  10  115  61.7  17.8  116.15  35.30  0.134  29  0
        
        8       8   2  197  70.0  45.0  543.00  30.50  0.158  53  1
        
        9       9   8  125  96.0  17.8  116.15  30.95  0.232  54  1
        
        10     10   4  110  92.0  17.8  116.15  37.60  0.191  30  0
        
        11     11  10  168  74.0  17.8  116.15  38.00  0.537  34  1
        
        12     12  10  139  80.0  17.8  116.15  27.10  1.441  57  0
        
        13     13   1  189  60.0  23.0  846.00  30.10  0.398  59  1
        
        14     14   5  166  72.0  19.0  175.00  25.80  0.587  51  1
        
        15     15   7  100  61.7  17.8  116.15  30.00  0.484  32  1
        
        16     16   0  118  84.0  47.0  230.00  45.80  0.551  31  1
        
        17     17   7  107  74.0  17.8  116.15  29.60  0.254  31  1
        
        18     18   1  103  30.0  38.0   83.00  43.30  0.183  33  0
        
        19     19   1  115  70.0  30.0   96.00  34.60  0.529  32  1
         ```
        
        ## Constraint 
        *Your csv file should not have categorical data*
        
        
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
