2. Develop a program to Load a dataset with at least two numerical columns (e.g., Iris, Titanic). Plot a scatter plot of two variables and calculate their Pearson correlation coefficient. Write a program to compute the covariance and correlation matrix for a dataset. Visualize the correlation matrix using a heatmap to know which variables have strong positive/negative correlations.
PROGRAM: download dataset file click here
#install required packages
#pip install pandas matplotlib
import pandas as pd
import matplotlib.pyplot as plt
# ==============================
# 1. Load Dataset
# ==============================
df = pd.read_csv("data.csv")
print("Dataset Preview:")
print(df.head())
print("\nColumns in dataset:")
print(df.columns)
# ==============================
# 2. Select Numerical Columns
# ==============================
num_col1 = input("\nEnter first numerical column name: ")
num_col2 = input("Enter second numerical column name: ")
x = df[num_col1]
y = df[num_col2]
# ==============================
# 3. Scatter Plot
# ==============================
plt.figure(figsize=(8, 5))
plt.scatter(x, y)
plt.title(f"Scatter Plot: {num_col1} vs {num_col2}")
plt.xlabel(num_col1)
plt.ylabel(num_col2)
plt.grid(True)
plt.show()
# ==============================
# 4. Pearson Correlation Coefficient
# ==============================
pearson_corr = x.corr(y)
print("\n--- Pearson Correlation Coefficient ---")
print(f"Correlation between {num_col1} and {num_col2}: {pearson_corr}")
# ==============================
# 5. Covariance Matrix
# ==============================
numeric_df = df.select_dtypes(include=["number"])
cov_matrix = numeric_df.cov()
print("\n--- Covariance Matrix ---")
print(cov_matrix)
# ==============================
# 6. Correlation Matrix
# ==============================
corr_matrix = numeric_df.corr()
print("\n--- Correlation Matrix ---")
print(corr_matrix)
# ==============================
# 7. Heatmap of Correlation Matrix
# ==============================
plt.figure(figsize=(8, 6))
plt.imshow(corr_matrix, cmap="coolwarm", interpolation="nearest")
plt.colorbar(label="Correlation Coefficient")
plt.xticks(range(len(corr_matrix.columns)), corr_matrix.columns, rotation=45)
plt.yticks(range(len(corr_matrix.columns)), corr_matrix.columns)
plt.title("Correlation Matrix Heatmap")
# Show values inside heatmap
for i in range(len(corr_matrix.columns)):
for j in range(len(corr_matrix.columns)):
plt.text(j, i, round(corr_matrix.iloc[i, j], 2),
ha="center", va="center")
plt.tight_layout()
plt.show()OUTPUT:
Dataset Preview:
Name Age Salary Department Experience
0 Amit 25 30000 IT 2
1 Riya 28 35000 HR 3
2 Rahul 35 50000 Finance 8
3 Sneha 30 45000 IT 5
4 Arjun 40 70000 Management 12
Columns in dataset:
Index(['Name', 'Age', 'Salary', 'Department', 'Experience'], dtype='str')
Enter first numerical column name: Age
Enter second numerical column name: Salary
--- Pearson Correlation Coefficient ---
Correlation between Age and Salary: 0.9903428626629642
--- Covariance Matrix ---
Age Salary Experience
Age 62.266667 1.524571e+05 44.376190
Salary 152457.142857 3.806000e+08 109114.285714
Experience 44.376190 1.091143e+05 31.838095
--- Correlation Matrix ---
Age Salary Experience
Age 1.000000 0.990343 0.996664
Salary 0.990343 1.000000 0.991228
Experience 0.996664 0.991228 1.000000
