Course description

Petrol price prediction using Machine Learning involves training a model to predict the future prices of petrol based on historical data and other relevant variables such as global crude oil prices, supply and demand, economic indicators, and geopolitical events. The model can be trained using various algorithms such as Linear Regression, Support Vector Machines, Random Forest, or Deep Learning techniques like Neural Networks. Once trained, the model can be used to make future predictions about petrol prices, which can be used by businesses and consumers for planning and decision-making. Accurate predictions of petrol prices can help businesses optimize their operations, while consumers can use this information to plan their travel and transportation expenses.

What will i learn?

• By training machine learning models on large datasets of historical petrol prices and relevant variables such as crude oil prices, exchange rates, and supply and demand factors
• Machine learning models can be used to make real-time predictions of petrol prices based on current data, such as weather conditions or political events that may affect petrol prices.
• Machine learning models can be used to identify trends in petrol prices over time, such as seasonal variations or long-term trends related to global energy markets.
• Machine learning models can be used to analyze individual consumer behavior and make personalized recommendations for when and where to purchase petrol based on factors such as past buying habits, location, and price sensitivity.