Project 11 | Bank Subscription To Term Predict Customer has taken subscription or not

Welcome to Project 11, where we dive into predicting whether a customer will subscribe to a term deposit (fixed deposit) or not. In this video, we'll guide you through the entire process, from understanding the problem to deploying a machine learning model.
🔍 What You'll Learn:
Introduction to the Problem: Understanding the significance of predicting term deposit subscriptions in the banking sector.
Data Exploration and Preprocessing: Analyzing the dataset, handling missing values, and feature engineering.
Model Selection and Training: Choosing the right algorithms, training models, and comparing their performance.
Evaluation Metrics: Assessing model accuracy using various metrics like precision, recall, and F1-score.
Hyperparameter Tuning: Optimizing the model for better performance.
Model Deployment: Deploying the final model for real-world use.
🔧 Tools and Technologies Used:
Python
Pandas and NumPy for data manipulation
Matplotlib and Seaborn for data visualization
Scikit-learn for machine learning algorithms
Jupyter Notebook for interactive coding
📊 Dataset:
We'll be using a publicly available dataset containing various features related to the customer’s profile and past interactions with the bank.
👨‍🏫 Who Is This For?
This video is perfect for data science enthusiasts, machine learning practitioners, and anyone interested in predictive analytics in the banking industry.
📢 Call to Action:
If you find this video helpful, make sure to like, subscribe, and hit the notification bell for more data science projects and tutorials.
Let's dive in and start predicting!
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[@SouravKumar-rr4de]

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[@datascience8012]

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