Add files via upload (#21)

Author: Nagasathvik

Naga Sathvik Rapelli/Hard Tasks/Task 10/task10.py

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+#1. Load the DataSet:
+import seaborn as sns
+iris_df = sns.load_dataset('iris')
+
+#2. Exploratory Data Analysis (EDA):
+
+print(iris_df.info())
+print(iris_df.describe())
+print(iris_df.head())
+
+#3. Data Cleaning:
+
+print(iris_df.isnull().sum())
+print(iris_df.duplicated().sum())
+
+#4. Aggregation:
+
+species_mean = iris_df.groupby('species').mean()
+
+#5. Visualizations:
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.pairplot(iris_df, hue='species')
+plt.show()
+
+sns.heatmap(iris_df.corr(), annot=True, cmap='coolwarm')
+plt.show()
+
+#6. Correlation Calculations:
+
+correlation_matrix = iris_df.corr()

Naga Sathvik Rapelli/Hard Tasks/Task 11/task11.py

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+#1. Load the Dataset:
+
+from sklearn.datasets import load_boston
+boston = load_boston()
+
+#2. Prepare the Data:
+
+import pandas as pd
+
+boston_df = pd.DataFrame(boston.data, columns=boston.feature_names)
+boston_df['PRICE'] = boston.target
+
+X = boston_df.drop('PRICE', axis=1)
+y = boston_df['PRICE']
+
+#3. Split the Data:
+
+from sklearn.model_selection import train_test_split
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+#4. Train the Model:
+
+from sklearn.linear_model import LinearRegression
+
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+#5. Evaluate the Model:
+
+train_score = model.score(X_train, y_train)
+print(f'Training Score: {train_score}')
+
+test_score = model.score(X_test, y_test)
+print(f'Testing Score: {test_score}')
+
+#6. Plot Residuals:
+
+import matplotlib.pyplot as plt
+
+train_residuals = y_train - model.predict(X_train)
+test_residuals = y_test - model.predict(X_test)
+
+plt.figure(figsize=(10, 5))
+plt.scatter(model.predict(X_train), train_residuals, label='Train Residuals', alpha=0.5)
+plt.scatter(model.predict(X_test), test_residuals, label='Test Residuals', alpha=0.5)
+plt.axhline(y=0, color='r', linestyle='--')
+plt.xlabel('Predicted Values')
+plt.ylabel('Residuals')
+plt.title('Residual Plot')
+plt.legend()
+plt.show()

Naga Sathvik Rapelli/Hard Tasks/Task 12/compressed_images/compressed_image.jpg

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+from PIL import Image
+import os
+
+def compress_image(input_path, output_path, quality=60):
+    """
+    Compresses an input image while maintaining quality.
+
+    Parameters:
+        input_path (str): Path to the input image file.
+        output_path (str): Path to save the compressed image file.
+        quality (int): Compression quality (0 - 95). Default is 60.
+    
+    Returns:
+        None
+    """
+    input_image = Image.open(input_path)
+
+    if input_image.mode == 'RGBA':
+        input_image = input_image.convert('RGB')
+
+    compressed_image = input_image.copy()
+    compressed_image.save(output_path, quality=quality)
+
+    print(f"Compressed image saved at: {output_path}")
+
+def main():
+    input_path = 'C:/Users/SATHVIK/OneDrive/Desktop/Motive.png'
+    output_folder = 'compressed_images'
+    os.makedirs(output_folder, exist_ok=True) 
+
+    quality = 60
+
+    # Compress image
+    output_path = os.path.join(output_folder, 'compressed_image.jpg')
+    compress_image(input_path, output_path, quality)
+
+if __name__ == "__main__":
+    main()
+

Naga Sathvik Rapelli/Hard Tasks/Task 12/task12.py

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+from PIL import Image
+import os
+
+def convert_image(input_path, output_path, output_format):
+    try:
+        with Image.open(input_path) as img:
+            img.save(output_path, format=output_format)
+        print(f"Image converted successfully: {input_path} -> {output_path}")
+    except Exception as e:
+        print(f"Error converting image: {e}")
+
+def main(input_folder, output_folder, output_format):
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+
+    for filename in os.listdir(input_folder):
+        input_path = os.path.join(input_folder, filename)
+
+        if os.path.isfile(input_path) and any(filename.lower().endswith(ext) for ext in ['.jpg', '.jpeg', '.png', '.bmp', '.gif']):
+            output_filename = os.path.splitext(filename)[0] + '.' + output_format.lower()
+            output_path = os.path.join(output_folder, output_filename)
+
+            convert_image(input_path, output_path, output_format)
+
+input_folder = 'C:/Users/SATHVIK/OneDrive/Desktop'
+output_folder = 'output_images'
+output_format = 'PNG'
+main(input_folder, output_folder, output_format)