diff --git a/Naga Sathvik Rapelli/Hard Tasks/Task 10/task10.py b/Naga Sathvik Rapelli/Hard Tasks/Task 10/task10.py new file mode 100644 index 0000000..3b017b1 --- /dev/null +++ b/Naga Sathvik Rapelli/Hard Tasks/Task 10/task10.py @@ -0,0 +1,33 @@ +#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() diff --git a/Naga Sathvik Rapelli/Hard Tasks/Task 11/task11.py b/Naga Sathvik Rapelli/Hard Tasks/Task 11/task11.py new file mode 100644 index 0000000..041d1fb --- /dev/null +++ b/Naga Sathvik Rapelli/Hard Tasks/Task 11/task11.py @@ -0,0 +1,52 @@ +#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() diff --git a/Naga Sathvik Rapelli/Hard Tasks/Task 12/compressed_images/compressed_image.jpg b/Naga Sathvik Rapelli/Hard Tasks/Task 12/compressed_images/compressed_image.jpg new file mode 100644 index 0000000..d2e6dd5 Binary files /dev/null and b/Naga Sathvik Rapelli/Hard Tasks/Task 12/compressed_images/compressed_image.jpg differ diff --git a/Naga Sathvik Rapelli/Hard Tasks/Task 12/task12.py b/Naga Sathvik Rapelli/Hard Tasks/Task 12/task12.py new file mode 100644 index 0000000..c93fcdc --- /dev/null +++ b/Naga Sathvik Rapelli/Hard Tasks/Task 12/task12.py @@ -0,0 +1,39 @@ +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() + diff --git a/Naga Sathvik Rapelli/Hard Tasks/Task 9/output_images/Motive.png b/Naga Sathvik Rapelli/Hard Tasks/Task 9/output_images/Motive.png new file mode 100644 index 0000000..ec6677b Binary files /dev/null and b/Naga Sathvik Rapelli/Hard Tasks/Task 9/output_images/Motive.png differ diff --git a/Naga Sathvik Rapelli/Hard Tasks/Task 9/output_images/WhatsApp Image 2024-02-15 at 06.43.27_0a95261b.png b/Naga Sathvik Rapelli/Hard Tasks/Task 9/output_images/WhatsApp Image 2024-02-15 at 06.43.27_0a95261b.png new file mode 100644 index 0000000..0eaef82 Binary files /dev/null and b/Naga Sathvik Rapelli/Hard Tasks/Task 9/output_images/WhatsApp Image 2024-02-15 at 06.43.27_0a95261b.png differ diff --git a/Naga Sathvik Rapelli/Hard Tasks/Task 9/task9.py b/Naga Sathvik Rapelli/Hard Tasks/Task 9/task9.py new file mode 100644 index 0000000..af620b9 --- /dev/null +++ b/Naga Sathvik Rapelli/Hard Tasks/Task 9/task9.py @@ -0,0 +1,28 @@ +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)