Oil Price Prediction

 Oil Price Prediction 

1. Introduction

Objective: Predict future oil prices using historical data, feature engineering, and regression models.
Purpose: Provide insights for industries and stakeholders to make informed decisions regarding oil price fluctuations.

2. Project Workflow

1. Problem Definition:
   - Predict oil prices using historical market data and relevant features.
   - Key questions:
     - What features are most correlated with oil prices?
     - How accurately can we predict future oil prices?
2. Data Collection:
   - Source: Financial APIs, Kaggle datasets, or public databases like EIA or FRED.
3. Data Preprocessing:
   - Handle missing values, normalize data, and create additional features.
4. Exploratory Data Analysis:
   - Analyze correlations, trends, and patterns in the data.
5. Modeling:
   - Train regression models for prediction.
6. Evaluation and Insights:
   - Assess model performance and derive actionable insights.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Visualization: Matplotlib, Seaborn, Plotly
  - Machine Learning: scikit-learn, XGBoost
  - APIs: yfinance (for financial data)

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn xgboost yfinance
```

Step 2: Collect and Explore Data

Download historical oil price data:
```
import yfinance as yf

data = yf.download("CL=F", start="2010-01-01", end="2023-01-01")
print(data.head())
```
Inspect the dataset:
```
print(data.info())
print(data.describe())
```

Step 3: Feature Engineering

Create additional features from the data:
```
data['Daily Change'] = data['Close'] - data['Open']
data['High-Low Difference'] = data['High'] - data['Low']
data['Moving Average'] = data['Close'].rolling(window=10).mean()
data.dropna(inplace=True)
```

Step 4: Visualize Data

Plot key features:
```
import matplotlib.pyplot as plt

plt.plot(data['Close'], label='Close Price')
plt.title('Oil Price Over Time')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()
```

Step 5: Prepare Data for Regression

Prepare features and labels for regression:
```
X = data[['Daily Change', 'High-Low Difference', 'Moving Average']]
y = data['Close']

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```

Step 6: Train and Evaluate Models

Train a regression model:
```
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

model = RandomForestRegressor()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f'Mean Squared Error: {mse}')
```

Step 7: Visualize Predictions

Plot actual vs. predicted prices:
```
plt.plot(y_test.values[:50], label='Actual')
plt.plot(predictions[:50], label='Predicted')
plt.title('Oil Price Prediction')
plt.legend()
plt.show()
```

5. Expected Outcomes

1. Visualizations showing historical oil prices and key features.
2. A trained regression model capable of predicting oil prices.
3. Insights into factors influencing oil prices.

6. Additional Suggestions

- Experiment with advanced regression models (e.g., XGBoost, Gradient Boosting).
- Include macroeconomic indicators (e.g., GDP, inflation) as additional features.
- Develop a dashboard for real-time oil price monitoring and forecasting.