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Author: RadoslavPetkow

Diamond Prices Data Analysis/Diamond Prices Data Analysis.py

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+import pandas as pd
+import seaborn as sns
+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, r2_score
+
+diamonds = pd.read_csv('diamonds.csv')
+
+print(diamonds.head())
+print(diamonds.info())
+print(diamonds.describe())
+
+diamonds = diamonds.dropna()
+
+sns.scatterplot(x='carat', y='price', data=diamonds)
+plt.title('Carat vs Price')
+plt.show()
+
+# Select only numeric columns for correlation analysis
+numeric_columns = diamonds.select_dtypes(include=['number']).columns
+correlation_matrix = diamonds[numeric_columns].corr()
+
+sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
+plt.title('Correlation Matrix')
+plt.show()
+
+X = diamonds[['carat', 'depth', 'table', 'x', 'y', 'z']]  # Exclude non-numeric columns
+y = diamonds['price']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+y_pred = model.predict(X_test)
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+
+print(f'Mean Squared Error: {mse}')
+print(f'R-squared: {r2}')