Need help with machine learning and my python. It won't run properly. # Import necessary librariesimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsfrom pandas.plotting import scatter_matrixfrom sklearn.preprocessing import StandardScaler# Load the Boston datasetboston_data = pd.read_csv('MultipleFiles/boston.csv')# Display the first few rows of the datasetprint("First few rows of the Boston dataset:")print(boston_data.head())# Shape of the Datasetprint("\nShape of the dataset:", boston_data.shape)# Column Namesprint("\nColumn names:", boston_data.columns)# Data Typesprint("\nData types:\n", boston_data.dtypes)# Descriptive Statisticsdescription = boston_data.describe()print("\nDescriptive statistics:\n", description)# Plot histograms for each featureboston_data.hist(bins=30, figsize=(15, 10))plt.tight_layout()plt.show()# Calculate the correlation matrixcorrelation_matrix = boston_data.corr()# Plot the heatmapplt.figure(figsize=(12, 8))sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm')plt.title("Correlation Matrix")plt.show()# Scatter matrix for visualizing relationshipsscatter_matrix(boston_data, figsize=(15, 15), alpha=0.5)plt.show()# Check for missing valuesmissing_values = boston_data.isnull().sum()print("\nMissing values in each column:\n", missing_values)# Features and target variableX = boston_data.drop('medv', axis=1) # Featuresy = boston_data['medv'] # Target variable# Standardize the featuresscaler = StandardScaler()X_scaled = scaler.fit_transform(X)# Convert back to DataFrameX_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)print("\nFirst few rows of the standardized features:\n", X_scaled_df.head())# Summary of Findingsprint("\nKey Insights from EDA:")print("- Identify which features have the strongest correlation with the target variable (medv).")print("- Visualize distributions to understand the spread and skewness of the data.")print("- Check for any outliers that may affect the model performance.")# Conclusionprint("\nThis EDA provides a comprehensive overview of the Boston dataset, highlighting key statistics, visualizations, and potential areas for further analysis or modeling.")
Need help with machine learning and my python. It won't run properly. # Import necessary librariesimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsfrom pandas.plotting import scatter_matrixfrom sklearn.preprocessing import StandardScaler# Load the Boston datasetboston_data = pd.read_csv('MultipleFiles/boston.csv')# Display the first few rows of the datasetprint("First few rows of the Boston dataset:")print(boston_data.head())# Shape of the Datasetprint("\nShape of the dataset:", boston_data.shape)# Column Namesprint("\nColumn names:", boston_data.columns)# Data Typesprint("\nData types:\n", boston_data.dtypes)# Descriptive Statisticsdescription = boston_data.describe()print("\nDescriptive statistics:\n", description)# Plot histograms for each featureboston_data.hist(bins=30, figsize=(15, 10))plt.tight_layout()plt.show()# Calculate the correlation matrixcorrelation_matrix = boston_data.corr()# Plot the heatmapplt.figure(figsize=(12, 8))sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm')plt.title("Correlation Matrix")plt.show()# Scatter matrix for visualizing relationshipsscatter_matrix(boston_data, figsize=(15, 15), alpha=0.5)plt.show()# Check for missing valuesmissing_values = boston_data.isnull().sum()print("\nMissing values in each column:\n", missing_values)# Features and target variableX = boston_data.drop('medv', axis=1) # Featuresy = boston_data['medv'] # Target variable# Standardize the featuresscaler = StandardScaler()X_scaled = scaler.fit_transform(X)# Convert back to DataFrameX_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)print("\nFirst few rows of the standardized features:\n", X_scaled_df.head())# Summary of Findingsprint("\nKey Insights from EDA:")print("- Identify which features have the strongest correlation with the target variable (medv).")print("- Visualize distributions to understand the spread and skewness of the data.")print("- Check for any outliers that may affect the model performance.")# Conclusionprint("\nThis EDA provides a comprehensive overview of the Boston dataset, highlighting key statistics, visualizations, and potential areas for further analysis or modeling.")
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Need help with machine learning and my python.
It won't run properly.
# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
# Load the Boston dataset
boston_data = pd.read_csv('MultipleFiles/boston.csv')
# Display the first few rows of the dataset
print("First few rows of the Boston dataset:")
print(boston_data.head())
# Shape of the Dataset
print("\nShape of the dataset:", boston_data.shape)
# Column Names
print("\nColumn names:", boston_data.columns)
# Data Types
print("\nData types:\n", boston_data.dtypes)
# Descriptive Statistics
description = boston_data.describe()
print("\nDescriptive statistics:\n", description)
# Plot histograms for each feature
boston_data.hist(bins=30, figsize=(15, 10))
plt.tight_layout()
plt.show()
# Calculate the correlation matrix
correlation_matrix = boston_data.corr()
# Plot the heatmap
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm')
plt.title("Correlation Matrix")
plt.show()
# Scatter matrix for visualizing relationships
scatter_matrix(boston_data, figsize=(15, 15), alpha=0.5)
plt.show()
# Check for missing values
missing_values = boston_data.isnull().sum()
print("\nMissing values in each column:\n", missing_values)
# Features and target variable
X = boston_data.drop('medv', axis=1) # Features
y = boston_data['medv'] # Target variable
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Convert back to DataFrame
X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)
print("\nFirst few rows of the standardized features:\n", X_scaled_df.head())
# Summary of Findings
print("\nKey Insights from EDA:")
print("- Identify which features have the strongest correlation with the target variable (medv).")
print("- Visualize distributions to understand the spread and skewness of the data.")
print("- Check for any outliers that may affect the model performance.")
# Conclusion
print("\nThis EDA provides a comprehensive overview of the Boston dataset, highlighting key statistics, visualizations, and potential areas for further analysis or modeling.")
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
# Load the Boston dataset
boston_data = pd.read_csv('MultipleFiles/boston.csv')
# Display the first few rows of the dataset
print("First few rows of the Boston dataset:")
print(boston_data.head())
# Shape of the Dataset
print("\nShape of the dataset:", boston_data.shape)
# Column Names
print("\nColumn names:", boston_data.columns)
# Data Types
print("\nData types:\n", boston_data.dtypes)
# Descriptive Statistics
description = boston_data.describe()
print("\nDescriptive statistics:\n", description)
# Plot histograms for each feature
boston_data.hist(bins=30, figsize=(15, 10))
plt.tight_layout()
plt.show()
# Calculate the correlation matrix
correlation_matrix = boston_data.corr()
# Plot the heatmap
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm')
plt.title("Correlation Matrix")
plt.show()
# Scatter matrix for visualizing relationships
scatter_matrix(boston_data, figsize=(15, 15), alpha=0.5)
plt.show()
# Check for missing values
missing_values = boston_data.isnull().sum()
print("\nMissing values in each column:\n", missing_values)
# Features and target variable
X = boston_data.drop('medv', axis=1) # Features
y = boston_data['medv'] # Target variable
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Convert back to DataFrame
X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)
print("\nFirst few rows of the standardized features:\n", X_scaled_df.head())
# Summary of Findings
print("\nKey Insights from EDA:")
print("- Identify which features have the strongest correlation with the target variable (medv).")
print("- Visualize distributions to understand the spread and skewness of the data.")
print("- Check for any outliers that may affect the model performance.")
# Conclusion
print("\nThis EDA provides a comprehensive overview of the Boston dataset, highlighting key statistics, visualizations, and potential areas for further analysis or modeling.")
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