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# Customer Churn Analysis

This notebook analyzes customer churn patterns using machine learning techniques.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Load the dataset
df = pd.read_csv('datasets/customer_data.csv')
print(f'Dataset shape: {df.shape}')

# Data exploration
print('Dataset Info:')
print(df.info())
print('\nFirst 5 rows:')
df.head()

# Feature engineering and model training
X = df.drop(['customer_id', 'churn'], axis=1)
y = df['churn']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train Random Forest model
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)