Imputation Tool for Handling Missing Data Overview:

This Python script provides a simple yet effective solution for handling missing data in a dataset. It utilizes the scikit-learn library to impute missing values using various strategies such as median, mean, etc.

Features:

Reads data from a CSV file.
Allows the user to specify columns containing missing values.
Imputes missing values using the specified strategy.
Saves the imputed dataset back to a CSV file.

Dependencies:

Python 3.x
NumPy
pandas
scikit-learn

Usage:

git clone https://github.com/Lucas-Jeanniot/Database_Variance_Imputer.git

Install Dependencies:

pip install numpy pandas scikit-learn

Run the Script:

   1. python Dataset_Imputation.py

   2. Input:
      - CSV path you want to impute data on
      - Columns you want to impute
      
  

    Output:
    The script will generate a new CSV file with imputed values, saving it in the same directory as the original file.

Example:

Suppose you have a CSV file named data.csv with missing values in columns A, B, and C. You can use this script to impute missing values using the median strategy and save the updated dataset to a new file named imputed_data.csv.

Importing necessary libraries

import numpy as np import pandas as pd from sklearn.impute import SimpleImputer

Importing the dataset

dataset = pd.read_csv('data.csv')

Extracting the relevant columns

X = dataset[['A', 'B', 'C']]

Handling missing data

imputer = SimpleImputer(missing_values=0, strategy='median') X_imputed = imputer.fit_transform(X)

Update the DataFrame with the imputed values

dataset[['A', 'B', 'C']] = X_imputed

Write the updated DataFrame back to the CSV file

dataset.to_csv('imputed_data.csv', index=False)

print("Imputed data saved to 'imputed_data.csv'.")