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In this project, we will develop a machine learning model using Python to predict customer churn for a telecommunications company. The dataset includes customer demographics, account information, and service usage patterns. Here’s a detailed step-by-step explanation of the entire process:
First, we need to load the dataset and perform data cleaning and preprocessing tasks:
# Sample Python code for data cleaning and preprocessingimport pandas as pdfrom sklearn.preprocessing import StandardScaler, OneHotEncoderfrom sklearn.compose import ColumnTransformerfrom sklearn.impute import SimpleImputer# Load datasetdf = pd.read_csv('telecom_customer_churn.csv')# Handle missing valuesimputer = SimpleImputer(strategy='mean')df['TotalCharges'] = imputer.fit_transform(df[['TotalCharges']])# Convert categorical variablescategorical_features = ['gender', 'Partner', 'Dependents', 'InternetService', 'Contract', 'PaymentMethod']encoder = ColumnTransformer(transformers=[('cat', OneHotEncoder(), categorical_features)], remainder='passthrough')df_encoded = encoder.fit_transform(df)# Normalize numerical featuresscaler = StandardScaler()df_encoded[['MonthlyCharges', 'tenure']] = scaler.fit_transform(df_encoded[['MonthlyCharges', 'tenure']])
Performing EDA is crucial to understand the dataset and identify significant features.
Sample EDA visualization code:
import seaborn as snsimport matplotlib.pyplot as plt# Correlation heatmapplt.figure(figsize=(12, 8))sns.heatmap(df.corr(), annot=True, cmap='coolwarm')plt.title('Correlation Heatmap')plt.show()# Distribution plot for MonthlyChargessns.histplot(df['MonthlyCharges'], kde=True)plt.title('Distribution of Monthly Charges')plt.show()
Now, we will build and compare at least two different machine learning models:
from sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LogisticRegressionfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report# Split data into training and testing setsX = df_encoded.drop('Churn', axis=1)y = df_encoded['Churn']X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Logistic Regressionlr = LogisticRegression()lr.fit(X_train, y_train)y_pred_lr = lr.predict(X_test)# Random Forest Classifierrf = RandomForestClassifier()rf.fit(X_train, y_train)y_pred_rf = rf.predict(X_test)
Evaluate the models using appropriate metrics such as accuracy, precision, recall, and F1-score.
# Evaluation Metrics for Logistic Regressionprint('Logistic Regression Classification Report:')print(classification_report(y_test, y_pred_lr))# Evaluation Metrics for Random Forestprint('Random Forest Classification Report:')print(classification_report(y_test, y_pred_rf))
Based on the evaluation metrics, the model with the better performance will be selected. In this case, Random Forest might outperform Logistic Regression due to its ability to handle complex interactions between features.
Our detailed report would include:
Make sure to include visualizations such as correlation heatmaps, distribution plots, and confusion matrices to support your analysis and findings.
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