Merge pull request #801 from Varunshiyam/Fixes-#800

Fixes #800 Deposit Prediction in Banks

Prediction Models/Deposit_Prediction/Readme.md

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+# Bank Deposit Prediction Using Machine Learning
+
+## Project Overview
+
+This project utilizes machine learning models to predict the likelihood of a bank client subscribing to a term deposit after a marketing campaign. By analyzing various client features such as age, job, balance, and previous campaign interactions, the project aims to improve targeting and conversion rates for future campaigns.
+
+## Problem Statement
+
+Predicting customer deposit subscription enables banks to optimize marketing strategies and direct efforts towards high-potential clients, thus enhancing both efficiency and campaign effectiveness. This project addresses the challenge of identifying clients who are more likely to subscribe to a term deposit.
+
+## Data
+
+The dataset contains client information including:
+
+- **Demographics:** Age, job, marital status, and education level.
+- **Financial Information:** Account balance and loan details.
+- **Campaign Interaction:** Contact type, number of contacts, days since previous contact, etc.
+
+## Approach
+
+1. **Exploratory Data Analysis (EDA):** Analyze and visualize the dataset to understand distributions, relationships, and feature relevance.
+2. **Data Preprocessing:** Handle missing values, encode categorical variables, and scale features as needed.
+3. **Model Building:** Implement multiple machine learning models, including:
+   - Logistic Regression
+   - Decision Tree
+   - Random Forest
+   - Support Vector Machine (SVM)
+   - Gradient Boosting
+4. **Evaluation:** Assess models based on accuracy, precision, recall, and F1-score to determine the most effective model for predicting term deposit subscription.
+
+## Requirements
+
+- Python 3.8+
+- pandas
+- numpy
+- scikit-learn
+- matplotlib
+- seaborn