This repository hosts a stock market prediction model for Tesla and Apple using Liquid Neural Networks. It showcases data-driven forecasting techniques, feature engineering, and machine learning to enhance the accuracy of financial predictions.

This project showcases a comprehensive exploratory data analysis (EDA) of Tesla stock prices using various analytical tools, including Python, R, Power BI, and Microsoft Excel. The project involves examining historical Tesla stock data, performing EDA, and predicting stock prices for January 2024.

The Tesla Stock Data Extraction and Analysis project involves gathering historical stock data and performing detailed analysis to identify trends, patterns, and predictions. It utilizes data visualization and statistical methods to derive insights for investment decisions.

Predicting Tesla stock prices using Time Series .This projects explores the capabilities and limitations of ARIMA model in stock analysis and prediction.

Python script analyzes Tesla stock data, displaying candlestick chart, 3D scatter plot, & line chart (closing price, MA50, MA200). Generates buy/sell signals based on moving average crossovers.

This project analyzes Tesla’s historical trading data (Jan 2021 – Jan 2026) to uncover market trends, short-term spikes, volatility, and medium-term momentum. The goal is to provide data-driven, executive-focused insights into performance, risk, and trading patterns.