You will get Churn Prediction Using Advanced Machine Learning Techniques
Project details
Project description. This project focuses on predicting customer churn using various machine learning classifiers, including Logistic Regression, Decision Trees, Random Forest, SVM, XGBoost, CatBoost, and LightGBM. The goal is to evaluate each model’s performance based on accuracy, precision, recall, and F1 score to identify the most effective method for predicting customer retention. The project utilizes Python libraries such as scikit-learn, pandas, and visualization tools like Seaborn and Matplotlib to derive insights and visualize model performance.
Machine Learning Tools
MATLAB, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$20
|
Standard
$25
|
Advanced
$40
|
|---|---|---|---|
| Delivery Time | 1 day | 4 days | 4 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
Number of Model Variations | 1 | 1 | 1 |
Number of Scenarios | 1 | 2 | 2 |
Number of Graphs/Charts | 0 | 0 | 1 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Model Variation
(+ 1 Day)
+$3About Mina
AI and Machine Learning Engineer
Maghaghah, Egypt - 1:02 am local time
Full project management from start to finish
Regular communication is important to me, so let’s keep in touch.
Steps for completing your project
After purchasing the project, send requirements so Mina can start the project.
Delivery time starts when Mina receives requirements from you.
Mina works on your project following the steps below.
Revisions may occur after the delivery date.
Churn Prediction Using Advanced Machine Learning Techniques
Project description. This project focuses on predicting customer churn using various machine learning classifiers, including Logistic Regression, Decision Trees, Random Forest, SVM, XGBoost, CatBoost, and LightGBM.Predic the Client Is Continue Or Not