Implementing Logistic Regression and Naive Bayes in Python

Logistic Regression Implementation

1. Upload and Prepare Dataset

from google.colab import files
uploaded = files.upload()

2. Import Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix

3. Load and Split Data

data = pd.read_csv("Social_Network_Ads.csv")
X = data[['Age','EstimatedSalary']]
y = data['Purchased']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

4. Feature Scaling and Model Training

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

5. Performance Evaluation

Calculate the confusion matrix and key metrics:

• Accuracy: (TP + TN) / Total
• Error Rate: (FP + FN) / Total
• Precision: TP / (TP + FP)
• Recall: TP / (TP + FN)

Gaussian Naive Bayes Implementation

1. Load Iris Dataset

from sklearn.datasets import load_iris
iris = load_iris()
X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = pd.Series(iris.target)

2. Model Training and Prediction

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=42)
model = GaussianNB()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

3. Performance Metrics

The model performance is evaluated using accuracy, error rate, precision, and recall scores.