Financial Data Anomaly Detection and Forecasting Project Overview This project involves the development of a machine learning model to analyze financial data, detect anomalies, and forecast future trends. Using a variety of models from scikit-learn, such as RandomForestRegressor, KMeans, and IsolationForest, the model is designed to predict financial performance and detect outliers or anomalous data points. The project focuses on leveraging Python, data science, and machine learning to build a robust system for financial data analysis.

Key Features Predictive Modeling: Utilizes Random Forest Regressor for forecasting future financial trends based on historical data. Anomaly Detection: Applies Isolation Forest to identify outliers and anomalies in financial datasets, helping in risk assessment. Clustering: Implements KMeans clustering to group financial data, making it easier to understand patterns and trends in large datasets. Data Preprocessing: Includes steps for handling missing values, normalizing data, and feature engineering to improve model accuracy. Technologies Used Programming Language: Python Libraries/Tools: scikit-learn (RandomForestRegressor, IsolationForest, KMeans, train_test_split) Pandas (for data manipulation) Matplotlib and Seaborn (for data visualization) Data Sources The project uses historical financial data (specific data sources can be included if applicable, or you can mention if it's synthetic data generated for the project). Installation To run the project locally, clone the repository and install the required dependencies:

bash Copy code git clone https://github.com/yourusername/financial-anomaly-detection.git cd financial-anomaly-detection pip install -r requirements.txt How It Works Data Loading and Preprocessing:

Load financial data from CSV or any other data format. Handle missing values and normalize features for model training. Model Training:

Split data into training and testing sets using train_test_split. Train the Random Forest Regressor model to predict future financial trends. Use KMeans clustering to identify and group similar data points. Anomaly Detection:

Train the Isolation Forest model to detect anomalies and outliers in financial data. Model Evaluation:

Evaluate the performance of the Random Forest model using appropriate metrics (e.g., accuracy, RMSE). Visualize the clustering results and anomalies detected. Example Usage Provide a simple script or example of how to use the project:

python Copy code import pandas as pd from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split

Load data

data = pd.read_csv('financial_data.csv')

Preprocessing steps...

X = data.drop('target_column', axis=1) y = data['target_column']

Train-test split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Train model

model = RandomForestRegressor() model.fit(X_train, y_train)

Predict and evaluate...

Results Provide details on the model performance, such as accuracy, R² score, or any other evaluation metric used. You can also include a visualization showing the clustering results or the anomalies detected in the dataset. Future Enhancements Extend the project to use more advanced deep learning models (e.g., neural networks for time series forecasting). Implement real-time financial data monitoring and anomaly detection. Integrate with an external API to pull live financial data.