Breast Cancer Diagnosis Prediction Using LIME Model

Explainable-AI/XAI on Cancer Dataset/Readme.md

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+# Breast Cancer Diagnosis Prediction Using LIME Model
+
+## Goal
+The main objective of this project is to predict whether a tumor is benign or malignant using machine learning models and explain the model’s decisions using LIME (Local Interpretable Model-Agnostic Explanations). The dataset used in this project is the **Breast Cancer Dataset** available from Kaggle.
+
+## Dataset
+- **Source:** Kaggle
+- **Dataset URL:** [Breast Cancer Dataset](https://www.kaggle.com/datasets/yasserh/breast-cancer-dataset)
+- The dataset contains features related to the physical characteristics of the tumor and the target variable is **diagnosis**, where:
+  - 1 represents Malignant
+  - 0 represents Benign
+
+## Description
+The dataset consists of 30 features, each representing a different attribute of the tumor, such as the radius, texture, perimeter, area, and smoothness. These features are used to predict whether a tumor is malignant or benign. In addition to building a predictive model, we employ **LIME** (Local Interpretable Model-agnostic Explanations) to explain individual predictions, enhancing the interpretability of the model.
+
+## What I Have Done
+### 1. Configured Kaggle API
+- Set up the Kaggle API for dataset download using the following code:
+
+### 2. Performed Exploratory Data Analysis (EDA)
+- Loaded the dataset into a pandas dataframe and analyzed its structure.
+- Checked for missing values, data types, and duplicated rows.
+- Visualized the distribution of diagnosis values.
+
+### 3. Data Preprocessing
+- Converted categorical values (`diagnosis`) to numerical values, where 1 indicates Malignant and 0 indicates Benign.
+- Applied one-hot encoding for categorical columns.
+- Split the data into training and testing sets using an 80-20 ratio.
+
+### 4. Built and Trained a Decision Tree Model
+- Implemented a **Decision Tree Classifier** for the initial classification task.
+- Trained the model using the training set (`X_train`, `y_train`) and evaluated its accuracy on the test set (`X_test`, `y_test`).
+
+### 5. Applied the LIME Model
+- Applied **LIME** to explain individual predictions of the decision tree model.
+- Generated explanations for test instances, which highlight how each feature contributes to the prediction of whether a tumor is benign or malignant.
+
+### 6. Evaluated the Model
+- Evaluated the model’s performance using accuracy, confusion matrix, and classification report metrics.
+- Used LIME to interpret and explain the model’s predictions for individual cases.
+
+## Models Used
+- **Decision Tree Classifier:** A tree-based model used for classification of tumors based on their features.
+- **LIME (Local Interpretable Model-Agnostic Explanations):** Used to explain predictions of the machine learning model by generating interpretable explanations for individual instances.
+
+## Libraries Needed
+- **Pandas:** For data manipulation.
+- **Numpy:** For numerical computations.
+- **Scikit-learn:** For building and evaluating the machine learning model.
+- **LIME:** For generating explanations for the machine learning model’s predictions.
+- **Matplotlib & Seaborn:** For data visualization.
+
+## Insights
+This project provides insights into:
+- How to build a decision tree model to predict whether a tumor is benign or malignant.
+- The application of **LIME** to explain individual predictions and understand the decision-making process of the model.
+- Visualizing and interpreting the effect of different features on the predictions.