Standardization of Normal Variables

In the Z_score_Statistics.ipynb file, I perform standardizing normal variables using the Z-score method. This file includes the following steps:

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Mounting Google Drive in Google Colab: Access files stored in Google Drive to enable seamless data loading.
Importing Libraries and Loading the Dataset: : Load the necessary Python packages and read the scores.csv file into a DataFrame.
Visualizing the Data Distribution:
- Use the Seaborn library for visualization.
- Plot the distributions of SAT and ACT scores.
Calculating Mean and Standard Deviation:
- Compute the mean and standard deviation for SAT and ACT scores.
- Determine the Z-score for the highest scorer in SAT and ACT among all applicants.
Applying the Z-Score to All Scores: Standardize the entire table by applying the Z-score formula to all values.
Fit-Transform Using StandardScaler:
- Import StandardScaler from sklearn.preprocessing.
- Initialize the scaler and apply it to the SATscore and ACTscore columns using fit and transform methods, or utilize fit_transform directly.
- Display the updated DataFrame to confirm standardization.

This project highlights the use of Z-score standardization and the application of Python libraries to prepare data for further analysis.