• Abhishek Jadhav
• Nitesh Pandit
• Pratik Thorat
• Pratik Sonawane
• Prof. Swati Gawand

Raw material procurement planning is crucial for automobile manufacturers to stay competitive in the global market. Forecasting demand for raw materials is very helpful for production planning. We implemented machine learning algorithms to forecast demand for raw material quantities needed in the future using past data. Machine learning algorithms can provide better accuracy than traditional forecasting techniques and can be flexible to changes. In this paper we implemented linear regression, Multiple output linear regression, random forest and k-nearest neighbor using the scikit-learn package to train ML models and evaluated their results using techniques such as RMSE, Rsquare.

Machine Learning, Automobile Industry, Raw Materials, Demand Forecasting, Linear Regression

"RAW MATERIAL DEMAND FORECASTING IN AUTOMOBILE INDUSTRY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.c257-c261, May-2022, Available :http://www.jetir.org/papers/JETIR2205335.pdf

"RAW MATERIAL DEMAND FORECASTING IN AUTOMOBILE INDUSTRY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppc257-c261, May-2022, Available at : http://www.jetir.org/papers/JETIR2205335.pdf