successfully completed

Mallela.Bhavya

@@ -0,0 +1 @@
+

Mallela_Bhavya/Task10/TASK-10.py

@@ -0,0 +1,43 @@
+import seaborn as sns
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load the Iris dataset from Seaborn
+iris = sns.load_dataset("iris")
+numeric_iris = iris.drop(columns='species')
+
+# Display the first few rows of the dataset
+print("First few rows of the dataset:")
+print(iris.head())
+
+# Summary statistics
+print("\nSummary statistics:")
+print(iris.describe())
+
+# Checking for missing values
+print("\nMissing values:")
+print(iris.isnull().sum())
+
+# Visualizations
+# Pairplot
+sns.pairplot(iris, hue="species")
+plt.title("Pairplot of Iris Dataset")
+plt.show()
+
+# Boxplot
+plt.figure(figsize=(10, 6))
+sns.boxplot(data=iris, orient="h")
+plt.title("Boxplot of Iris Dataset")
+plt.show()
+
+# Histograms
+plt.figure(figsize=(10, 6))
+iris.hist()
+plt.suptitle("Histograms of Iris Dataset")
+plt.show()
+
+# Correlation heatmap
+plt.figure(figsize=(8, 6))
+sns.heatmap(numeric_iris.corr(), annot=True, cmap="coolwarm")
+plt.title("Correlation Heatmap of Iris Dataset")
+plt.show()

Mallela_Bhavya/Task11/TASK-11.py

@@ -0,0 +1,40 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
+# Fetch the Boston housing dataset from the original source
+data_url = "http://lib.stat.cmu.edu/datasets/boston"
+raw_df = pd.read_csv(data_url, sep=r"\s+", skiprows=22, header=None)
+data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
+target = raw_df.values[1::2, 2]
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)
+
+# Create and train the linear regression model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Make predictions on the training and testing sets
+y_train_pred = model.predict(X_train)
+y_test_pred = model.predict(X_test)
+
+# Calculate the mean squared error for training and testing sets
+train_mse = mean_squared_error(y_train, y_train_pred)
+test_mse = mean_squared_error(y_test, y_test_pred)
+
+print("Train MSE:", train_mse)
+print("Test MSE:", test_mse)
+
+# Plot residuals
+plt.scatter(y_train_pred, y_train_pred - y_train, c='blue', marker='o', label='Training data')
+plt.scatter(y_test_pred, y_test_pred - y_test, c='green', marker='s', label='Test data')
+plt.xlabel('Predicted values')
+plt.ylabel('Residuals')
+plt.legend(loc='upper left')
+plt.hlines(y=0, xmin=min(y_train_pred.min(), y_test_pred.min()), xmax=max(y_train_pred.max(), y_test_pred.max()), color='red')
+plt.title('Residuals plot')
+plt.show()

Mallela_Bhavya/Task12/TASK-12.py

@@ -0,0 +1,66 @@
+from PIL import Image
+import os
+
+def get_size_format(b, factor=1024, suffix="B"):
+    """
+    Scale bytes to its proper byte format.
+    e.g: 1253656 => '1.20MB', 1253656678 => '1.17GB'
+    """
+    for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]:
+        if b < factor:
+            return f"{b:.2f}{unit}{suffix}"
+        b /= factor
+    return f"{b:.2f}Y{suffix}"
+
+def compress_img(image_name, new_size_ratio=0.9, quality=90, width=None, height=None, to_jpg=True):
+    try:
+        # Load the image into memory
+        img = Image.open(image_name)
+
+        # Print the original image shape
+        print("[*] Image shape:", img.size)
+
+        # Get the original image size in bytes
+        image_size = os.path.getsize(image_name)
+        print("[*] Size before compression:", get_size_format(image_size))
+
+        if width and height:
+            # If width and height are set, resize with them instead
+            img = img.resize((width, height), Image.LANCZOS)
+        elif new_size_ratio < 1.0:
+            # If resizing ratio is below 1.0, multiply width & height with this ratio to reduce image size
+            img = img.resize((int(img.size[0] * new_size_ratio), int(img.size[1] * new_size_ratio)), Image.LANCZOS)
+
+        # Split the filename and extension
+        filename, ext = os.path.splitext(image_name)
+
+        # Make a new filename appending "_compressed" to the original file name
+        if to_jpg:
+            # Change the extension to JPEG
+            new_filename = f"{filename}_compressed.jpg"
+            # Ensure image is in RGB mode for JPEG
+            if img.mode in ("RGBA", "LA"):
+                img = img.convert("RGB")
+        else:
+            # Retain the same extension of the original image
+            new_filename = f"{filename}_compressed{ext}"
+
+        # Save the compressed image
+        img.save(new_filename, optimize=True, quality=quality)
+
+        # Print the new image shape
+        print("[+] New Image shape:", img.size)
+
+        # Get the new image size in bytes
+        new_image_size = os.path.getsize(new_filename)
+        print("[*] Size after compression:", get_size_format(new_image_size))
+        print(f"[*] Compressed image saved as: {new_filename}")
+
+    except FileNotFoundError:
+        print("Error: The file was not found.")
+    except OSError as e:
+        print(f"Error: {e}")
+
+# Example usage:
+input_image = input("Enter the path to the image: ")
+compress_img(input_image, new_size_ratio=0.8, quality=80, width=800, height=600)

