Updated readme

README.md

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-## Predicting Telco Customer Churn using IBM dataset
-The file data.csv is the dataset got from Kaggle <link>[telco-customer-churn](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)
+# Predicting Telco Customer Churn using IBM dataset
+
+This project applies machine learning techniques to predict customer churn using a dataset containing customer behavior and subscription details. The aim is to identify customers likely to leave a service and gain insights through model interpretability using SHAP values.
+
+## 📊 Project Overview
+
+The notebook performs the following tasks:
+
+- **Data Preprocessing**
+  - Categorical encoding using LabelEncoder.
+  - Feature scaling using StandardScaler.
+  - Dropping irrelevant or low-impact features.
+  
+- **Exploratory Data Analysis (EDA)**
+  - Correlation analysis.
+  - KDE plots for feature distribution.
+  - Heatmap for multivariate correlation.
+
+- **Model Building**
+  - **Random Forest Classifier**
+  - **Logistic Regression**
+
+- **Model Evaluation**
+  - Classification Report
+  - Confusion Matrix
+  - Accuracy, Brier Score Loss, ROC AUC Score
+  - SHAP analysis for model interpretability
+
+## 🧰 Technologies & Libraries
+
+- Python
+- Pandas
+- Seaborn
+- Matplotlib
+- Scikit-learn
+- SHAP
+
+> **Note:** The file data.csv is the dataset got from Kaggle [telco-customer-churn](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)