Home
Refer
Jobs
Alumni
Resume
Notifications

Develop a machine learning model using Python to predict customer churn for a telecommunications company. Use the provided dataset, which includes customer demographics, account information, and service usage patterns. Your task is to: 1. Clean and preprocess the data. 2. Perform exploratory data analysis to identify significant features. 3. Build and compare at least two different machine learning models. 4. Evaluate the models using appropriate metrics and choose the best-performing model. 5. Write a detailed report explaining your methodology, findings, and the rationale behind the model selection. Include visualizations to support your analysis.

🚀 Best Answers Get Featured in our LinkedIn Community based on Your Consent, To Increase Your Chances of Getting Interviewed. 🚀


```html

Building a Machine Learning Model to Predict Customer Churn

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:

Data Cleaning and Preprocessing

First, we need to load the dataset and perform data cleaning and preprocessing tasks:

  • Handle missing values by either imputing them or dropping rows/columns as necessary.
  • Convert categorical variables into numerical format using techniques like one-hot encoding.
  • Normalize or standardize the numerical features to ensure all features are on the same scale.
# 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']])

Exploratory Data Analysis (EDA)

Performing EDA is crucial to understand the dataset and identify significant features.

  • Use correlation heatmaps to identify correlations between features and the target variable.
  • Generate summary statistics and visualize distributions of features using histograms and boxplots.
  • Analyze churn rates across different categories using bar plots.

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()

Building and Comparing Machine Learning Models

Now, we will build and compare at least two different machine learning models:

  • Logistic Regression
  • Random Forest Classifier
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)

Model Evaluation

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.

Report

Our detailed report would include:

  • An overview of data cleaning and preprocessing steps.
  • Insights from exploratory data analysis including visualizations.
  • Model building and comparisons showing the performance metrics for each model.
  • Rationale behind selecting the best-performing model.

Visualizations

Make sure to include visualizations such as correlation heatmaps, distribution plots, and confusion matrices to support your analysis and findings.

References


```

© 2024 Referral Solutions, Inc. Incorporated. All rights reserved.