Mallela_Bhavya/Task9/TASK-9.py

@@ -0,0 +1,37 @@
+from PIL import Image
+import os
+
+def convert_image(input_path, output_path, output_format):
+    try:
+        # Open the image
+        with Image.open(input_path) as img:
+            # Check if the image has an alpha channel and convert it to RGB if necessary
+            if output_format == 'JPEG' and img.mode == 'RGBA':
+                img = img.convert('RGB')
+
+            # Convert and save the image to the desired format
+            img.save(output_path, format=output_format)
+            print(f"Image converted successfully to {output_format} format.")
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+def main():
+    input_path = input("Enter the path to the input image: ")
+    output_format = input("Enter the desired output format (e.g., JPEG, PNG, BMP, GIF): ").upper()
+
+    # Validate output format
+    if output_format not in ['JPEG', 'PNG', 'BMP', 'GIF']:
+        print("Invalid output format. Please choose from JPEG, PNG, BMP, or GIF.")
+        return
+
+    # Extract the file name and extension
+    file_name, file_extension = os.path.splitext(input_path)
+
+    # Set the output path
+    output_path = f"{file_name}_converted.{output_format.lower()}"
+
+    # Convert the image
+    convert_image(input_path, output_path, output_format)
+
+if __name__ == "__main__":
+    main()

TASK-10.py

@@ -0,0 +1,43 @@
+import seaborn as sns
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load the Iris dataset from Seaborn
+iris = sns.load_dataset("iris")
+numeric_iris = iris.drop(columns='species')
+
+# Display the first few rows of the dataset
+print("First few rows of the dataset:")
+print(iris.head())
+
+# Summary statistics
+print("\nSummary statistics:")
+print(iris.describe())
+
+# Checking for missing values
+print("\nMissing values:")
+print(iris.isnull().sum())
+
+# Visualizations
+# Pairplot
+sns.pairplot(iris, hue="species")
+plt.title("Pairplot of Iris Dataset")
+plt.show()
+
+# Boxplot
+plt.figure(figsize=(10, 6))
+sns.boxplot(data=iris, orient="h")
+plt.title("Boxplot of Iris Dataset")
+plt.show()
+
+# Histograms
+plt.figure(figsize=(10, 6))
+iris.hist()
+plt.suptitle("Histograms of Iris Dataset")
+plt.show()
+
+# Correlation heatmap
+plt.figure(figsize=(8, 6))
+sns.heatmap(numeric_iris.corr(), annot=True, cmap="coolwarm")
+plt.title("Correlation Heatmap of Iris Dataset")
+plt.show()

TASK-11.py

@@ -0,0 +1,40 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
+# Fetch the Boston housing dataset from the original source
+data_url = "http://lib.stat.cmu.edu/datasets/boston"
+raw_df = pd.read_csv(data_url, sep=r"\s+", skiprows=22, header=None)
+data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
+target = raw_df.values[1::2, 2]
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)
+
+# Create and train the linear regression model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Make predictions on the training and testing sets
+y_train_pred = model.predict(X_train)
+y_test_pred = model.predict(X_test)
+
+# Calculate the mean squared error for training and testing sets
+train_mse = mean_squared_error(y_train, y_train_pred)
+test_mse = mean_squared_error(y_test, y_test_pred)
+
+print("Train MSE:", train_mse)
+print("Test MSE:", test_mse)
+
+# Plot residuals
+plt.scatter(y_train_pred, y_train_pred - y_train, c='blue', marker='o', label='Training data')
+plt.scatter(y_test_pred, y_test_pred - y_test, c='green', marker='s', label='Test data')
+plt.xlabel('Predicted values')
+plt.ylabel('Residuals')
+plt.legend(loc='upper left')
+plt.hlines(y=0, xmin=min(y_train_pred.min(), y_test_pred.min()), xmax=max(y_train_pred.max(), y_test_pred.max()), color='red')
+plt.title('Residuals plot')
+plt.show()

