yuvaranithulasingam / odd2023-datascience-ex-07

Ex-07-Feature-Selection

AIM

To Perform the various feature selection techniques on a dataset and save the data to a file.

Explanation

Feature selection is to find the best set of features that allows one to build useful models. Selecting the best features helps the model to perform well.

ALGORITHM

STEP 1

Read the given Data

STEP 2

Clean the Data Set using Data Cleaning Process

STEP 3

Apply Feature selection techniques to all the features of the data set

STEP 4

Save the data to the file

CODE

DEVELOPED BY: Yuvarani T

Register no:212222110057

Importing library

import pandas as pd
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

Data loading

data = pd.read_csv('/content/titanic_dataset.csv')
data
data.tail()
data.isnull().sum()
data.describe()

Now, we are checking start with a pairplot, and check for missing values

sns.heatmap(data.isnull(),cbar=False)

Data Cleaning and Data Drop Process

data['Fare'] = data['Fare'].fillna(data['Fare'].dropna().median())
data['Age'] = data['Age'].fillna(data['Age'].dropna().median())

Change to categoric column to numeric

data.loc[data['Sex']=='male','Sex']=0
data.loc[data['Sex']=='female','Sex']=1

Instead of nan values

data['Embarked']=data['Embarked'].fillna('S')

Change to categoric column to numeric

data.loc[data['Embarked']=='S','Embarked']=0
data.loc[data['Embarked']=='C','Embarked']=1
data.loc[data['Embarked']=='Q','Embarked']=2

Drop unnecessary columns

drop_elements = ['Name','Cabin','Ticket']
data = data.drop(drop_elements, axis=1)
data.head(11)

Heatmap for train dataset

f,ax = plt.subplots(figsize=(5, 5))
sns.heatmap(data.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)

Now, data is clean and read to a analyze

sns.heatmap(data.isnull(),cbar=False)

How many people survived or not... %60 percent died %40 percent survived

fig = plt.figure(figsize=(18,6))
data.Survived.value_counts(normalize=True).plot(kind='bar',alpha=0.5)
plt.show()

Age with survived

plt.scatter(data.Survived, data.Age, alpha=0.1)
plt.title("Age with Survived")
plt.show()

Count the pessenger class

fig = plt.figure(figsize=(18,6))
data.Pclass.value_counts(normalize=True).plot(kind='bar',alpha=0.5)
plt.show()

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2

X = data.drop("Survived",axis=1)
y = data["Survived"]

mdlsel = SelectKBest(chi2, k=5)
mdlsel.fit(X,y)
ix = mdlsel.get_support()
data2 = pd.DataFrame(mdlsel.transform(X), columns = X.columns.values[ix]) # en iyi leri aldi... 7 tane...

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

target = data['Survived'].values
data_features_names = ['Pclass','Sex','SibSp','Parch','Fare','Embarked','Age']
features = data[data_features_names].values

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, mean_squared_error, r2_score

Split the data into training and test sets

X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.3, random_state=42)

Create a Random Forest classifier

my_forest = RandomForestClassifier(max_depth=5, min_samples_split=10, n_estimators=500, random_state=5, criterion='entropy')

Fit the model to the training data

my_forest.fit(X_train, y_train)

Make predictions on the test data

target_predict = my_forest.predict(X_test)

Evaluate the model's performance

accuracy = accuracy_score(y_test, target_predict)
mse = mean_squared_error(y_test, target_predict)
r2 = r2_score(y_test, target_predict)

print("Random forest accuracy: ", accuracy)
print("Mean Squared Error (MSE): ", mse)
print("R-squared (R2) Score: ", r2)

OUPUT

Initial data

Null values

Describing the data

Missing values

Data after cleaning

Data on Heatmap

Report of(people survied & died)

Cleaned null values

Report of survied people's age

Report of pessengers

Report

RESULT:

Thus, Sucessfully performed the various feature selection techniques on a given dataset.