12 PM – AI and ML – Gamas

Today We Did

1. We learned more about running streamlit in local machine.
2. We learned about the single digit computer vision. We learned how training data matters. If you train your models with black and white images, then most likely it will predicts well with black and white images. And it will predicts poorly with gradient color images.
3. We learned there are more than just computer vision in AI and ML world. There are predicting stock market, predicting health.
4. We started doing Titanic Survivorship project.

Homework

1. Learn about “pandas” python library. What are the common use case for this library.
2. Learn about “numpy” python library. What are the common use case for this library.
3. Create a Python project that would read a CSV file (student_scores.csv) and display the average test score for each subject
   1. Download this csv file: students_scores
   2. Create a new python project in pycharm
   3. Inside that project create a new text file (not a python file) requirements.txt . Inside the file, put this one line.

      1. pandas==2.2.2

   4. After you do this, the pycharm should display a blue link to “install requirement” . Click this to install pandas library into your project.
   5. create main.py
      1. Inside the file write codes
         1. Import the Pandas library.
         2. Load the CSV file into a DataFrame.
         3. Display the first few rows of the DataFrame to get an overview of the data.
         4. Print the column names and the number of rows and columns in the dataset.
         5. Calculate the mean score for each subject (Math, Science, English).
      2. If you do it correctly, you should see the following as your output

      3. Column Names: Index(['Name', 'Math', 'Science', 'English'], dtype='object')
         Number of Rows and Columns: (5, 4)
         
         Mean Scores for Each Subject:
         Math 84.0
         Science 87.4
         English 87.0
         dtype: float64