Ai powered Financial analysis and risk prediction

Project: Financial Analysis with Classical ML Models

Goal: Use machine learning models (regression, classification, clustering, ARL) to analyze financial data for insights and predictions.

1. Data Collection & Preprocessing

Data Sources: Kaggle, Yahoo Finance, UCI ML Repository

Common Data Types: Stock prices, credit transactions, customer spending, financial statements

Preprocessing:

Handle missing values (mean imputation, forward fill for time series)

Normalize/scale numerical data

Encode categorical variables (One-Hot Encoding, Label Encoding

2. ML Models for Financial Analysis

(A) Stock Price Prediction (Regression)

Models: Linear Regression, Ridge/Lasso Regression, XGBoost

Features: Previous closing prices, moving averages, volume

Output: Predict future stock price

(B) Credit Risk Analysis (Classification)

Models: Logistic Regression, Decision Trees, Random Forest

Features: Credit score, income, previous defaults

Output: Classify loan applicants as low-risk or high-risk

(C) Customer Segmentation (Clustering)

Models: K-Means, Hierarchical Clustering, DBSCAN

Features: Customer spending habits, transaction frequency

Output: Group customers into spending categories (e.g., high spenders, occasional buyers)

(D) Fraud Detection (Anomaly Detection)

Models: Isolation Forest, One-Class SVM

Features: Transaction amount, location, time

Output: Detect fraudulent transactions

(E) Market Basket Analysis (Association Rule Learning - ARL)

Models: Apriori, FP-Growth

Features: Customer transaction data

Output: Find relationships between financial products (e.g., people who invest in stocks also buy mutual funds

3. Model Integration & Deployment

Backend: Flask / FastAPI

Frontend: Streamlit / Dash

Database: PostgreSQL / SQLite

Visualization: Matplotlib, Seaborn.