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.
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