1. Abstract

Petrol price prediction has become an important task due to the frequent fluctuations in fuel prices. These fluctuations occur because of various factors such as crude oil prices, government policies, economic conditions, and global demand. Predicting petrol prices in advance can help governments, businesses, and consumers make better financial decisions.

This project focuses on forecasting future petrol prices using machine learning and deep learning techniques. The dataset contains historical petrol price records, which are analyzed to understand patterns and trends over time.

Time-series forecasting models such as ARIMA (Autoregressive Integrated Moving Average) and LSTM (Long Short-Term Memory) are used in this project. ARIMA is a statistical model used for analyzing and forecasting time-series data, while LSTM is a deep learning model that can capture long-term dependencies in sequential data.

The project also explores Automated Machine Learning (AutoML) using the AutoKeras library, which helps automate the model training and optimization process. By comparing different models, the system predicts petrol prices for upcoming dates.

This project demonstrates how machine learning and deep learning techniques can be applied to real-world forecasting problems and helps in understanding fuel price trends.

2. Objectives

The main objectives of this project are:

1. To understand the concept of petrol price forecasting.
2. To analyze historical petrol price data and identify trends.
3. To perform data preprocessing and time-series data preparation.
4. To implement machine learning models for price prediction.
5. To use deep learning models such as LSTM for forecasting future petrol prices.
6. To apply ARIMA models for time-series analysis and prediction.
7. To explore Automated Machine Learning (AutoML) using AutoKeras.
8. To evaluate model performance and generate future petrol price predictions.

3. Existing System

In traditional systems, petrol price prediction is often performed using basic statistical methods or manual analysis.

These systems have several limitations:

1. Manual analysis is time-consuming and less accurate.
2. Traditional models may fail to capture complex patterns in price fluctuations.
3. Large historical datasets are difficult to analyze manually.
4. Sudden market changes cannot be predicted effectively.
5. Traditional forecasting techniques may not handle large datasets efficiently.

Due to these limitations, advanced machine learning and deep learning models are required.

4. Proposed System

The proposed system uses machine learning and deep learning techniques to forecast future petrol prices.

In this system:

1. Historical petrol price data is collected and analyzed.
2. Data preprocessing is performed to prepare the dataset for training.
3. Time-series forecasting models such as ARIMA and LSTM are implemented.
4. AutoKeras, an automated machine learning library, is used to automate model development.
5. The models are trained and evaluated to determine the most accurate forecasting method.
6. The trained model predicts petrol prices for future dates.

This system provides an efficient way to analyze price trends and forecast future petrol prices using intelligent algorithms.

5. Implementation Procedure

The implementation of this project is carried out in the following steps:

Step 1: Data Collection

1. Collect historical petrol price data from reliable sources.
2. Load the dataset into the working environment.

Step 2: Data Analysis

1. Analyze petrol price trends over time.
2. Understand the distribution and structure of the dataset.

Step 3: Data Preprocessing

1. Convert date values into time-series format.
2. Normalize the dataset and prepare it for machine learning models.

Step 4: Data Visualization

1. Visualize petrol price trends using graphs and charts.
2. Identify patterns and fluctuations in the data.

Step 5: Model Building

1. Implement forecasting models such as:
2. ARIMA (Statistical Time-Series Model)
3. LSTM (Deep Learning Model)

Step 6: Automated Machine Learning

1. Use AutoKeras to automate the model training process.
2. Generate optimized models automatically.

Step 7: Model Evaluation

1. Evaluate models using performance metrics such as Mean Squared Error (MSE) and accuracy.

Step 8: Price Forecasting

1. Use the trained model to predict future petrol prices.

6. Software Requirements

The software used in this project includes:

Operating System:

1. Windows / Linux / macOS
2. Programming Language:
3. Python 3.x

Development Environment:

1. Jupyter Notebook / Google Colab / VS Code

Libraries and Frameworks:

1. TensorFlow
2. Keras
3. NumPy
4. Pandas
5. Matplotlib
6. Scikit-learn
7. AutoKeras

Dataset Source:

1. Public petrol price datasets / online data sources

Web Browser:

1. Chrome / Firefox

7. Hardware Requirements

The hardware required for this project includes:

1. Processor: Intel i3 / i5 or higher
2. RAM: Minimum 4 GB (8 GB recommended)
3. Storage: Minimum 128 GB free space
4. System: Laptop / Desktop Compute
5. Internet Connection

8. Advantages of the Project

1. Helps forecast future petrol prices accurately.
2. Assists governments and businesses in financial planning.
3. Uses advanced machine learning and deep learning models.
4. Automates the model building process using AutoML.
5. Improves prediction accuracy using historical data patterns.
6. Handles large datasets efficiently.
7. Can be extended to predict other economic indicators.
8. Useful for economic analysis and market prediction.