📈 Forecasting Tesla Stock Prices Using Time Series Analysis 🚗

Welcome to the Tesla Stock Price Forecasting project, where we delve into time-series analysis to predict stock price trends for one of the world's most innovative companies—Tesla Inc.

🛠️ Tech Stack

Python🐍 Machine-Learning🖥️ Time-Series🎢

📜Table of Contents

• Objective
• Project Flow
• Models Used
• Visuals
• Conclusion
• How to Run

🎯Objective

This project aims to analyze the Tesla stock price dataset by performing detailed Exploratory Data Analysis (EDA) and developing robust forecasting models to predict future stock trends. This can provide valuable insights for investors and stakeholders.

🌟Project Flow

• Data Ingestion: Loading the dataset and checking its integrity.
• Exploratory Data Analysis: Understanding patterns, seasonality, and trends in stock prices.
• Data Transformation: Preprocessing the data for model readiness (e.g., scaling, differencing).
• Model Building: Constructing and training forecasting models.
• Analysis: Evaluating model performance and visualizing predictions.

🔢Models Used

We leverage the following time-series forecasting models: ARIMA: Autoregressive Integrated Moving Average SARIMA: Seasonal Autoregressive Integrated Moving Average

📊Visuals

📌Conclusion

The project demonstrates how time-series models like ARIMA and SARIMA can effectively forecast stock price movements, providing essential insights for decision-making.

🚀How to Run

1. Clone the repository:

https://github.com/itskshitija/Tesla-Stock-Price-Prediction.git

Connect 🤝

• 📩 Email: kshitijachilbule5@gmail.com
• 👩‍💻 Github: https://github.com/itskshitija
• 📶 LinkedIn: https://www.linkedin.com/in/kshitija-chilbule-b98515309/
• 🌐 Portfolio: https://itskshitija.github.io/My-Portfolio/