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https://github.com/chandkund/stock-price-prediction-
Predict Tesla stock prices using historical data. The project preprocesses data, applies normalization and standardization, trains a linear regression model, and evaluates its performance. Visualizations include box plots and time series plots.
https://github.com/chandkund/stock-price-prediction-
data-science linear-regression numpy pandas plotly python sklearn sklearn-model
Last synced: 15 days ago
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Predict Tesla stock prices using historical data. The project preprocesses data, applies normalization and standardization, trains a linear regression model, and evaluates its performance. Visualizations include box plots and time series plots.
- Host: GitHub
- URL: https://github.com/chandkund/stock-price-prediction-
- Owner: chandkund
- License: mit
- Created: 2024-08-15T09:13:03.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-16T19:11:50.000Z (3 months ago)
- Last Synced: 2024-11-03T04:02:51.797Z (15 days ago)
- Topics: data-science, linear-regression, numpy, pandas, plotly, python, sklearn, sklearn-model
- Language: Jupyter Notebook
- Homepage:
- Size: 221 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Stock Price Prediction
This project aims to predict stock prices using historical data. We use a dataset containing Tesla's stock prices and various machine learning techniques to build and evaluate a predictive model.
## Table of Contents
- [Project Overview](#project-overview)
- [Installation](#installation)
- [Usage](#usage)
- [Code Explanation](#code-explanation)
- [Model Evaluation](#model-evaluation)
- [License](#license)
## Project Overview
The dataset used in this project is a CSV file containing Tesla's historical stock prices. The data includes:
- **Date**: The date of the stock price.
- **Open**: The opening price of the stock.
- **High**: The highest price of the stock during the day.
- **Low**: The lowest price of the stock during the day.
- **Close**: The closing price of the stock.
- **Adj Close**: The adjusted closing price, accounting for dividends and stock splits.
- **Volume**: The number of shares traded on that day.
The goal of this project is to predict Tesla's future stock prices based on this historical data. The dataset is used to train and evaluate a machine learning model that forecasts stock prices.
## Installation
To run this project, you'll need to have Python installed along with the following packages:
1. **Clone the Repository:**
```bash
git clone https://github.com/chandkund/Stock-Price-Prediction-
cd Stock-Price-Prediction-
```
2. **Install Dependencies:**
Make sure you have Python installed. Then install the required packages:
```bash
pip install pandas numpy matplotlib seaborn scikit-learn plotly
```
Or install them individually:
```bash
pip install pandas
pip install numpy
pip install matplotlib
pip install seaborn
pip install scikit-learn
pip install plotly
```
- `pandas`
- `numpy`
- `matplotlib`
- `seaborn`
- `scikit-learn`
- `plotly`
## Usage
- Clone the Repository:
git clone https://github.com/chandkund/stock-price-prediction-.git
cd stock-price-prediction-
- Prepare the Dataset:
Place your dataset (e.g., tesla.csv) in the project directory or adjust the file path in the code.
- Run the Code:
You can run the Jupyter notebook or Python script to execute the code. Make sure to update the path to
the dataset if necessary.
python script.py
## Code Explanation
- **Import Relevant Libraries**:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.linear_model import LinearRegression
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, plot, iplot
```
- **Load and Preprocess Data**:
```python
raw_data = pd.read_csv("D:\\Project\\Project_3\\tesla.csv")
df = raw_data.copy()
df['Date'] = pd.to_datetime(df["Date"])
```
- **Visualize the Data**:
```python
fig, ax = plt.subplots(figsize=(12, 6))
sns.boxplot(df[['Open', 'High', 'Low', 'Close', 'Adj Close']])
plt.show()
fig, ax = plt.subplots(figsize=(12, 6))
plt.plot(df["Date"], df['Adj Close'], marker="|", linestyle="-", color='dodgerblue', linewidth=2,
markersize=8)
ax.set_title('Adjusted Closing Prices Over Time', fontsize=16, fontweight='bold')
ax.set_xlabel("Date")
ax.set_ylabel("Price")
plt.xticks(rotation=45)
ax.grid(True, linestyle="--", linewidth=0.7)
plt.tight_layout()
```
- **Create a Plotly Visualization**:
```python
init_notebook_mode(connected=True)
layout = go.Layout(
title="Stock Prices of Tesla",
xaxis=dict(
title="Date",
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title="Price",
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
tesla_data = go.Scatter(x=df['Date'], y=df['Close'], mode='lines', name='Close Price')
plot = go.Figure(data=[tesla_data], layout=layout)
iplot(plot)
```
- **Normalization and Standardization**:
```python
from sklearn.preprocessing import MinMaxScaler, StandardScaler
cols = ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']
# Min-Max Normalization
scaled = MinMaxScaler()
df[cols] = pd.DataFrame(scaled.fit_transform(df[cols]), columns=cols)
# Standardization
scaled = StandardScaler()
df[cols] = pd.DataFrame(scaled.fit_transform(df[cols]), columns=cols)
```
- **Split Data and Train Model**:
```python
X = df[['Open', 'High', 'Low', 'Volume']]
Y = df['Close']
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
```
- **Model**:
```python
model = LinearRegression()
model.fit(X_train, Y_train)
```
## Model Evaluation
After training the model, evaluate its performance using Mean Squared Error (MSE):
```python
pred = model.predict(X_test)
mse = mean_squared_error(Y_test, pred)
print(f"Mean Squared Error: {mse}")
```
## License
This project is licensed under the MIT License. See the LICENSE file for details.
Make sure to adjust any file paths and repository URLs as needed. This `README.md` provides a structured overview of the project, including installation instructions, code explanation, and model evaluation.
This project is licensed under the MIT License. See the [LICENSE](LICENSE)