Welcome to DataScienceForAll Repository! This repository contains a collection of data science projects, showcasing out skills and expertise in the field. Each project demonstrates different aspects of data analysis, machine learning, and visualization.

1. [Breast Cancer Prediction]
   • Description: The project predicts the diagnosis (M = malignant, B = benign) of the Breast Cancer
   • Technologies Used: The notebooks uses Decision Tree Classification and Logistic Regression
   • Results: The logistic regression gave 97% accuracy and decision tree gave 93.5% accuracy
2. [Red Wine Quality Prediction](
   • Description: The project predicts the quality of the wine in the value 0 or 1. 1 for good quality and 0 for bad quality
   • Technologies Used: The notebooks uses logistic regression, support vector machine, decision tree and knn
   • Results: The logistic regression model performs the best with accuracy of 86.67%
3. [Heart Stroke Prediction]
   • Description: The project predicts the risk of heart stroke on studying the person's demographics and medical info
   • Technologies Used: The notebooks uses logistic regression, support vector machine, decision tree and knn
   • Results: The logistic regression, SVM and KNN performs the best with 93.8 % accuracy
4. [House Price Prediction]
   • Description: The project predicts the house price after studying the variables such as location, area, bredroom, bathroom count and many more.
   • Technologies Used: The notebooks uses Linear Regression, Ridge Regression and Random Forest Regressor
   • Results: The Random Forest Regressor performed best with accuracy of 87.89%
5. [Titanic Survival Prediction]
   • Description: The project predicts the survival during the titanic disaster based on socio-economic measures
   • Technologies Used: The notebooks uses Descision Tree Classifier
   • Results: The Decision Tree Classifer performed well on the test dataset with an accuracy of 89.5%
6. [Diamond Price Prediction]
   • Description: The project predicts the price (in US dollars) of the diamonds based on their features
   • Technologies Used: The notebooks uses Descision Tree Regressor and Random Forest Regressor
   • Results: The Decision Tree Regresor performed well on the test dataset with an accuracy of 96%
7. [Medical Cost Prediction]
   • Description: The project predicts the medical treatment cost by analysing the patients age, gender, bmi, smoking habits etc.
   • Technologies Used: The notebooks uses Linear and Polynomial Regression, Decision Tree and Random Forest Regressor
   • Results: The Decision Tree Regressor and Random Forest Regressor performed well
8. [Room Occupancy Detection]
   • Description: The project predicts the room occupancy by analyzing the sensor data such as temperature, light and co2 level.
   • Technologies Used: The notebooks uses Random Forest Classifier
   • Results: The Random Forest Classifier performed well with an accuracy of 98%
9. [Sleep Disorder Prediction]
   • Description: The project aims to predict sleep disorders and their types by analyzing lifestyle and medical variables, such as age, BMI, sleep duration, blood pressure, and more
   • Technologies Used: The notebooks uses Random Forest Classifier and Decision Tree cLassifier
   • Results: The Random Forest Classifier performed well with an accuracy of 89%
10. [Pima Indians Diabetes Prediction]
    • Description: The primary objective of the Pima Indian Diabetes Prediction project is to analyze various medical factors of female patients, to predict whether they have diabetes or not.
    • Technologies Used: The notebooks uses Logistic Regression, Random Forest Classifier and Support Vector Machine
    • Results: The Logistic Regression performed with an accuracy of 78%.
11. [Bank Customer Churn Prediction]
    • Description: The main objective of the Bank Customer Churn Prediction project is to analyze the demographics in order to predict whether a customer will leave the bank or not.
    • Technologies Used: The notebooks uses Random Forest Classifier and Decision Tree Classifier
    • Results: The Random Forest Classifier and Decision Tree Classifier performed equally well with an accuracy of 87%

This project is licensed under the [MIT License]. You are free to use the code and resources for educational or personal purposes.

Contributions are welcome! If you would like to contribute to this repository, please follow the guidelines outlined in [CONTRIBUTING.md]. Any improvements, bug fixes, or additional projects are greatly appreciated.

We welcome any feedback, suggestions, or questions you may have about the projects or the repository. Feel free to reach out to me via email at [email protected]

Enjoy exploring data science projects!