Project Based Learning (Sample Project)

# Step 1 – create the data file (in a real project you collect this from a survey or form)
import pandas as pd
data = {'Roll': range(1, 13),
        'Name': ['Aarav', 'Bela', 'Chirag', 'Diya', 'Eshan', 'Farah', 'Gopal', 'Hina',
                 'Ishaan', 'Jiya', 'Kunal', 'Lata'],
        'Section': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'],
        'English': [78, 85, 62, 90, 71, 88, 55, 93, 67, 80, 74, 69],
        'Maths': [92, 74, 58, 81, 88, 67, 49, 95, 73, 61, 84, 77],
        'IP': [95, 89, 70, 92, 84, 79, 61, 98, 76, 83, 90, 72]}
pd.DataFrame(data).to_csv('class12_result.csv', index=False)
print('class12_result.csv created with', len(data['Roll']), 'students')

# Step 2 – read, add Total, Percent and Grade
import pandas as pd
df = pd.read_csv('class12_result.csv')
df['Total'] = df[['English', 'Maths', 'IP']].sum(axis=1)
df['Percent'] = (df['Total'] / 3).round(1)
def grade(p):
    if p >= 90: return 'A1'
    if p >= 80: return 'A2'
    if p >= 70: return 'B1'
    if p >= 60: return 'B2'
    return 'C'
df['Grade'] = df['Percent'].apply(grade)
df.to_csv('class12_processed.csv', index=False)
print(df[['Name', 'Section', 'Total', 'Percent', 'Grade']])

# Step 3 – analysis
import pandas as pd
df = pd.read_csv('class12_processed.csv')
print('Class average %   :', round(df['Percent'].mean(), 2))
print('Subject averages  :'); print(df[['English', 'Maths', 'IP']].mean().round(1))
print('Topper            :', df.loc[df['Total'].idxmax(), 'Name'])
print('Section-wise mean :'); print(df.groupby('Section')['Percent'].mean().round(2))
print('Grade count       :'); print(df['Grade'].value_counts().sort_index())

# Step 4 – charts
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('class12_processed.csv')
avg = df[['English', 'Maths', 'IP']].mean()
plt.bar(avg.index, avg.values, color=['#2563eb', '#f97316', '#16a34a'])
plt.title('Subject-wise Class Average'); plt.ylabel('Average marks'); plt.ylim(0, 100)
plt.savefig('project_subject_avg.png')
plt.figure()
plt.hist(df['Percent'], bins=[50, 60, 70, 80, 90, 100], edgecolor='black', color='#a78bfa')
plt.title('Distribution of Percentage'); plt.xlabel('Percent'); plt.ylabel('Students')
plt.savefig('project_hist.png')
plt.figure()
gc = df['Grade'].value_counts().sort_index()
plt.pie(gc.values, labels=gc.index, autopct='%1.0f%%')
plt.title('Grade Share')
plt.savefig('project_pie.png')
print('3 charts saved')

# Step 5 – menu-driven program that ties everything together
import pandas as pd
df = pd.read_csv('class12_processed.csv')
while True:
    print('1.Show all 2.Search by name 3.Students above % 4.Exit')
    ch = input('Choice: ')
    if ch == '1':
        print(df[['Roll', 'Name', 'Percent', 'Grade']].to_string(index=False))
    elif ch == '2':
        n = input('Name: ')
        r = df[df['Name'].str.lower() == n.lower()]
        print(r[['Name', 'English', 'Maths', 'IP', 'Percent']].to_string(index=False) if len(r) else 'Not found')
    elif ch == '3':
        p = float(input('Minimum %: '))
        print(df[df['Percent'] >= p]['Name'].tolist())
    else:
        print('Thank you'); break