Welcome to my GitHub repository showcasing the projects I completed during the Let's Grow More - Virtual Internship Program in Data Analytics. These projects highlight my skills and knowledge in various data analytics techniques and algorithms.

In this project, I implemented the decision tree algorithm to predict outcomes based on input features. The goal was to create a classification model that can accurately classify data instances using a hierarchical tree-like structure. I utilized the scikit-learn library to build the decision tree model and evaluated its performance using appropriate metrics.

In this project, I focused on forecasting and predicting stock prices using a Stacked Long Short-Term Memory (LSTM) neural network. The objective was to develop a model that can effectively analyze historical stock price data and provide predictions for future price movements. I implemented the Stacked LSTM model using the Keras library and evaluated its performance in predicting stock prices.

• Project 1: Prediction using Decision Tree/: Contains the code, dataset, and documentation for the Prediction using Decision Tree Algorithm project.
• Project 2: Stock Market Prediction and Forecasting/: Contains the code, dataset, and documentation for the Stock Price Forecasting and Prediction using Stacked LSTM project.

Feel free to explore each project folder for more details, including the code implementations, data analysis, and project documentation.

To run the code and reproduce the results, you will need Python 3.x and the following libraries:

• NumPy
• Pandas
• scikit-learn
• Keras

You can install the required libraries using pip: pip install numpy pandas scikit-learn keras

I would like to express my gratitude to Let's Grow More for providing me with the opportunity to participate in their Virtual Internship Program in Data Analytics. The program has been instrumental in enhancing my skills and knowledge in the field of data analytics.

Happy analyzing! 📊🚀