✅ ADVANCED LEVEL NumPy Projects

 

1. Movie Recommendation (Cosine Similarity)

Description:
Build a basic recommendation system using cosine similarity between movie ratings.

Concepts: Dot product, vector norms

Code:

import numpy as np

user1 = np.array([4, 3, 5, 0, 2])
user2 = np.array([5, 3, 4, 2, 1])

cos_sim = np.dot(user1, user2) / (np.linalg.norm(user1) * np.linalg.norm(user2))

print("Similarity Score:", cos_sim)

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2. Predict House Price (Simple Linear Regression from Scratch)

Description:
Use NumPy to implement linear regression manually (no sklearn).

Concepts: Mean, covariance, slope, intercept

Code:

import numpy as np

# Dataset
area = np.array([800, 1000, 1200, 1500, 1800])
price = np.array([120, 150, 180, 220, 260])

# slope (m)
m = np.cov(area, price)[0,1] / np.var(area)

# intercept (c)
c = price.mean() - m * area.mean()

print("Slope:", m)
print("Intercept:", c)

# Predict price for 1600 sq ft
pred = m * 1600 + c
print("Predicted Price:", pred)

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3. COVID-19 Daily Cases Simulation & Plot

Description:
Simulate daily cases using random integers and plot the trend.

Concepts: random, matplotlib, cumulative sum

Code:

import numpy as np
import matplotlib.pyplot as plt

cases = np.random.randint(50, 500, 30)
total_cases = np.cumsum(cases)

plt.plot(total_cases)
plt.title("COVID Cases Simulation")
plt.xlabel("Days")
plt.ylabel("Total Cases")
plt.show()

---

✅ 4. Sudoku Validator Using NumPy

Description:
Check if a Sudoku solution is valid using row/column uniqueness.

Concepts: array slicing, unique()

Code:

import numpy as np

board = np.array([
    [5,3,4,6,7,8,9,1,2],
    [6,7,2,1,9,5,3,4,8],
    [1,9,8,3,4,2,5,6,7],
    [8,5,9,7,6,1,4,2,3],
    [4,2,6,8,5,3,7,9,1],
    [7,1,3,9,2,4,8,5,6],
    [9,6,1,5,3,7,2,8,4],
    [2,8,7,4,1,9,6,3,5],
    [3,4,5,2,8,6,1,7,9]
])

def is_valid(board):
    for i in range(9):
        if len(np.unique(board[i])) != 9 or len(np.unique(board[:,i])) != 9:
            return False
    return True

print("Valid Sudoku:", is_valid(board))