TASK-12.py

@@ -0,0 +1,66 @@
+from PIL import Image
+import os
+
+def get_size_format(b, factor=1024, suffix="B"):
+    """
+    Scale bytes to its proper byte format.
+    e.g: 1253656 => '1.20MB', 1253656678 => '1.17GB'
+    """
+    for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]:
+        if b < factor:
+            return f"{b:.2f}{unit}{suffix}"
+        b /= factor
+    return f"{b:.2f}Y{suffix}"
+
+def compress_img(image_name, new_size_ratio=0.9, quality=90, width=None, height=None, to_jpg=True):
+    try:
+        # Load the image into memory
+        img = Image.open(image_name)
+
+        # Print the original image shape
+        print("[*] Image shape:", img.size)
+
+        # Get the original image size in bytes
+        image_size = os.path.getsize(image_name)
+        print("[*] Size before compression:", get_size_format(image_size))
+
+        if width and height:
+            # If width and height are set, resize with them instead
+            img = img.resize((width, height), Image.LANCZOS)
+        elif new_size_ratio < 1.0:
+            # If resizing ratio is below 1.0, multiply width & height with this ratio to reduce image size
+            img = img.resize((int(img.size[0] * new_size_ratio), int(img.size[1] * new_size_ratio)), Image.LANCZOS)
+
+        # Split the filename and extension
+        filename, ext = os.path.splitext(image_name)
+
+        # Make a new filename appending "_compressed" to the original file name
+        if to_jpg:
+            # Change the extension to JPEG
+            new_filename = f"{filename}_compressed.jpg"
+            # Ensure image is in RGB mode for JPEG
+            if img.mode in ("RGBA", "LA"):
+                img = img.convert("RGB")
+        else:
+            # Retain the same extension of the original image
+            new_filename = f"{filename}_compressed{ext}"
+
+        # Save the compressed image
+        img.save(new_filename, optimize=True, quality=quality)
+
+        # Print the new image shape
+        print("[+] New Image shape:", img.size)
+
+        # Get the new image size in bytes
+        new_image_size = os.path.getsize(new_filename)
+        print("[*] Size after compression:", get_size_format(new_image_size))
+        print(f"[*] Compressed image saved as: {new_filename}")
+
+    except FileNotFoundError:
+        print("Error: The file was not found.")
+    except OSError as e:
+        print(f"Error: {e}")
+
+# Example usage:
+input_image = input("Enter the path to the image: ")
+compress_img(input_image, new_size_ratio=0.8, quality=80, width=800, height=600)

TASK-9.py

@@ -0,0 +1,37 @@
+from PIL import Image
+import os
+
+def convert_image(input_path, output_path, output_format):
+    try:
+        # Open the image
+        with Image.open(input_path) as img:
+            # Check if the image has an alpha channel and convert it to RGB if necessary
+            if output_format == 'JPEG' and img.mode == 'RGBA':
+                img = img.convert('RGB')
+
+            # Convert and save the image to the desired format
+            img.save(output_path, format=output_format)
+            print(f"Image converted successfully to {output_format} format.")
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+def main():
+    input_path = input("Enter the path to the input image: ")
+    output_format = input("Enter the desired output format (e.g., JPEG, PNG, BMP, GIF): ").upper()
+
+    # Validate output format
+    if output_format not in ['JPEG', 'PNG', 'BMP', 'GIF']:
+        print("Invalid output format. Please choose from JPEG, PNG, BMP, or GIF.")
+        return
+
+    # Extract the file name and extension
+    file_name, file_extension = os.path.splitext(input_path)
+
+    # Set the output path
+    output_path = f"{file_name}_converted.{output_format.lower()}"
+
+    # Convert the image
+    convert_image(input_path, output_path, output_format)
+
+if __name__ == "__main__":
+    main()

bhavya

@@ -0,0 +1 @@
+