From 301cb4c456b378304277ae26d9a34ef4bdad8873 Mon Sep 17 00:00:00 2001
From: Lakshmiprasanna8008
 <158503837+Lakshmiprasanna8008@users.noreply.github.com>
Date: Sun, 5 May 2024 19:01:18 +0530
Subject: [PATCH 1/5] Create Task1

---
 Lakshmi Prasanna Konda/Task1 | 36 ++++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 Lakshmi Prasanna Konda/Task1

diff --git a/Lakshmi Prasanna Konda/Task1 b/Lakshmi Prasanna Konda/Task1
new file mode 100644
index 0000000..8f8b98b
--- /dev/null
+++ b/Lakshmi Prasanna Konda/Task1	
@@ -0,0 +1,36 @@
+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:
+            # 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)
+
+    # If the input file already has an extension, remove it
+    file_name_without_ext = file_name.split('.')[0]
+
+    # Set the output path
+    output_path = f"{file_name_without_ext}_converted.{output_format.lower()}"
+    
+    # Convert the image
+    convert_image(input_path, output_path, output_format)
+
+if __name__ == "__main__":
+    main()

From 5c3cef51b21b9936e2540534ceff594ab9e63615 Mon Sep 17 00:00:00 2001
From: Lakshmiprasanna8008
 <158503837+Lakshmiprasanna8008@users.noreply.github.com>
Date: Sun, 5 May 2024 19:01:54 +0530
Subject: [PATCH 2/5] Create Task2

---
 Lakshmi Prasanna Konda/Task2 | 43 ++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)
 create mode 100644 Lakshmi Prasanna Konda/Task2

diff --git a/Lakshmi Prasanna Konda/Task2 b/Lakshmi Prasanna Konda/Task2
new file mode 100644
index 0000000..2fee7c7
--- /dev/null
+++ b/Lakshmi Prasanna Konda/Task2	
@@ -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()

From 79ab2ebd8996b90487aa09590bb36a8815ece42e Mon Sep 17 00:00:00 2001
From: Lakshmiprasanna8008
 <158503837+Lakshmiprasanna8008@users.noreply.github.com>
Date: Sun, 5 May 2024 19:03:25 +0530
Subject: [PATCH 3/5] Create Task3

---
 Lakshmi Prasanna Konda/Task3 | 45 ++++++++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)
 create mode 100644 Lakshmi Prasanna Konda/Task3

diff --git a/Lakshmi Prasanna Konda/Task3 b/Lakshmi Prasanna Konda/Task3
new file mode 100644
index 0000000..5bb8616
--- /dev/null
+++ b/Lakshmi Prasanna Konda/Task3	
@@ -0,0 +1,45 @@
+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
+import pandas as pd
+data_url = "http://lib.stat.cmu.edu/datasets/boston"
+raw_df = pd.read_csv(data_url, sep="\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]
+
+# Load the Boston housing dataset
+ 
+X = data
+y = 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)
+
+# Initialize the linear regression model
+model = LinearRegression()
+
+# Fit the model on the training data
+model.fit(X_train, y_train)
+
+# Predict on the training and testing data
+y_train_pred = model.predict(X_train)
+y_test_pred = model.predict(X_test)
+
+# Calculate the scores
+train_score = model.score(X_train, y_train)
+test_score = model.score(X_test, y_test)
+
+print("Training score:", train_score)
+print("Testing score:", test_score)
+
+# 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='lightgreen', marker='s', label='Testing data')
+plt.xlabel('Predicted values')
+plt.ylabel('Residuals')
+plt.legend(loc='upper left')
+plt.hlines(y=0, xmin=0, xmax=50, lw=2, color='red')
+plt.title('Residual plot')
+plt.show()

From 26f80186950ecb4bbeacaaaa6b8b6091758553d5 Mon Sep 17 00:00:00 2001
From: Lakshmiprasanna8008
 <158503837+Lakshmiprasanna8008@users.noreply.github.com>
Date: Sun, 5 May 2024 19:11:09 +0530
Subject: [PATCH 4/5] Create Task4

---
 Lakshmi Prasanna Konda/Task4 | 53 ++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 Lakshmi Prasanna Konda/Task4

diff --git a/Lakshmi Prasanna Konda/Task4 b/Lakshmi Prasanna Konda/Task4
new file mode 100644
index 0000000..c337023
--- /dev/null
+++ b/Lakshmi Prasanna Konda/Task4	
@@ -0,0 +1,53 @@
+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):
+    # 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 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.ANTIALIAS)
+    elif width and height:
+        # If width and height are set, resize with them instead
+        img = img.resize((width, height), Image.ANTIALIAS)
+
+    # 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"
+    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)
+    print(f"[*] Compressed image saved as: {new_filename}")
+
+# Example usage:
+Input=input()
+compress_img(Input, new_size_ratio=0.8, quality=80, width=800, height=600)

From 954e4e9b399970b01a6fa2c3296dade713b8e2ed Mon Sep 17 00:00:00 2001
From: Lakshmiprasanna8008
 <158503837+Lakshmiprasanna8008@users.noreply.github.com>
Date: Sun, 5 May 2024 19:13:22 +0530
Subject: [PATCH 5/5] Create README.md

---
 Lakshmi Prasanna Konda/README.md | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 Lakshmi Prasanna Konda/README.md

diff --git a/Lakshmi Prasanna Konda/README.md b/Lakshmi Prasanna Konda/README.md
new file mode 100644
index 0000000..dc62a02
--- /dev/null
+++ b/Lakshmi Prasanna Konda/README.md	
@@ -0,0 +1,20 @@
+Advance Level 
+
+Task 1 :Image Converter:
+
+Where the  program that accepts images in multiple formats that may be in JPEG, PNG, BMP, GIF and converts them into a desired format using Python Imaging Library (PIL) that may be any of the formats mentioned.
+
+
+Task 2:Data Analysis with Pandas:
+
+In the program on loading  the "Iris" dataset from Seaborn and analyzing it using Pandas and performed exploratory data analysis, cleaning, aggregation, visualizations, and correlation calculations.
+The output of the program will consits of calculations , graph which will be more understanding to users.
+
+
+Task 3:Linear Regression with Scikit-learn:
+
+Completed a program by applying linear regression to predict house prices from the Boston housing dataset using scikit-learn and compared train and test scores and plot residuals.
+
+Task 4:Image Compression:
+
+Developed a Python program for compressing images while maintaining quality by exploring compression techniques like RLE and DCT and allowing  users to adjust compression quality, support various image formats, and provide output options. Optionally, include a user interface.