📈 Tesla Stock Prediction

📌 Project Overview

This project analyzes Tesla stock data and builds machine learning models to predict and classify stock movements.
The analysis includes EDA, feature correlation, moving averages, and two models:

• Linear Regression → Predict Closing Price (regression task)
• Logistic Regression → Predict whether next day's price goes up (classification task)

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📊 Exploratory Data Analysis

• 1 Trend analysis of Tesla stock Closing prices over time

• 2 Trend analysis of Tesla stock Closing prices over the years

• 3 50-day vs 200-day moving averages

• 4 Correlation heatmap of stock features

• 5 Volume vs Closing Price scatter plot

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🤖 Models Used

🔹 Linear Regression

• Predicts the Closing Price of Tesla stock.
• Evaluation metric: R² score, RMSE, MAE.
• Visualization: Actual vs Predicted Closing Prices.

🔹 Logistic Regression

• Predicts whether the price will go up or down next day (Price_Up = 1 or 0).
• Evaluation metrics: Accuracy, Precision, Recall, F1-score.
• Visualization: Confusion Matrix heatmap.

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📊 Model Comparison

1️⃣ Linear Regression – Actual vs Predicted Closing Price

2️⃣ Logistic Regression – Confusion Matrix

3️⃣ Side-by-Side Metric Comparison

• Linear Regression (R²) → measures how well regression predictions fit actual prices
• Logistic Regression (Accuracy) → measures classification correctness

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🛠️ Tech Stack

• Python, Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn

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🚀 How to Run

git clone https://github.com/YOUR-USERNAME/Tesla-Stock-Prediction.git
cd Tesla-Stock-Prediction
pip install -r requirements.txt
jupyter notebook Tesla_Stock_Prediction.ipynb