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Practical 3

The document outlines a Python script for analyzing the 'auto-mpg' dataset using pandas, numpy, and scikit-learn. It includes data loading, preprocessing, handling missing values, feature selection with SelectKBest, and visualization of feature scores. The script concludes by identifying the top three relevant features based on their scores.

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18 views2 pages

Practical 3

The document outlines a Python script for analyzing the 'auto-mpg' dataset using pandas, numpy, and scikit-learn. It includes data loading, preprocessing, handling missing values, feature selection with SelectKBest, and visualization of feature scores. The script concludes by identifying the top three relevant features based on their scores.

Uploaded by

try.adityabhise
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as TXT, PDF, TXT or read online on Scribd
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import pandas as pd

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectKBest, f_regression

# Load the dataset


df = pd.read_csv('auto-mpg.csv')

# Display the first few rows of the dataset


print("Dataset Preview:")
print(df.head())

# Check for null values


print("\nNull Values:")
print(df.isnull().sum())

# Basic information about the dataset


print("\nDataset Info:")
print(df.info())

# Handle missing values (optional, depending on dataset)


df = df.dropna()

# Remove non-numeric columns like 'car name' (or any other categorical columns)
df = df.drop(columns=['car name']) # Dropping the 'car name' column

# If there are any other categorical columns, encode them (e.g., using one-hot
encoding)
# df = pd.get_dummies(df, drop_first=True) # Uncomment if you have categorical
features

# Splitting into features (X) and target (y)


X = df.drop(columns=['mpg']) # Assuming 'mpg' is the target variable
y = df['mpg']

# Feature selection using SelectKBest


from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_regression

selector = SelectKBest(score_func=f_regression, k='all')


X_new = selector.fit_transform(X, y)

# Display feature scores


feature_scores = pd.DataFrame({'Feature': X.columns, 'Score': selector.scores_})
print("\nFeature Scores:")
print(feature_scores)

# Visualizing feature scores


import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10, 6))
sns.barplot(x='Feature', y='Score', data=feature_scores)
plt.title('Feature Selection Scores')
plt.xticks(rotation=45)
plt.show()
# Selecting top 3 features (example)
top_features = feature_scores.nlargest(3, 'Score')['Feature'].tolist()
print("\nTop 3 Relevant Features:")
print(top_features)

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