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

Commit 243005c

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Add files via upload (#21)
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#1. Load the DataSet:
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import seaborn as sns
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iris_df = sns.load_dataset('iris')
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#2. Exploratory Data Analysis (EDA):
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print(iris_df.info())
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print(iris_df.describe())
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print(iris_df.head())
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#3. Data Cleaning:
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print(iris_df.isnull().sum())
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print(iris_df.duplicated().sum())
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#4. Aggregation:
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species_mean = iris_df.groupby('species').mean()
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#5. Visualizations:
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import matplotlib.pyplot as plt
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import seaborn as sns
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sns.pairplot(iris_df, hue='species')
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plt.show()
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sns.heatmap(iris_df.corr(), annot=True, cmap='coolwarm')
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plt.show()
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#6. Correlation Calculations:
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correlation_matrix = iris_df.corr()
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#1. Load the Dataset:
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from sklearn.datasets import load_boston
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boston = load_boston()
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#2. Prepare the Data:
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import pandas as pd
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boston_df = pd.DataFrame(boston.data, columns=boston.feature_names)
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boston_df['PRICE'] = boston.target
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X = boston_df.drop('PRICE', axis=1)
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y = boston_df['PRICE']
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#3. Split the Data:
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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#4. Train the Model:
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from sklearn.linear_model import LinearRegression
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model = LinearRegression()
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model.fit(X_train, y_train)
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#5. Evaluate the Model:
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train_score = model.score(X_train, y_train)
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print(f'Training Score: {train_score}')
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test_score = model.score(X_test, y_test)
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print(f'Testing Score: {test_score}')
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#6. Plot Residuals:
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import matplotlib.pyplot as plt
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train_residuals = y_train - model.predict(X_train)
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test_residuals = y_test - model.predict(X_test)
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plt.figure(figsize=(10, 5))
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plt.scatter(model.predict(X_train), train_residuals, label='Train Residuals', alpha=0.5)
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plt.scatter(model.predict(X_test), test_residuals, label='Test Residuals', alpha=0.5)
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plt.axhline(y=0, color='r', linestyle='--')
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plt.xlabel('Predicted Values')
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plt.ylabel('Residuals')
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plt.title('Residual Plot')
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plt.legend()
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plt.show()
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from PIL import Image
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import os
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def compress_image(input_path, output_path, quality=60):
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"""
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Compresses an input image while maintaining quality.
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Parameters:
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input_path (str): Path to the input image file.
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output_path (str): Path to save the compressed image file.
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quality (int): Compression quality (0 - 95). Default is 60.
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Returns:
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None
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"""
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input_image = Image.open(input_path)
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if input_image.mode == 'RGBA':
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input_image = input_image.convert('RGB')
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compressed_image = input_image.copy()
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compressed_image.save(output_path, quality=quality)
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print(f"Compressed image saved at: {output_path}")
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def main():
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input_path = 'C:/Users/SATHVIK/OneDrive/Desktop/Motive.png'
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output_folder = 'compressed_images'
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os.makedirs(output_folder, exist_ok=True)
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quality = 60
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# Compress image
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output_path = os.path.join(output_folder, 'compressed_image.jpg')
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compress_image(input_path, output_path, quality)
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if __name__ == "__main__":
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main()
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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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with Image.open(input_path) as img:
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img.save(output_path, format=output_format)
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print(f"Image converted successfully: {input_path} -> {output_path}")
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except Exception as e:
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print(f"Error converting image: {e}")
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def main(input_folder, output_folder, output_format):
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if not os.path.exists(output_folder):
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os.makedirs(output_folder)
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for filename in os.listdir(input_folder):
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input_path = os.path.join(input_folder, filename)
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if os.path.isfile(input_path) and any(filename.lower().endswith(ext) for ext in ['.jpg', '.jpeg', '.png', '.bmp', '.gif']):
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output_filename = os.path.splitext(filename)[0] + '.' + output_format.lower()
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output_path = os.path.join(output_folder, output_filename)
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convert_image(input_path, output_path, output_format)
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input_folder = 'C:/Users/SATHVIK/OneDrive/Desktop'
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output_folder = 'output_images'
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output_format = 'PNG'
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main(input_folder, output_folder, output_format)

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