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+# NeoLung: Lung cancer prediction using machine learning
+
+## Aim:
+
+The purpose of this project is to comapare Classification algorithms implemented on Lung Cancer Dataset
+
+## Dataset:
+
+The Lung cancer dataset used in the project has been collected from data.world whose link is:
+
+https://data.world/sta427ceyin/survey-lung-cancer
+
+## Working:
+
+We have selected **10 of the following classification algorithms** that have been used in this project:
+1. Logistic Regression
+2. K-Nearest Neighbors (KNN)
+3. Decision Tree
+4. Support Vector Machines (SVM)
+5. Naive Bayes
+6. Random Forest
+7. Gradient Boosting
+8. Neural Networks
+9. AdaBoost
+10. XGBoost
+
+Then we build the model for each of the above mentioned algorithms. Using the following **Evaluation Metrics** we have compared the algorithms:
+1. Accuracy
+2. Precision
+3. F1 Score
+4. Recall Score
+5. Confusion Matrix
+
+These are the accuracies of the algorithms:
+1. Logistic Regression: **90.29%**
+2. K-Nearest Neighbors (KNN): **87.37%**
+3. Decision Tree: **87.37%**
+4. Support Vector Machines (SVM): **84.46%**
+5. Naive Bayes: **86.4%**
+6. Random Forest: **89.32%**
+7. Gradient Boosting: **89.32%**
+8. Neural Networks: **84.46%**
+9. AdaBoost: **84.46%**
+10. XGBoost: **84.46%**
+
+## Results:
+
+Out of all the algorithms so implemented, **Logistic Regression** performed the best. The evaluation metrics for Logistic Regression is as follows:
+
+**Accuracy: 0.9029126213592233**
+
+**Precision: 0.9052631578947369**
+
+**Recall: 0.9885057471264368**
+
+**F1 score: 0.945054945054945**
+
+**Confusion Matrix:**
+
+![download](https://github.com/rohitinu6/Lung_Cancer_Prediction_Using_Machine_Learning/assets/113301503/b1e82b1c-2487-486a-b476-d34786148d40)