Error in user YAML: (<unknown>): could not find expected ':' while scanning a simple key at line 4 column 1

---

# **Comprehensive Machine Learning Prediction Web Application** 📊

This project is a versatile **Streamlit-based web application** that offers four distinct types of predictions:
1. **Drug Recommendation Prediction** 💊
2. **Spam Email Detection** ✉️
3. **Stock Price Prediction** 📈
4. **Health Disease Prediction** (Cancer, CHD Heart Disease, Diabetes, Stroke) 🏥

The app leverages various **machine learning algorithms** for each task, allowing users to train models, visualize performance metrics, and make real-time predictions.

---

Features ✨

1. Drug Recommendation Prediction 💊

• Goal: Predict which drug is suitable for a patient based on medical features like age, sex, blood pressure, cholesterol, etc.
• Machine Learning Models:
  • Logistic Regression
  • K-Nearest Neighbors
  • Support Vector Machine (SVM)
  • Naive Bayes (GaussianNB)
• Key Features:
  • Input patient data and predict the recommended drug.
  • Displays classification report, confusion matrix, and accuracy for each model.
  • Allows users to compare models with different accuracy scores.

2. Spam Email Detection ✉️

• Goal: Detect whether an email is spam or not based on its content.
• Machine Learning Models:
  • MultinomialNB
  • GaussianNB
  • BernoulliNB
  • ComplementNB
• Key Features:
  • Input custom email content to check if it's classified as spam or ham.
  • Visualization of word clouds for spam and non-spam emails.
  • Displays the classification report, confusion matrix, and prediction accuracy.
  • Model saving functionality for future use.

3. Stock Price Prediction 📈

• Goal: Predict the future stock prices of companies based on their historical data.
• Machine Learning Model:
  • Linear Regression
• Key Features:
  • Select from popular stocks (AAPL, GOOG, MSFT, AMZN) or enter a custom stock ticker.
  • Fetches historical stock price data from Yahoo Finance.
  • Predicts future stock prices based on the last 10 years of data using linear regression.
  • Displays stock summary, stock price charts, model metrics (R², MAE, MSE), and prediction charts.
  • Users can input future days to predict stock prices.

4. Health Disease Prediction 🏥

• Goal: Predict the risk of diseases like Cancer, CHD Heart Disease, Diabetes, and Stroke.
• Machine Learning Models:
  • Naive Bayes (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB)
  • Support Vector Machine (SVM)
  • Logistic Regression
• Key Features:
  • Input custom health data to predict disease risk.
  • Handles categorical data encoding and missing value imputation.
  • Applies SMOTE for imbalanced datasets to improve predictions.
  • Displays classification report, confusion matrix, accuracy, and correlation matrix for feature relationships.
  • Users can test models with their own feature values for real-time disease prediction.

---

Installation Instructions 🚀

1. Clone the repository:

   git clone https://github.com/your-repo/machine-learning-app.git
   cd machine-learning-app

2. Install required dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

---

How to Use the Application 🛠️

1. Select the Task:

   • Choose from Drug Prediction, Spam Detection, Stock Price Prediction, or Health Disease Prediction from the sidebar.

2. Upload Dataset or Use Default:

   • For health and spam tasks, upload your own dataset or use the default one provided.

3. Model Selection:

   • Choose the machine learning model you want to train and evaluate.

4. Make Predictions:

   • Input custom data (patient details, email content, stock ticker, etc.) and let the app predict outcomes like drug recommendation, spam classification, stock prices, or disease risk.

5. Visualizations:

   • View detailed classification reports, confusion matrices, word clouds, and stock price trends directly in the app.

---

Key Libraries and Tools 🛠️

• Streamlit: For building interactive web applications.
• Scikit-learn: For machine learning models.
• Seaborn & Matplotlib: For data visualizations.
• Yahoo Finance API: For fetching stock data.
• WordCloud: For visualizing word distributions in spam detection.
• SMOTE: For oversampling to handle class imbalances.

---

Conclusion 🎯

This all-in-one prediction app is designed to provide a hands-on experience with real-world datasets and machine learning models. It covers a wide range of use cases, from financial predictions to health diagnostics and spam detection, offering insights through interactive model training and real-time predictions.