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This repository was archived by the owner on Jun 29, 2024. It is now read-only.

Commit 6e86268

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Merge remote-tracking branch 'origin/main'
2 parents 5b72b07 + 39ee561 commit 6e86268

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‎TASK-10.py‎

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

‎TASK-11.py‎

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

‎TASK-12.py‎

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

‎TASK-9.py‎

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

‎bhavya‎

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