Feature Selection Case Study in Data Science
In this article, I am going to discuss one Feature Selection Case Study in Data Science with Examples. Please read our previous article where we discussed Feature Selection in Data Science with Examples.
A Feature Selection Case Study
We are about to work on the Pokemon Dataset here for this case study.
Data Description – This dataset includes statistics on all 802 Pokemon from all seven generations of the Pokemon franchise. Base stats, performance against other types, height, weight, classification, egg steps, experience points, abilities, and whether or not a pokemon is legendary are all included in this dataset.
Dataset Link – here
# Import Necessary Libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # Import dataset and display first 5 records df = pd.read_csv('pokemon.csv') df.head()
# Let's check the info of dataset df.info()
# Conclusion - # Shape of the dataset - 801 rows, 41 columns # Numerical Features - 34 # Categorical Features - 7 # Now, let’s check missing data df.isnull().sum()
# Here we have 1 categorical and 3 numerical features with missing values # Handling Missing Values - Categorical df['type2'].fillna('Unknown', inplace=True) # Handling Missing Values - Numerical cols = ['height_m','percentage_male','weight_kg'] for c in cols: df[c].fillna(df[c].mean(), inplace=True) # Let's convert the categorical features to numerical cols = ['abilities','capture_rate', 'classfication', 'japanese_name', 'name', 'type1', 'type2'] from sklearn.preprocessing import LabelEncoder lb = LabelEncoder() for c in cols: df[c] = lb.fit_transform(df[c]) # Method 1 - Check correlation and drop features with high correlation plt.figure(figsize=(20, 15)) sns.heatmap(df.corr(), annot=True)
# We will check and drop all the features with correlation greater than 0.8 # creating an empty set for storing correlated features correlated_features = set() # creating correlation matrix correlation_matrix = df.corr() for i in range(len(correlation_matrix .columns)): for j in range(i): if abs(correlation_matrix.iloc[i, j]) > 0.8: colname = correlation_matrix.columns[i] correlated_features.add(colname) correlated_features
# Here we can see according to results # we can drop only 1 feature i.e. generation df.drop('generation', axis=1, inplace=True)
# Method 2- Now let's check the feature importance # And drop features accordingly # Splitting data into test and train sets from sklearn.model_selection import train_test_split y = df.pop('is_legendary') x = df X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=0) # fitting the model from sklearn.ensemble import RandomForestClassifier classifier = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42) classifier.fit(X_train, y_train) # plotting feature importances features = df.columns importances = classifier.feature_importances_ indices = np.argsort(importances) plt.figure(figsize=(10,15)) plt.title('Feature Importances') plt.barh(range(len(indices)), importances[indices], color='b', align='center') plt.yticks(range(len(indices)), [features[i] for i in indices]) plt.xlabel('Relative Importance') plt.show()
# Let's keep only top 10 features all_feat = [features[i] for i in indices] all_feat = all_feat[::-1] top_10 = all_feat[:10] top_10
# drop rest of the features other_feat = all_feat[10:] df.drop(other_feat, axis=1, inplace=True)
In the next article, I am going to discuss Data Preprocessing in Data Science with Examples. Here, in this article, I try to explain One Feature Selection Case Study in Data Science with Examples. I hope you enjoy this Feature Selection Case Study in Data Science with Examples article.
About the Author: Pranaya Rout
Pranaya Rout has published more than 3,000 articles in his 11-year career. Pranaya Rout has very good experience with Microsoft Technologies, Including C#, VB, ASP.NET MVC, ASP.NET Web API, EF, EF Core, ADO.NET, LINQ, SQL Server, MYSQL, Oracle, ASP.NET Core, Cloud Computing, Microservices, Design Patterns and still learning new technologies.