📃:Implementing Machine Learning Models for Stock Market Prediction

[BenakDeepak]

🔴 Title:
Implementing Machine Learning Models for Stock Market Prediction

🔴 Aim:
To build and compare various machine learning models to predict stock prices and trends based on historical stock market data, evaluating the performance of each model using key metrics like accuracy, mean absolute error (MAE), and root mean squared error (RMSE).

🔴 Brief Explanation:
The goal of this project is to implement and compare multiple machine learning models for predicting stock prices and identifying trends. The stock market dataset will be used to forecast continuous variables, such as stock prices (regression), and to classify conditions, such as whether the price will increase or decrease (classification).

Models to be Evaluated:
Classification Models:

Logistic Regression
Random Forest Classifier
Support Vector Machine (SVM)
k-Nearest Neighbors (k-NN)
Neural Networks
Gradient Boosting Classifier
Regression Models:

Linear Regression
Decision Trees
Random Forest Regression
Support Vector Regression (SVR)
Gradient Boosting Regression
Evaluation Metrics:
For classification tasks: Accuracy, Precision, and F1 Score
For regression tasks: Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE)

Feature Scaling: Normalize or standardize features to enhance model performance.
Screenshots 📷
N/A

✅ To be Mentioned while taking the issue:
Full name: Benak Deepak
What is your participant role? (Mention the Open Source Program name. Eg. GSOC, GSSOC, SSOC, JWOC, etc.): GSSOC
Happy Contributing 🚀
All the best. Enjoy your open-source journey ahead. 😎

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[github-actions]

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