all_tasks_completed

Author: shyam-chauhan

Chauhan_Shyam_R/Task_10/data_analysis.py

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+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# 1. Load the DataSet:
+iris_df = sns.load_dataset('iris')
+
+# 2. Exploratory Data Analysis (EDA):
+print("Dataset information:")
+print(iris_df.info())
+
+print("\nSummary statistics:")
+print(iris_df.describe())
+
+print("\nFirst few rows of the dataset:")
+print(iris_df.head())
+
+# 3. Data Cleaning:
+print("\nChecking for missing values:")
+print(iris_df.isnull().sum())
+
+print("\nChecking for duplicate rows:")
+print(iris_df.duplicated().sum())
+
+# 4. Aggregation:
+species_mean = iris_df.groupby('species').mean()
+
+# 5. Visualizations:
+# Pairplot
+sns.pairplot(iris_df, hue='species')
+plt.suptitle('Pairplot of Iris Dataset', y=1.02)
+plt.show()
+
+# Correlation Heatmap
+plt.figure(figsize=(8, 6))
+sns.heatmap(iris_df.corr(), annot=True, cmap='coolwarm')
+plt.title('Correlation Matrix')
+plt.show()
+
+# 6. Correlation Calculations:
+correlation_matrix = iris_df.corr()

Chauhan_Shyam_R/Task_11/regression.py

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+from sklearn.datasets import load_diabetes
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load the dataset
+diabetes = load_diabetes()
+
+# Prepare the data
+X = diabetes.data
+y = diabetes.target
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train the model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Evaluate the model
+train_score = model.score(X_train, y_train)
+test_score = model.score(X_test, y_test)
+print(f'Training Score: {train_score:.2f}')
+print(f'Testing Score: {test_score:.2f}')
+
+# Plot residuals
+train_residuals = y_train - model.predict(X_train)
+test_residuals = y_test - model.predict(X_test)
+
+plt.figure(figsize=(12, 6))
+
+plt.subplot(1, 2, 1)
+plt.scatter(model.predict(X_train), train_residuals, alpha=0.5, label='Train Residuals')
+plt.axhline(y=0, color='r', linestyle='--')
+plt.title('Residual Plot (Training)')
+plt.xlabel('Predicted Values')
+plt.ylabel('Residuals')
+plt.legend()
+
+plt.subplot(1, 2, 2)
+plt.scatter(model.predict(X_test), test_residuals, alpha=0.5, label='Test Residuals')
+plt.axhline(y=0, color='r', linestyle='--')
+plt.title('Residual Plot (Testing)')
+plt.xlabel('Predicted Values')
+plt.ylabel('Residuals')
+plt.legend()
+
+plt.tight_layout()
+plt.show()

Chauhan_Shyam_R/Task_12/be_better.png

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+from PIL import Image
+import os
+
+def compress_image(input_path, output_path, quality=60):
+    try:
+        input_image = Image.open(input_path)
+
+        if input_image.mode == 'RGBA':
+            input_image = input_image.convert('RGB')
+
+        # Determine output format based on file extension
+        output_format = os.path.splitext(output_path)[1][1:].upper()  # Get file extension and convert to uppercase
+        if output_format not in ['JPEG', 'JPG', 'PNG']:
+            raise ValueError("Unsupported output format. Supported formats: JPEG, JPG, PNG")
+
+        # Save the compressed image
+        compressed_image = input_image.copy()
+        compressed_image.save(output_path, format=output_format, quality=quality)
+
+        print(f"Compressed image saved at: {output_path}")
+
+    except FileNotFoundError:
+        print(f"Error: The file '{input_path}' was not found.")
+    except Exception as e:
+        print(f"Error: {e}")
+
+def main():
+    input_path = r'D:\Python-Programming-Internship\Chauhan_Shyam_R\Task_12\be_better.png'  # Adjust this path
+    output_folder = 'compressed_images'
+    os.makedirs(output_folder, exist_ok=True) 
+
+    # Interactive quality adjustment
+    try:
+        quality = int(input("Enter compression quality (0 - 95): "))
+        if quality < 0 or quality > 95:
+            raise ValueError("Compression quality must be between 0 and 95.")
+    except ValueError:
+        quality = 60  # Default value if invalid input
+
+    # Compress image
+    output_path = os.path.join(output_folder, 'compressed_image.png')
+    compress_image(input_path, output_path, quality)
+
+if __name__ == "__main__":
+    main()